Technology – The Power of a Data Catalog

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A data catalog can be an excellent resource for businesses, researchers, and academics. A data catalog is a central repository for curated data sets. This collection of information helps you make the most of your information. It also makes your content more accessible to users. Many businesses use data catalogs to create a more personalized shopping experience. They also make it easier to find products based on their preferences. Creating a data catalog is an easy way to get started.

A data catalog is an essential step for any fundamentally data-driven organization. The right tool can make it easier to use the data within the organization, ensuring its consistency, accuracy, and reliability. A good data catalog can be updated automatically and allow humans to collaborate with each other. It can also simplify governance processes and trace the lifecycle of your company’s most valuable assets. This can also save you money. A properly implemented data catalog can lead to a 1,000% ROI increase.

A data catalog allows users to make better business decisions. The data in the catalog is accessible to everyone, which helps them make better decisions. It also enables teams to access data independently and easily, reducing the need for IT resources to consume data. Additionally, a data catalog can improve data quality and reduce risks. It is important to understand the power of a digital data catalog and how it can benefit your company. It can help you stay on top of your competition and increase your revenue.

A data catalog is essential for generating accurate business decisions. With a robust data catalog, you can create a digital data warehouse that connects people and data. It also provides fast answers to business questions. The benefits of using a data catalog are enormous. For example, 84% of respondents said that data is essential for accurate business decisions. However, they reported that without a database, organizations are struggling to achieve the goal of being data-driven. It has been estimated that 76% of business analysts spend at least seventy percent of their time looking for and interpreting the information. This can hinder innovation and analysis.

A data catalog is an invaluable resource to companies that use it to organize and analyze their data. It helps them discover which data assets are most relevant for their business and identify which ones need more attention. Furthermore, a data catalog can be used to identify the best data assets within an organization. This is a powerful way to leverage your data. This is not just about finding and analyzing the information; it can also help you improve your company’s productivity and boost innovation.

Creating a data catalog is essential for a data-driven organization. It makes it possible to ingest multiple types of data. Besides providing a centralized location for storing and presenting data, a good data catalog can also provide metadata that is meaningful to the user. This can help them create more meaningful analytics and make their data more valuable. It can even help prevent the spread of harmful and inaccurate information.

When creating a data catalog, it is important to define the types of data you have and their purpose. A data catalog is an essential tool for data-driven enterprises. A catalog is a repository for structured data and can be customized to accommodate the needs of your business. In addition to describing the type of datasets, it can also provide access to metadata that makes the information even more useful. The best data catalogs include the ability to add and edit business and technical metadata.

A data catalog should allow users to add metadata for free. A good data catalog should allow people to search for specific terms. Moreover, it should provide the ability to add and tag metadata about reports, APIs, servers, and more. The data catalog should also support custom attributes like department, business owner, technical steward, and certified dataset. This is crucial for the data-driven enterprise. A good data catalog should provide a comprehensive view of all data across an organization.

Denodo Platform 8.0 – Demo Overview

Technology – Should I Put My Whole Work History on LinkedIn?

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You might be wondering if it’s a good idea to put your entire work history on LinkedIn. Your resume is the first thing that employers will see. It’s important to keep it relevant and to keep your experience to a minimum. However, if you’ve been working for a number of companies, it’s important to make sure that you highlight your most recent employment. Treat LinkedIn like a resume, which means that you should provide the past 10 to 15 years with the most recent five to 10 years being the most important.

Generally, the experience section on your LinkedIn profile should be relevant to your resume. Include only roles that are relevant to your current job search, and do not include roles that were held before ten years. Remember to include dates for each role. If you have twenty or thirty years of experience, do not put it on your profile. Instead, focus on the last five to ten years. You can add dates for your previous roles, but you shouldn’t put your entire work history on your profile.

Your LinkedIn profile is an important way for companies to see what you can offer. If you have multiple jobs, you might be wondering whether to include your entire work history. You should only include the most recent positions. If you’ve had several jobs, you should include them all. Your experience section is the most important part of your profile because it’s what employers will use to determine your qualifications for the job. You can use Laszlo Bock’s formula to describe your achievements, which can be useful for your professional development.

The experience section of your LinkedIn profile should support your resume. When listing your work history, make sure to include the roles you’ve had for the past ten or so years. Write a compelling story that shows your successes and adds credibility to your professional journey. Here are a few suggestions to help you create a comprehensive and achievement-based experience section on LinkedIn. Your profile will be much more impressive if it’s complete and includes your work experience.

Unlike a resume, your LinkedIn experience section should support your resume. It should be a well-written summary of your achievements. Your job description should be more than a list of bullet points. It should be a narrative, instead of a list. Your headline should highlight your main objective. You may also want to highlight your most recent experiences. In the case of a company or recruiter, the company’s website should be able to see your profile.

Besides the information you provide on your resume, your LinkedIn profile should also include the roles you’ve held. It’s best to include your latest positions in this section, but avoid including the ones you’ve held for more than a decade. If you’re in a position where you’re looking for a new position, you should focus your LinkedIn profile on your job experience. The company will look for your qualifications and hire you for the job.

Your LinkedIn profile should contain your most recent work history. Putting your entire work history on your profile will make it less relevant to recruiters. It is best to include your most recent job positions and highlights your achievements. When you’re building your professional profile, try to include your most relevant roles. A job title should be prominent and highlighting your achievements should be the main focus. You should also mention your GitHub profile.

Your LinkedIn profile should include your experience. It should highlight the roles you’ve held in the last ten to fifteen years. You should also include your achievements. When you’re describing your experience, try to include as much information as you can. Incorporate your personal information. For example, you can include your hobbies. It’s best to avoid listing personal details and leave them out of your profile.

If you’re writing your experience on LinkedIn, focus on the most recent positions. If you’ve held several different positions in the past, highlight the most recent ones. You can also mention your school projects, GitHub profile, and other achievements. You can also include your achievements and skills. Just remember that your experience section on LinkedIn should be short and simple. It should contain only the most relevant roles. This way, your profile will be more attractive to recruiters.

#linkedintips,  #linkedinexperiencesection
LinkedIn Tips: How far back should my experience go

Technology – How to Search Google by Date

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One of the most common questions that people ask is how to filter Google results by date. While older information may be more reliable, you may want to check out the most current results. After all, a few years is a long time to wait for the latest results on a particular topic. You might also be searching for the most recent information on a specific topic or issue. That is why it is helpful to be able to narrow down your results by date.

You can also filter your search results by date. Depending on what you are looking for, you can select the year, month, week, day, hour, or custom dates. For example, if you are trying to find a movie that was released a year ago, you may only want to see results that are from last year. You can even use custom dates to filter your search. It’s all up to you and how you want to use it.

You can also filter Google results by date by using the new before and after commands in the search bar. These commands can help you filter results by date, so that you can get an updated picture of rankings based on the exact day you search. You can also use these commands to find the most recent results. However, make sure that you use the YYYY-MM-DD command before you start typing your query. You can combine this option with Restrict to Range or Filter by Attribute to get the best results.

Once you have set the date range for your search, you can click on the ‘advanced search’ option on the search results page. This option is located on the advanced search page. You can select the date range, time frame, or both to filter your results by date. You can also filter Google results by date by using the dates ‘before’ and ‘after’ commands together. You can also select the ‘after’ command to filter by a specific date or period.

Depending on your needs, you might want to filter your Google results by date. There are two methods for this. Firstly, you can use the ‘advanced search’ command to narrow down your results by period. By using the ‘advanced search mode’ command, you can filter results by date. Once you have chosen a date, you can then use the ‘advanced’ command to limit the results by time.

In addition to the date, you can also filter Google search results by the most recent year, month, week, day, and hour. You can also filter your search by date by adding a time range to your query. If you are looking for a specific product, you can select a specific time period to narrow down the results by date. If you need to limit your search by date, you can use the ‘date’ keyword.

Secondly, you can also filter your search by date. While it may seem cumbersome, this method is quite convenient for users who need to search for certain products or services on a particular day. Simply enter the desired date in the text box and hit enter. Once you have entered the term, click on the ‘date’ option and a drop-down list will appear. Now, the dates will be shown according to the date you entered.

If you want to filter Google results by date, you can select the date range and time range. You can also use the ‘date range’ field in the ‘date’ field. If you choose the date option, you can specify the timestamp of the search term to narrow down the results. Then, you can specify the time period in the ‘date’ field. You can also specify the time and location of the search term.

By using the ‘date’ option, you can filter the Google search results by the most recent year, month, week, day, or hour. You can also choose a custom date range. Then, you can set a filter based on the time range. If you want to search for a specific time period, select the ‘month’ setting. If you want to filter by date, you can select ‘week’ or ‘year.

How to SORT and FILTER Google Search Results by DATE

Technology – Some Pinterest Social Media Alternatives

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Some Pinterest social media alternatives may be more fun than the original. While Pinterest is a popular information discovery tool, the site isn’t the only platform that uses images, GIFs, and videos. In addition to the pinboards that Pinterest provides, there are other sites that are just as fun. The following are a few alternatives to the site you’re currently using. Just be sure to check out all of them out to find the one that best fits your needs.

PearlTrees is a Pinterest social media alternative that is similar but not exactly the same. It follows a similar concept, but instead of boards, users follow different types of trees, allowing them to search for similar content and save items with pearls. The interface and user experience are similar to that of Pinterest, so if you’re looking for a simple, fun alternative, try it out! This is a good place to start if you like Pinterest but don’t know much about it.

FoodGawker is another alternative to Pinterest, which is geared towards food lovers. While its concept is similar to that of Pinterest, Pearltrees uses a slightly different method. You can bookmark content and share it with others. This website uses the terms “trees” and “pearls” to refer to content, which allows you to follow your favorite trees and favorites. The main feature of this site is that you can search for recipes and ideas through keywords.

Aside from being a great alternative to Pinterest, there are many other reasons to switch to a different platform. The site is based on what people share, so it can sometimes be hard to find the type of content you’re looking for. If you’re looking for content to share, you might want to consider Juxtapost or Mix. Both of these sites have many benefits and are worth checking out. There’s also a popular app called Juxtapost.

Aside from food lovers, Pinterest isn’t for everyone. Moreover, monetization is not available in some countries, and user-generated content is uneven. As a result, using the appropriate words is important. In addition, Pinterest isn’t very user-friendly for beginners. You might want to invest in a few apps that work with your smartphone or tablet. If you can’t decide on any of these options, check out some of the other social media sites that are similar.

Another popular alternative to Pinterest is FoodGawker. This site is devoted to food lovers, and offers recipes, and other related content. While the concept of both sites is similar, each site has its own advantages and disadvantages. However, many users find Pinterest to be the most appealing social media platform for their interests. In addition to this, some people find FoodGawker to be the best alternative to Pinterest in terms of food-related content.

Some Pinterest social media alternatives are not suitable for everyone. While MANteresting is dedicated to food lovers, DartItUp is geared toward college-minded sports fans. Other alternatives include Pearltrees, which is similar to Pinterest but has a different concept. Its members can bookmark and share content. Its concept is centered around the concepts of trees and pearls. The user can even follow their favorite tree or pearl to stay updated on its content.

Although Pinterest is the best-known social networking site, it doesn’t offer instant gratification. It requires a lot of time and effort to understand, and ads are expensive. Additionally, Pinterest isn’t ideal for beginners. For those who want to use the site, it’s best to learn how to create a profile and use the platform’s search engine. The website has been updated continuously, so it’s not always easy to find what you’re looking for.

Besides being a great social networking site, there are many alternatives to Pinterest. Some of them are better for certain purposes. The main reason why Pinterest is so popular is that it has a limited number of categories and restrictions. The website is best for people who enjoy art and design. If you’re interested in a particular niche, you can choose one of these sites. The site has a huge database of artists and designers who can share and sell their works.

Best Alternatives to Pinterest | Pinterest Alternatives

Technology – What Is An Iterative Approach In Software Development?

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What is an iterative development approach? This software development method combines an iterative design process and an incremental build model. It can be applied to any type of software project. Iterative development approaches are also known as agile development. These methodologies are generally used for smaller projects. In many cases, a team of developers can produce a complete version of the product within a year. This approach is ideal for small and medium-sized organizations.

The iterative software development model allows rapid adaptation to changes in user needs. It enables the rapid change of code structure and implementations with minimum cost and time. If a change is not beneficial, the previous iteration can be rolled back. Iterative development is a proven technique that is gaining momentum in software development. This approach has several advantages. It is flexible and adaptable, allowing companies to rapidly respond to changing client needs.

Iterative development allows for rapid adaptation to changing requirements. This approach is especially useful for small companies, as it can make fundamental changes to the architecture and implementation without incurring too much cost or time. The team can also roll back to the previous iteration if the change is too detrimental. In addition, the process ensures that the customer will have the product that they want. The customer will be satisfied with the end product with the iterative approach.

When developing a large software, you must develop an efficient, high-quality product. This is important if your product is large and requires significant change to achieve success. With an iterative approach, you can make incremental changes in the development process without having to rewrite the entire software. As a result, iterative development ensures that you deliver the best quality and most efficient solution possible.

With an iterative development approach, the team can make changes to the software rapidly, allowing it to evolve as the business needs change. With iterative development, iterative improvements are more likely to be made, and the system will be more effective in the long run. The process can also be more cost-effective if you deliver a complex and complicated product. The best part about this approach is that it is incredibly easy to learn.

One of the main advantages of an iterative development approach is that it provides rapid adaptation to changing needs. Iterative development allows you to make changes in the code structure or implementation. You can make fundamental changes without incurring high costs or affecting the original design. You can also change the design of the application as you go along. In this way, you can be certain that the product will be able to meet the market needs of your customers.

There are several disadvantages to iterative development. It may require more intensive project management. The system architecture might not be well-defined and may become a constraint. Finding highly skilled people for risk analysis and software design is also time-consuming. However, in the case of a game app, an iterative approach will give you a complete and workable product to test out in the real world.

Using an iterative development approach will allow you to make fundamental changes to your software in a short amount of time. Iterative development will allow you to make changes to your software architecture and the overall design of the product. This is why this process is so popular with game developers and is often recommended by other organizations. Iterative development will improve the quality of your game, while a traditional one will delay the release date.

The iterative development approach is the most effective way of software development. It allows you to make fundamental changes quickly, with a minimal impact on the quality of the finished product. During this process, iterative development will result in a more useful and less costly deliverable. In many cases, iterative development will lead to a better product than a waterfall-style approach.

Iterative and Incremental Software Development Process

Technology – Alternative Browsers For Chrome

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Many of the more popular browsers, namely Microsoft’s Internet Explorer and Mozilla Firefox, are not considered “open source” browsers. This is because they are not developed by or developed for the community. Their code is not released under an Open Source license but instead is released under a Commercial License. These licenses can be a bit restrictive, especially in terms of the license requirements. In this article, I will explain what Commercial Licenses are and how they affect non-Microsoft browsers.

A Commercial License is a type of royalty that allows the manufacturer to charge a fee for use in the developer’s program. While this is the most common licensing arrangement for web browsers, not all of them employ this mechanism. The most common example is Sun’s OpenOffice suite, designed as an open-source project but heavily commercialized. This is similar to Microsoft’s Office Suite, which is also based on an Open Source project. Microsoft’s ActiveX and Adobe Flash are also based on Commercial License programs.

There are two main limitations of Commercial Licenses when it comes to non-Microsoft browsers. First, they can be expensive. Microsoft has designed its own engine from scratch and has no competitors to support it. Due to its proprietary nature, this engine cannot be shared with any other browser and must always be included with Microsoft’s Internet Explorer. In short, if you want a non-Microsoft browser, you’re going to have to spend more money – though it is worth it.

Second, many of the Commercial Licenses include clauses that limit the browser’s distribution to specific parties. These are generally the carriers and manufacturers of Microsoft’s products and restrict browser distribution. Some clauses are so limiting that many organizations, such as universities and schools, choose to implement their own browsers instead of Microsoft. This is not recommended. The Internet is an open platform, and everyone is free to implement any technology they deem appropriate.

The WebKit-based Browser from Apple is one example. Apple’s Safari is based on the same codebase as WebKit and is not a fork of WebKit. Neither is it an alternative and in fact, it is not even really a browser at all. The primary difference is that Safari uses WebKit for most elements, such as web navigation. It also includes a new WebKit-based key-board layout much like what you’d see on the Mac OS X platform.

Open Source-based browsers, such as Mozilla Firefox, are not based on any license agreement but instead are derivatives of the Mozilla codebase. This means that the code is available for anyone to change and customize, while the licensing terms are much more permissive. Although this type of browser doesn’t come pre-installed with Microsoft, it can still be used with Microsoft applications if you buy a license for it. However, it has its drawbacks, such as lacking many customization options available with commercial non-Microsoft browsers.

Opera is also a popular browser and is similar to Safari in many ways. It is a fork of the Linux operating system. While the commercial version has many advantages, such as the ability to use most of the Microsoft Office software pre-installed, Opera is often seen as lacking some of the features available with Microsoft. For instance, it lacks the password manager and some of the other Microsoft-related tools. However, the software does have an excellent user interface and is the preferred browsing application for many developers and designers.

Finally, there are third-party browsers available for Chrome. These browsers are less expensive than Microsoft-based browsers and have many of the same features available with Microsoft browsers.  Some of the Opera features, like the password manager, can also be found in a third-party browser. This gives users of all operating systems more freedom to choose which browser they want to use for their surfing needs.

Technology – Alternative Browsers For Chrome

Technology – Denodo ODBC And JDBC Driver Virtual DataPort (VDP) Engine Compatibility?

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Recently, while patching a Denodo environment, the question arose as to whether an older ODBC or JDBC driver can be used against a newer patched environment. It is described in the first paragraph of the denodo documentation, the directionality of the compatibility can be overlooked easily.

Can An Older ODBC Or JDBC Driver Be Used Against A Newer Past Environment?

The short answer is yes.  Denodo permits backward compatibility of older drivers with newer versions. Even across major versions for denodo version 7 and 8.

ODBC and JDBC driver Compatibility

The older ODBC and JDBC drivers can be of an update that is an older version (patch or major version) than the update installed on the server.

However, as is clearly stated in the documentation, you cannot use a newer driver against an older version of Denodo. This goes for denodo patch versions as well as denodo major versions. Connecting a Virtual DataPort server using an updated newer ODBC or JDBC on the Virtual DataPort (VDP) Engine server. This will not be supported, and it may lead to unexpected errors.

Related Denodo References

For more information about ODBC and JDBC drivers compatibility, please see these links to denodo

Documentation.

Denodo > Drivers > JDBC

Denodo > Drivers > ODBC

Backward Compatibility Between the Virtual DataPort Server and Its Clients

Technology – An Introduction to SQL Server Express

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If you use SQL, several options are open to you, from the Enterprise editions down to SQL Server Express, a free version of Microsoft’s main RDBMS (Relational Database Management System), SQL Server. SQL Server is used to store information and access other information from multiple other databases. Server Express Edition is packed with features, such as reporting tools, business intelligence, advanced analytics, and so on.

SQL Server Express 2019 is the basic version of SQL Server, a database engine that can be deployed to a server, or you can embed it into an application. It is free and ideal for building desktops and small server applications driven by data. It is ideal for independent software developers, vendors, and those building smaller client apps.

The Benefits

SQL Server Express offers plenty of benefits, including:

  • Automated Patching – allows you to schedule windows to install important updates, to SQL Server and Windows automatically
  • Automated Backup – take regular backups of your database
  • Connectivity Restrictions – when you install Express on an Image Gallery-created Server VM installation, there are three options to restrict connectivity – Local (in the VM), Private (in a Virtual Network), and Public (via the Internet)
  • Server-Side Encryption/Disk Encryption – Server-side encryption is encryption-at-rest, and disk encryption encrypts data disks and the OS using Azure Key Vault
  • RBAC Built-In Roles – Role-Based Access Control roles work with your own custom rules and can be used to control Azure resource access.

The Limitations

However, SQL Express also has its limitations:

  • The database engine can only use a maximum of 1 GB of memory
  • The database size is limited to 10 GB
  • A maximum of 1 MB buffer cache
  • The CPU is limited to four cores or one socket, whichever is the least. However, there are no limits to SQL connections.

Getting Around the Limitations

Although your maximum database size is limited to 10 GB (Log Files are not included in this), you are not limited to how many databases you can have in an instance. In that way, a developer could get around that limit by having several interconnected databases. However, you are still limited to 1 GB of memory, so using the benefit of having several databases to get around the limitation could be wiped out by slow-running applications.

You could have up to 50 instances on a server, though, and each one has a limit of 1 GB memory, but the application’s development cost could end up being far more than purchasing a standard SQL license.

So, in a nutshell, while there are ways around the limits, they don’t always pay off.

SQL Server Express Versions

SQL Server Express comes in several versions:

  • SQL Server Express With Tools – this version has the SQL Server Database, and all the tools need for managing SQL instances, such as SQL Azure, LocalDB, and SQL Server Express
  • SQL Server Management Studio – this version contains the tools needed for managing SQL Server Instances, such as SQL Azure, SQL Express, and Local DB, but it doesn’t have SQL Server
  • SQL Server Express LocalDB –  if you need SQL Server Express embedded into an application, this version is the one for you. It is a lite Express version with all the Express features, but it runs in User Mode and installs fast with zero-configuration
  • SQL Server Express With Advanced Series – this version offers the full SQL Server Express experience. It offers the database engine, the management tools, Full-Text Search, Reporting Services, Express tools, and everything else that SQL Server Express has.

What SQL Server Express 2019 is Used For and Who Uses it

Typically, SQL Server Express is used for development purposes and to build small-scale applications. It suits the development of mobile web and desktop applications and, while there are some limitations, it offers the same databases as the paid versions, and it has many of the same features.

MSDE was the first SQL Server Data Engine from Microsoft, which was called Microsoft Desktop Engine. SQL Server Express grew when Microsoft wanted to build a Microsoft Access alternative to provide software vendors and developers with a path to the premium versions of SQL Server Enterprise and Standard.  

It is typically used to develop small business applications – web apps, desktop apps, or mobile apps. It doesn’t have all the features the premium versions have. Still, most small businesses don’t have the luxury of using a DBA (SQL Server database administrator), and they often don’t have access to developers who use DBAs either.

Lots of independent developers embed Server Express into the software, given that distribution is free. Microsoft has even gone down the road of creating SQL Server Express LocalDB. This lite version offers independent software vendors and developers an easier way of running the Server in-process in the applications and not separately. SQL Server Express is also considered a great starting point for those looking to learn about SQL Server.

Downloading SQL Server Express Edition 2019

SQL Server Express Edition 2019 is pretty easy to download, and you get it from the official Microsoft Website.

Once you have downloaded it onto your computer, follow the steps below to install it and set it up:

Step One

  • Right-click on the installation file, SQL2019-SSEI-Expr.exe.
  • Click on Open to get the installation process started – ensure that the user who is logged on has the rights needed to install software on the system. If not, there will be issues during the installation and setup.

Step Two

  • Now you need to choose which type of installation you need. There are three:
  • Basic – installs the database engine using the default configuration setup
  • Custom – this takes you through the installation wizard and lets you decide which parts to install. This is a detailed installation and takes longer than the basic installation
  • Download Media – this option allows you to download the Server files and install them when you want on whatever computer you want.
  • Choose the Custom installation – while the Basic is the easiest one, takes less time, and you don’t need to worry about the configuration as it is all done for you, the custom version allows you to configure everything how you want it.

Step Three

  • Now you have a choice of three package installation types:
  • Express Core – at 248 MB, this only installs the SQL Server Engine
  • Express Advanced – at 789 MB, this installs the SQL Server Engine, Full-Text Service, and the Reporting Services features
  • LocalDB – at 53 MB, this is the smallest package and is a lite version of the full Express Edition, offering all the features but running in user mode.

Step Four

  • Click on Download and choose the path to install Server Express to – C:\SQL2019
  • Click on Install and leave Server Express to install – you will see a time indicator on your screen, and how long it takes will depend on your system and internet speed.

Step Five

  • Once the installation is complete, you will see the SQL Server Installation Center screen. This screen offers a few choices:
  • New SQL Server Stand-Alone Installation or Add Features to Existing Installation
  • Install SQL Server Reporting Services
  • Install SQL Server Management Tools
  • Install SQL Server Data Tools
  • Upgrade From a Previous Version of SQL Server
  • We will choose the first option – click on it and accept the License Terms

Step Six

  • Click on Next, and you will see the Global Rules Screen, where the setup is checked against your system configuration
  • Click on Next, and the Product Updates screen appears. This screen looks for updates to the setup. Also, if you have no internet connection, you can disable the option to Include SQL Server Product Updates
  • Click on Next, and the Install Rules screen appears. This screen will check for any issues that might have happened during the installation. Click on Next

Step Seven

  • Click on Next, and the Feature Selection screen appears
  • Here, we choose which features are to be installed. As you will see, all options are enabled, so disable these:
  • Machine Learning Services and Language Extensions
  • Full-Text and Semantic Extractions for Search
  • PolyBase Query Service for External Data
  • LocalDB
  • Near the bottom of the page, you will see the Instance Root Directory option. Set the path as C:\Program Files\Microsoft SQL Server\

Step Eight

  • Click Next, and you will see the Server Configuration screen
  • Here, we will set the Server Database Engine startup type – in this case, leave the default options as they are
  • Click on the Collation tab to customize the SQL Server collation option
  • Click Database Engine Configuration to specify the Server authentication mode – there are two options:
  • Windows Authentication Mode – Windows will control the SQL logins – this is the best practice mode
  • Mixed Mode – Windows and SQL Server authentication can access the SQL Server.
  • Click on Mixed Mode, and the SQL Server login password can be set, along with a Windows login. Click on the Add Current User button to add the current user

Step Nine

  • Click on the Data Directories tab and set the following;
  • Data Root Directory – C:\Program Files\Microsoft SQL Server\
  • User Database Directory – C:\Program fees\Microsoft SQL Server\MSSQL.15.SQLEXPRESS\MSSQL\Data
  • User Database Log Directory – C:\Program fees\Microsoft SQL Server\MSSQL.15.SQLEXPRESS\MSSQL\Data
  • Backup Directory – C:\Program fees\Microsoft SQL Server\MSSQL.15.SQLEXPRESS\MSSQL\Backup

Step Ten

  • Click the TempDB tab and set the size and number of tempdb files – keep the default settings and click Next
  • Now you will see the Installation Progress screen where you can monitor the installation
  • When done, you will see the Complete Screen, telling you the installation was successful.

Frequently Asked Questions

Microsoft SQL Server Express Edition  2019 is popular, and the following frequently asked questions and answers will tell you everything else you need to know about it.

Can More than One Person Use Applications That Utilize SQL Server Express?

If the application is a desktop application, it can connect to all Express databases stored on other computers. However, you should remember that all applications are different, and not all are designed to be used by multiple people. Those designed for single-person use will not offer any options for changing the database location.

Where it is possible to share the database, the SQL Server Express Database must be stored in a secure, robust location, always be backed up, and available whenever needed. At one time, that location would have been a physical server located on the business premises but, these days, more and more businesses are opting for cloud-based storage options.

Can I Use SQL Server Express in Production Environments?

Yes, you can. In fact, some of the more popular CRM or accounting applications include Server Express. Some would tell you not to use it in a production environment, mostly because of the risks of surpassing your 10 GB data limit. However, provided you monitor this limit carefully, SWL Server Express Edition can easily be used in production environments.

Is SQL Server Express Edition Scalable?

There is a good reason why Microsoft allows you to download SQL Server Express Edition for free. It’s because, if it proves too small for your needs, at some point, you can upgrade to the premium SQL Server Standard version. While the Express Edition is limited and you are likely to outgrow it at some point, transferring your database over to the Standard version when the time comes is easy. Really, the Express version is just a scaled-down version of Standard. Any development you do on it is fully compatible with any other Edition of SQL Server and can easily be deployed.

Can I Use SQL Server Express in the Cloud?

Cloud computing is being adopted by more and more businesses and their applications. These days, many are now built in the cloud as web or mobile apps. However, when it comes to desktop applications, it is a slightly different story, as these need to be near the SQL Server Express Database to work properly. Suppose you host the database in the cloud but leave the application on the desktop. In that case, you are likely to experience poor performance, and you may even find your databases becoming corrupted.

You can get around this issue by running your application in the cloud, too, and this is easy using a hosted desktop (a hosted remote desktop service), which used to be known as a terminal service. In this case, the database and application reside on servers in the data center provided by the host and are remotely controlled by the users. As far as the user is concerned, it won’t look or feel any different from running on their own computer.

What Do I Get With SQL Server Express?

The premium SQL Server editions contain many features that you can also find in the free SQL Server Express Edition. Aside from the database engine, you also get:

Plus, the Express licensing allows you to bundles SQL Server Express with third-party applications.

What Isn’t Included?

There are a few things you don’t get in the Express edition compared to SQL Server Standard. For a start, Express edition has limits not found in the premium editions:

  • Each relational database can be no larger than 10 GB, but log files are not included as there are no limits on these
  • The database engine is limited to just 1 GB of memory
  • The database engine is also restricted to one CPU socket or four CPU cores, whichever is the lower of the two.
  • All the SQL Server Express Edition components must be installed on a single server
  • SQL Server Agent is not included – admins use this for automating tasks such as database replication, backups, monitoring, scheduling, and permissions.
  • Availability Groups
  • Backup Compression
  • Database Mirrors limited to Witness Only
  • Encrypted Backup
  • Failover Clusters
  • Fast recovery
  • Hot add memory and CPU
  • Hybrid Backup to Windows Azure
  • Log Shipping
  • Mirrored backups
  • Online Index create and rebuild
  • Online Page and file restore
  • Online schema change
  • Resumable online index rebuilds

Where Do I Find the SQL Server Express Edition Documentation?

You can find the relevant documentation at https://docs.microsoft.com/en-us/sql/?view=sql-server-ver15 and are urged to make good use of it. Refer to the documentation whenever you don’t understand something or want to learn how to do something new.

Microsoft SQL Server Express Edition 2019 is worth considering for small businesses, as it gives you a good starting point. As your business grows, you can upgrade to the premium versions without having to worry about learning a new system – you already know the basics, and your databases will transfer seamlessly over.

Related References

Erkec, Esat. 2020. “How to Install SQL Server Express Edition.” SQL Shack – Articles about Database Auditing, Server Performance, Data Recovery, and More. January 16, 2020.

shirgoldbird. n.d. “Microsoft SQL Documentation – SQL Server.” Docs.microsoft.com.

“What Is SQL Server Express and Why Would You Use It.” 2020. Neovera. March 27, 2020.

“What Is SQL Server Express Used For?” n.d. Your Office Anywhere.

“What Is SQL Server Express? Definition, Benefits, and Limitations of SQL Server Express.” 2017. Stackify. April 19, 2017.

Technology – An Introduction to SQL Server Express

Technology – 5 Best Free Online Flowchart Makers

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Did you know that you can create stunning flowcharts anywhere and at any time without spending a lot with the best flowchart makers? Flowcharts are handy as they streamline your work and life. Even though flowcharts makers are available on Windows and other platforms, one can create a flowchart on Excel or even make it on Microsoft Word. However, web-based solutions are better because all you need is a browser – everything else is done for you. This guide covers some of the best free online flowchart makers you will come across:

1. Lucidchart

Lucidchart gives the users the ability to create great diagrams. It is pretty reliable with a drag and drop interface which makes everything easy and seamless. The platform contains pre-made templates that you choose from, or you can decide to use a blank canvas. Documents created by this best free online flowchart maker can be saved in various formats such as PNG, JPEG, PDF, Visio, and SVG.

Pros

  • It points out opportunity areas in every process
  • Multi-column flowcharts
  • Copy and paste even across sheets
  • Creative design features and fascinating color selection
  • Easy formatting the notes and the processes

Cons

  • It has a more detailed toolbar
  • No 3D designs
  • Could have some spelling and grammar errors
  • The free version could be quite limited

2. Cacoo

If you require real-time collaboration on your ideal flowchart maker, then cacoo is the one. The maker comes with a fluid and streamlined interface that makes everything seem easy. It has different templates for any project you may handle, such as wireframes, flowcharts, Venn diagrams, and many other valuable charts. For the flowcharts, Cacoo gives you a wide range of shapes to select from – all you do is drag and drop what you need.

Pros

  • Org charts
  • Drag and drop feature for the charts
  • Conceptual visualizations
  • Wireframes for web development
  • Easy to use

Cons

  • The free version may be limited
  • One cannot easily group images
  • Requires more creative options

3. Gliffy

Gliffy is also the best free online flowchart maker one can get in the market. If you are looking for a lightweight and straightforward tool for your flowcharts, gliffy will satisfy your needs. With this platform, one can create a flowchart in seconds with just a few clicks. It comes with basic templates that help you achieve your objective with much ease.

Pros

  • Great for creating easy diagrams, process flows, and wireframes
  • Availability of templates make your life easier
  • Intuitive flash interface

Cons

  • Limitation on the color customization
  • Presence of bugs when using browsers such as Google Chrome
  • One cannot download the diagrams in different formats

4. Draw.io

With this platform, there is no signing up; all you need is storage space. Options available include Dropbox, Google Drive, your local storage, and OneDrive. You can decide to use the available templates or draw a new flowchart. With this platform, you can easily add arrows, shapes, and any other objects to your flowcharts. draw.io supports imports from Gliffy, SVG, JPEG, PNG, VSDX, and Lucidchart. You can also export in different formats like PDF, PNG, HTML XML, SVG, and JPEG.

Pros

  • Produces high-quality diagrams
  • Smart connectors
  • Integrates with storage options like Google Drive
  • Allows collaborative curation of diagrams
  • Users can group shapes

Cons

  • Z-order of shapes are not easy on this platform
  • The app may lag when working with a browser
  • Adding unique graphics and shapes may slow down its speed

5. Wireflow

It is another best free online flowchart maker for app designers and web developers. It is ideal for designing wireframes and user flows. It is very intuitive and comes with a variety of chart designs you can choose from. The platform has the drag and drop feature making everything easy. All you do is drag and drop your shapes, designs, and other items on a fresh canvas to create a stunning flowchart.

It has various connectors to select from. After the flowchart is complete, you can export the file as a JPG. It is a drawback to this platform in that you cannot export in several different formats.

Pros

  • Simple to use
  • User-friendly and intuitive
  • Well-designed graphics
  • Available templates
  • A variety of different chart types

Cons

  • Supports exports only in one format
  • Takes time looking for the templates
  • Limited color range

Final Thoughts

If you are looking for the best free online flowchart makers, you need to consider draw.io, wireflow, gliffy, and cacoo. These platforms will offer you high-quality graphic charts. They will make your work more effortless due to available templates and a wide range of other options to develop accessible and understandable flowcharts.

Links for the Flowchart Makers

Related References

Technology – The Difference Between Float Vs. Double Data Types

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It would be incorrect to say that floating-point numbers should never be used as an SQL data type for arithmetic. I will stick to double-precision floating-point data types for SQL Server that are suitable for my requirements.

The double-precision floating-point data type is ideal for modeling weather systems or displaying trajectories but not for the type of calculations the average organization may use in the database. The biggest difference is in the accuracy when creating the database. You need to analyze the data types and fields to ensure no errors and insert the data values for maximum accuracy. If there is a large deviation, the data will not be processed during the calculation. If you detect incorrect use of the data type with double precision, you can switch to a suitable decimal or number type.

What are the differences between numeric, float, and decimal data types, and should they be used in which situations?

  • Approximate numeric data types do not store the exact values specified for many numbers; they store an extremely close approximation of the value
  • Avoid using float or real columns in WHERE clause search conditions, especially the = and <> operators

For example, suppose the data that the report has received is summarized at the end of the month or end of the year. In that case, the decimal data for calculation becomes integer data and is added to the summary table.

In SQL Server, the data type float _ n corresponds to the ISO standard with a value from n = 1 to 53. The floating-point data is approximated not by the data type’s value but by the range of what can be represented. Both float- and float-related numeric SQL types consist of a significant numeric value and an exponent, a signed integer that indicates the size of the numeric value.

And float-related numeric SQL data types are precise positive integers that define the number of significant digits and exponents of a base number. This type of data representation is called floating-point representation. A float is an approximate number, meaning that not all values can be displayed in the data type range because it is a rounded value.

You can’t blame people for using a data type called Money to store the money supply. In SQL Server, decimal, number, Money, and SmallMoney data types have a decimal place to store values. Precision means the total number of digits after the decimal point.

From a mathematical point of view, there is a natural tendency to use floats. People who use float spend their lives rounding up values and solving problems that shouldn’t exist. As I mentioned earlier, there are places where it makes sense to hover above the real, but these are for scientific calculations, not business calculations.

SmallMoney (2144783647, 4 bytes) We can use this data type for Money- or currency values. The double type can be used as a data type with real values for dealing with Money.

Type Description Memory bits Integer 0 1 null TinyInt allows integers 0 to 255 1 bytes TinyInt allows integers 32767 2 bytes Int allows integers 2147483647 4 bytes BigInt allows integers 9223372036854775807 8 bytes Decimal P is a precisely scaled number. The parameter p specifies the maximum total number of digits stored to the left or right of the decimal point. The data type low and upper range storage observations Real 340E 38 4 Bytes We can use float924 as an ISO synonym for real.

In MariaDB, the number of seconds has elapsed since the beginning of the 1970s (01-01) with a decimal accuracy of 6 digits (0 is the default). The same range of precision is the SQL Server type range (bytes) MariaDB type range size (bytes) Precision notes Date 0001 01-01-99.99 12: 31: 3 They cover the same range: Date 0.001-03-01 9.99912: 31 8: 0: 3 Round DateTime 0.01 0.1-02.9999 12: 31 8 0: 6 In MariaDB the value is near impossible to specify (see below). We can insert a value that requires fewer bits than that assigned to the null-bit pad on the left.

A binary string is a sequence of octets, not a character set, and the associated sorting is described by the binary data type descriptor. Decimal (p) is the exact numerical precision (p scale (n)) of a decimal number that is any number with a decimal point. A Boolean data type consists of different truth values (true, false, and boolean), and it supports unknown truth values, zeroes, and forbidden (not zero) constraints.

This syntax was deprecated in MySQL 8.0.17.7 and will be removed in future versions of MySQL: float (p) A floating-point number. MySQL uses the p-value to specify whether to use a float or a double due to the data type.

Creating data types in PostgreSQL is done with the create-type command. For example, the following commonly used data types are organized into categories with a brief description of the value range and memory size. The native data type is the text data type, the numeric data type, and the date/time Boolean data type.

To understand what floating-point SQL is and what numerical data types are, you need to study computer science a little. Floating-point arithmetic was developed when saving memory was a priority and was used as a versatile method for calculating large numbers. The SQL Prompt Code Analysis Rule (BP023) warns you when using Floating over Real data types. It introduces significant inaccuracies into the type of calculations that many companies do with their SQL Server data.

The difference between a float and a p is that a real float is binary (not decimal) and has an accuracy equal to or greater than the defined value.

The reason for this difference is that the SQL standard specifies a default from 0 to D. Still, the implementation is free to choose a default M. This means that an operation of this type will result in a result different from the result it would produce for MariaDB type if you use enough decimal places. It is important to remember that numerical SQL data types sacrifice precision ranges to approximate the names.

Technology – What Is The Data Fabric Approach?

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What is The data fabric, and how does it automating discovery, creation, and ingestion help organizations? Data-fabric tools, which can be appliances, devices, or software, allow users to quickly, easily, and securely access and manage large amounts of data. Automating the discovery, creation, and ingestion, big data Fabric accelerates real-time insights from operational data silos, reducing IT expenses. While this is already a buzzword amongst business architects and data enthusiasts, what exactly does the introduction of data-fabric tools mean for you?

In an enterprise environment, managing information requires integrating diverse systems, applications, storage, and servers. This means that finding out what consumers need is often difficult without the aid of industry-wide data-analyzing, data-warehousing, and application discovery methods. Traditional IT policies such as traditional computing, client-server, or workstation-based architectures are no longer enough to satisfy the needs of companies within an ever-changing marketplace.

Companies in the information age no longer prefer to work in silos. Organizations now face the necessity of automating the management of their data sources. This entails the management of a large number of moving parts -not just one. Therefore, a data management system needs to be very flexible and customizable to cope with the fast changes taking place in information technology. The traditional IT policies may not keep up with the pace of change; thus, some IT departments might be forced to look for alternative solutions such as a data fabric approach. A data-fabric approach automates the entire data management process, from discovery to ingestion.

Data fabrics are applications that enable organizations to leverage the full power of IT through a common fabric. With this approach, real-time business decisions can be made, enabling the tactical and strategic deployment of applications. Imagine the possibilities: using data management systems to determine which applications should run on the main network or which ones should be placed on a secondary network. With real-time capabilities, these applications can also be able to use different storage configurations – meaning, real-time data can be accessed from any location, even while someone is sleeping. And because the applications running on the fabric are designed to be highly available and fault-tolerant, any failure within the same fabric will not affect other services or applications. This results in a streamlined and reliable infrastructure.

There are two types of data fabrics: infrastructure-based and application-based. Infrastructure-based data fabrics are used in large enterprises where multiple applications need to be implemented and managed simultaneously. For example, the IT department may decide to use an enterprise data lake (EDL) to use many file servers. Enterprise data lakes allow users to access data directly from the source rather than log on to a file server every time they need information. File servers are more susceptible to viruses, so IT administrators may find it beneficial to deploy their EDLS over the file server. This scenario exemplifies the importance of data preparation and recovery.

Application-wise, data preparation can be done by employing the smart enterprise graph (SEM). A smart enterprise graph is one in which all data sources (read/write resources) are automatically classified based on capacity and relevance and then mapped in a manner that intelligently allows organizations to rapidly use the available resources. Organizations can decide how to best utilize their data sources based on key performance indicators (KPIs), allowing them to make the most of their available resources. This SEM concept has been implemented in many different contexts, including online retailing, customer relationship management (CRM), human resources, manufacturing, and financial industries.

Data automation also provides the basis for big data fabric, which refers to collecting, preparing, analyzing, and distributing big data on a managed infrastructure. In a big data fabric environment, data is processed more thoroughly and more quickly than ingesting on a smaller scale. Enterprises are able to reduce costs, shorten cycle times, and maximize operational efficiencies by automating ingesting, processing, and deployment on a managed infrastructure. Enterprises may also discover ways to leverage their existing network and storage systems to improve data processing speed and storage density.

When talking about what is data fabric approach, it’s easy to overstate its value. However, in the right environments and with the right intelligence, data fabrics can substantially improve operational efficiencies, reduce maintenance costs, and even create new business opportunities. Any company looking to expand their business should consider deploying a data fabric approach as soon as possible. In the meantime, any IT department looking to streamline its operations and decrease workloads should investigate the possibility of implementing a data fabrics approach.

Technology – What Can Dremio Do For You?

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Dremio is a cloud-based platform providing business data lake storage and analytic solutions. Dremio’s is a major competitor with:

  • Denodo,
  • DataBrick, and
  • Cloudera.

Dremio provides fast, fault-tolerant, scalable, and flexible database access with MySQL, Informix, PHP, Java-location, and more. Their database engine is based on Apache Arrow and is designed for fast, low-cost, and high-throughput data access for any web application.

Dremio provides high-throughput ingested data lakes optimized on Apache Arrow and MySQL for fast, fault-tolerant, scalable, and flexible query and data ingestion. With Dremio, you can easily put together a system capable of loading information as and when the user wants it, and you get highly flexible solutions for all kinds of businesses. With Dremio, your customer can focus on building his business rather than worrying about your server requirements.

If you are looking for a web analytics solution that will give you the insight you need to improve your business runs and grow, look no further than Dremio. With their state-of-the-art technology and user-friendly user interface, you can manage your dynamic data and queries easily and efficiently with just a few clicks. With their free today and pay later plans, you can take advantage of Dremio for your small and medium-sized business. In addition to their sophisticated and powerful analytics tools, they also offer advanced reporting such as real-time reporting for enterprise deployment options.

Dremio was developed by two world-class industry veterans who have spent years developing it into what it is today. With this software, you can build a highly efficient and secure data access and analytical layer with MySQL, PHP, Informix, and other layers such as HDFS, Ceph, and Red Hat Enterprise Linux. Their objective is to provide the best in data governance and security along with easy and intuitive access to your dynamic data. The result is an intuitive solution for all of your data access needs, from scheduling data jobs to back-up and restore. With Dremio, your developers will focus on their core business and let the technology work for you to provide you with an effective data layer.

With Dremio, your team can take full advantage of their built-in semantic layer that allows them to manage and access a rich data model without writing the SQL or Java code. With Dremio, your team can: Create, drop, update and delete all information in the semantic layer. With the ability to manage, view, and search for schemas, relationships, schemas, and tables, you can take full advantage of your full Dremio license along with powerful analytical abilities.

Another way that Dremio helps your team gain analytical power is by providing easy access to their own set of tools. The most powerful tool available to your team is the Metadata Browser. With the Metadata Browser, you can preview all of the stored information in your chosen Dataset. You can see all of the relationships, columns, names, sizes, and other details that you want to work with.

If you are looking for an easy way to manage and update all of your Datasets and work with multiple Datasets simultaneously, then using the Data Catalog is a must! With the Data Catalog, you will not only be able to view your entire data catalog at once but also drill down into it for further investigation. Imagine being able to update all of your Datasets, groups, departments, and projects all in one place. This feature alone could save your team hours each week!

When you are choosing your Dremio provider, make sure that they offer the Data Catalog. Dremio also offers a data source editor, so if you are a newcomer to Dremio and do not know how to build a data source, this is a great feature to have. After all, how many times have you wanted to import a certain group of Datasets and cannot remember exactly where you saved it? The Data Catalog makes it easy and painless to import and save your data. This is probably one of the best features of Dremio that I can talk about.

Technology – The Advantages of Using Microsoft SQL Server Integration Services

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Microsoft SQL Server Integration Services (SSIS) is designed to combine the features of SQL Server with components of Enterprise Management System (EMMS) so that they can work together for enterprise solutions. Its core area of expertise is bulk/batched data delivery. As a SQL Server collection member, Integration Services is a logical solution to common organizational needs and current market trends, particularly those expressed by previously installed SQL Server users. It extends SSIS functionality, such as data extraction from external sources, data transformations, data maintenance, and data management. It also helps to convert data from one server into another.

There are several ways to use SSIS. External data sources may be data obtained from an outside source, such as a third-party application, or data obtained from an on-site database, such as a company’s own system. These external sources may contain transformations, including automatic updates, or specific requests, such as viewing certain data sources. There is also the possibility of data integration, in which different sets of data sources may be integrated into SSIS. Integration Services is useful for developing, deploying, and maintaining customer databases and other information sources.

The advantage of integrating SSIS with other vendors’ products is that it allows information to be made available within the organization and outside the organization. In other words, vendors can sell to internal users as well as external customers. Integration Services is usually sold as part of Microsoft SQL Server solutions. However, some companies may develop their own SSIS interfaces and build the entire communication layer independently.

There are two major advantages of using SSIS. The first is great support for telecommunication companies and enterprises that need to process a huge amount of information quickly and efficiently. Telecommunication companies use SSI to interface with other modules such as Microsoft Office applications, Sharepoint, and more. Another advantage of SSI is that integration provides access to all of the capabilities that a particular program or server has, such as data integration with Microsoft Visual Basic and JavaScript and the program’s full functionality or server. SSI is commonly used for web applications, particularly in sites that have to process large amounts of data quickly and efficiently.

There are a few disadvantages of using SSI, however. SSI is quite slow when compared to VBA and another object-oriented programming (OOP) methods. SSI also has some disadvantages in data quality, and the SSI interface can be difficult to use if one does not know how to code in the programming language. SSI is also limited in the number of programs and applications that can be integrated into one installation of SSI.

SSI is not only less flexible than VBA but can also be slower when compared to the traditional VBA script programs, as well. SSI can use a program or server with an SSI interface. Still, not all programs and servers that support SSI will provide an interactive command line for integration with a Microsoft SQL Server Integration Services database. In some cases, an interactive command line is necessary for SSI to use the DTS file necessary to process the data from an in-house database. SSI cannot connect to SSO independently but can use an in-house or external SSI file as a starting point for a connect and bind scenario.

For SSI to work effectively in a team-based development environment, the developer must understand and be familiar with the program. SSI has been designed with several different developer topologies and languages to write code and have it run in a timely manner while keeping track of files that might not be included with the program. A team-based development environment should be defined as a group effort where regular communication between team members and corporate databases can help this process along. SSI was designed to provide developers with the flexibility and control they need to maintain these relationships.

SSI can provide several advantages over VBA, including support for data structures in various programming languages and formats. This type of integration can save time for a business and is very cost-effective. SSI also provides several different programming interfaces and is flexible enough to use in any environment. If your company needs to use SSI, you must take the time to learn how to integrate it with your company’s database to ensure that the data structures used are compatible and effective for your application.

Technology – Denodo 8 Java Version Requirements

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In a recent customer meeting about the denodo installation requirements, the discussion turned to the supported Java version for denodo 8.   So, we looked it up to confirm, and as it turns out, the supported version of Java for denodo 8 is Oracle 11. Fortunately, it is well documented in the denodo documentation, the links to which have been provided below.

P. S. This is an increase in the Java version required for version 7, which was 1.8.

Related References

Denodo / Home / Knowledge Base / Installation & Updates / Java versions supported by the Denodo Platform

Denodo / User Manuals / Denodo Platform Installation Guide / Appendix / Supported Java Runtime Environments (JRE)

Technology – How to Install Zip and Unzip in Linux

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Zipping and unzipping files make complicated tasks like file transfer easier. Zip is a commonly utilized compression function that is portable and easy to use. One can even unzip files in Windows created in Linux.

Compression of files and folders allows faster and more effective transfer, storage, and emailing of files. Unzip is a tool that will enable you to decompress files. It is a utility unavailable on most Linux by default but can be installed easily. Below is an easy guide on doing a Linux zip and unzip installation.

How to Do a Linux Zip and Unzip Installation

There are different commands you ought to execute in the various Linux distributions.

How to Install Zip/Unzip in Debian and Ubuntu Systems

Install the zip tool by running;

$ sudo apt-get install zip

Sit back and wait a minute until the installation is completed. After installing, confirm the zip version installed by using the command

$ zip -v

To install the unzip utility, use an almost similar command

$ sudo apt install zip

You can also confirm the unzip tool installed using the command

$ unzip -v

How to Install Zip/Unzip in Fedora and Linux CentOS

The process is simple and can be done using the following command

To install the zip function, use

$ sudo dnf install zip

To install the unzip function, use

$ sudo dnf install unzip

You can check the path once the installation is complete using the following command

which unzip

You can also confirm if everything has been installed correctly by running the command below

unzip -v

It will give verbose with unzip utility details

Installing Zip/Unzip in Manjaro/Arch Linux

For these distributions, run the following command

$ sudo pacman -S zip

To install the unzip tool, run

$ sudo pacman -S unzip

Installing Zip/Unzip in OpenSUSE

Run the following command to install zip on OpenSUSE

$ sudo zipper install zip

To install the unzip tool, run

$ sudo zipper install unzip

Command Examples for Zipping and Unzipping Files in Linux

The basic syntax to create a .zip file is;

Zip options zipfile list_of_files

Using Linux to Unzip a File

You can use the unzip command without any options. It will unzip all the files to the current directory. An example is (the SampleZipFile is the result of your initial compression)

Unzip sampleZipFile.zip

It will be unzipped in the current folder by default, as long as you have read-write access.

Cautions for Zipping and Unzipping Linux

Files and folders can be password-protected. A password-protected .zip file can be decompressed using the -P option. Run the following command for this obstacle

Unzip -P Password sampleZipFile.zip

The Password in the command above is the password for the .zip file.

You may be asked whether you want to overwrite the current files, skip extraction for the current file, overwrite all files, rename the current file, or skip extraction for all files. The options would be as shown;

[y]es, [n]o, [A]ll, [N]one, [r]ename

Override these files by using the -o option. For instance;

Unzip -o sampleZipFile.zip

Take caution while executing this command since it will completely overwrite the existing copies.

Bottom Line

With these essentials on Linux zip and unzip commands, you can start improving your file management now. However, for newer Linux distributions, the zip and unzip tools already come pre-installed. You won’t have to worry about installation.

Technology – Integration Testing Vs. System Testing

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Software applications may contain several different modules, which essentially require a partnership between teams during the development process. The individually developed modules get integrated to form a ready-to-use software application. But before the software gets released to the market, it must be thoroughly tested to ensure it meets user requirement specifications.

Integration Testing

The integration testing phase involves assembling and combining the modules tested separately. It helps detect defects in the interfaces during the early stages and ensure the software components work as one unit.

Integration testing has two puposes: component and system integration testing.

  • Component integration testing: With this level of testing, it deals explicitly with the interactions between the software components tested separately.
  • System integration testing: It focuses on evaluating the interactions between various types of systems or micro-services.

System Testing

System testing is the most expansive level of software testing. It mainly involves:

  • Load testing: Determines the level of responsiveness and stability under real-life loads.
  • Usability testing: Determines the ease of use from the perspective of an end-user.
  • Functional testing: Ensures all the software features work as intended.
  • Security testing: Detects if there are any security flaws in the system that might lead to unauthorized access to data.
  • Recovery testing: Determines the possibility of recovery if the system crashes.
  • Regression testing: Confirms the software application changes have not negatively affected the existing features.
  • Migration testing; Ensures the software allows for seamless migration from old infrastructure systems to new ones when necessary.

Main Differences between Integration Testing and System Testing

Integration testing

  • Performed after modules (units) of the software have been tested separately.
  • It checks the interface modules.
  • Limited to functional testing.
  • Testers use the big bang, top-down, bottom-up, or sandwich/hybrid testing approaches.
  • Testers use a combination of white/grey box testing and black-box testing techniques.
  • Test cases mimic the interactions between modules.
  • Performed by independent developers or software developers themselves.

System testing

  • Performed after integration testing.
  • Checks the system as a whole to ensure it meets the end-user requirements.
  • It features both functional and non-functional test aspects.
  • Tests cover several areas, including usability, performance, security, scalability, and reliability.
  • Testers use black-box testing techniques.
  • Test cases mimic the real-life circumstances of a user.
  • Performed by test engineers.

There you have it!

Does charging your iPhone after 100% hurt the battery?

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I use my phone all day long and leave it on the charger with any issues through three versions of the iPhone now, and I’ve never had any problems.  However, I freely admit that I have never really thought about having my iPhone on the battery charger all the time, but someone asked if it was bad for the battery and got me to thinking.

Does the iPhone battery stop charging charger when full?  

The iPhone really is smart enough to stop accepting the charge once the iPhone’s battery reaches 100%capacity. After that, you are basically using your iPhone from the power source rather than the battery. Plus, when you do remove your iPhone from the power source, your iPhone starts out with a 100% charge.

What does shorten iPhone Battery Life?

What does shorten the battery’s life span is routinely letting the iPhone battery go dead before charging it back to 100%. When Ever possible, plug the iPhone in when the charge falls to 30% or less to reduce stress on the battery. “It’s better to recharge for shorter periods more often than to consistently wait for lengthy high-volume charging.  Letting the battery get hot also takes a toll. If you’re going to leave your iPhone plugged in for a while, removing the phone the case to let the escape is probably a good idea.

Technology – Understanding Data Model Entities

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Data Modeling is an established technique of comprehensively documenting an application or software system with the aid of symbols and diagrams. It is an abstract methodology of organizing the numerous data elements and thoroughly highlighting how these elements relate to each other. Representing the data requirements and elements of a database gra phically is called an Entity Relationship Diagram, or ERD.

What is an Entity?

Entities are one of the three essential components of ERDs and represent the tables of the database. An entity is something that depicts only one information concept. For instance, order and customer, although related, are two different concepts, and hence are modeled as two separate entities.

A data model entity typically falls in one of five classes – locations, things. events, roles, and concepts. Examples of entities can be vendors, customers, and products. These entities also have some attributes associated with them, which are some of the details that we would want to track about these entities.

A particular example of an entity is referred to as an instance. Instances form the various rows or records of the table. For instance, if there is a table titled ‘students,’ then a student named William Tell will be a single record of the table.

Why Do We Need a Data Model Entity?

Data is often stored in various forms. An organization may store data in XML files, spreadsheets, reports, and relational databases. Such a fragmented data storage methodology can present challenges during application design and data access. Writing maintainable and efficient code becomes all the more difficult when one has to think about easy data access, scalability, and storage. Additionally, moving data from one form to the other is difficult. This is where the Entity Data Model comes in. Describing the data in the form of relationships and entities, the structure of the data becomes independent of the storage methodology. As the application and data evolve, so does the Data Model Entity. The abstract view allows for a much more streamlined method of transforming or moving data.

Denodo Modeling Associations

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Denodo associations, referential constraints are part art and part science. The importance of both primary keys and associations and their effect on the denotative optimizer is hard to overstate. Combining appropriately applying primary keys and associations based on actual view use is an essential element in tuning denodo and getting the denodo optimizer to provide the best results.  To simplify matters, here are some basic concepts to help get you started.

Enterprise Relationship Diagrams (ERD)

Associations do more than just reflect the source system Enterprise Relationship Diagrams (ERD). To be denodo associations need to:

  • Be added based on actual use — not only based on source system Enterprise Relationship Diagrams (ERD). This is especially true if you are skipping tables for simplicity or efficiency purposes, which otherwise would have been used based on the Enterprise Relationship Diagram (ERD).
  • Associations need to be applied for views that are being reused in other views.  These associations need to mirror the joins to support the join and help the optimizer understand the actual relationship.

Placement of Denodo Associations

The knowledge base article (‘Best Practices to Maximize Performance II: Configuring the Query Optimizer’) is a bit misleading as it does imply that you need associations in both layers. Ideally, associations between entities in the same data source will be defined as Foreign Key constraints and can be imported from the data source (at the base view layer). Associations defined within the Denodo Platform are best defined in the semantic layer (i.e., between user-facing derived views). There is no need to define duplicate associations at other levels.  Denodo is planning to update the (‘Best Practices to Maximize Performance II: Configuring the Query Optimizer’) document to clarify this understanding of the proper placement of associations within the logical layer structure of denodo.

Importance Primary And Foreign Keys

It is essential when working with associations that the primary keys (PK) and foreign keys (FK) between views are correctly understood and identified. These primary key (PK) and foreign key (FK) indexes need to be applied (if not already imported) to the affected views, in addition to applying the referential constraints of the Association to provide the maximum opportunity for the denotative optimizer to make the correct choices.

Determining the “Principal” and “Dependent” Association Constraint

The referential constraint is defined as part of an association between two entity types. The definition for a referential constraint specifies the following information:

  • The “Principal” end of the constraint is an entity type whose entity Primary key (PK) is referenced by the foreign key (FK) dependent end.
  • The “Dependent” end of the constraint is the foreign key (FK), which references the Primary Key (PK) of the opposite side of the constraint.

Not all associations will have a Primary Key (PK) and Foreign Key (FK) relationship. Still, where these relationships exist, the referential constraint must be applied and applied correctly to ensure the denodo optimizer uses the correct optimization logic.

General Guidance When working with Data Warehouse Schemas

The basic guidelines for association referential constraints are:

  • Between dimension and Fact: the dimension is the principle
  • Between two Facts: Parent fact (the one, in a one-to-many relationships) is Primary
  • Between dimension and Bridge: The Dimension is primary

Denodo References

Denodo > Community > denodo Platform 8.0 > Associations

Denodo > Knowledge Base > Best Practices > Best Practices to Maximize Performance II: Configuring the Query Optimizer

Technology – Denodo Security Enforcement

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As the Virtual DataPort Administration Guide, explains in the section “Types of Access Rights” section, on VDP databases, views, rows, and columns. The denodo role-based access mechanism controls how and what a user or user role can use in the virtual layer, including the data catalog.

Important Denodo Security Notes

  • Consumer security authorization is imposed at the object level, then Data Level
  • Consumer security authorization is not imposed on Modeling Layers/VDP Folders
  • Using a virtual database to partition projects or subjects is a Best Practice

Basically, the ability to grant security is as follows:

VDP Database

  •  Permissions grants include connection, creation, read, write and admin privileges over a VDP database.

VDP Views

  • Permissions grants include read, write, insert, update and delete privileges over a view.

VDP Columns Within a VDP View

  • Permissions grants include the denial of the projection specific columns /fields within a view.

Row Level Security

  • Row Level restrictions can be added to allow users to obtain only the rows that match a certain condition or to return all the rows masking the sensitive fields

Denodo Virtual DataPort (VDP) Administration Guide

 For more information, see these section denodo Virtual DataPort Administration Guide:

  • Section 12.2 of the guide describes the general concepts of user and access rights management in DataPort, while
  • Section 12.3 describes how privileges are managed and assigned to users and roles using the VDP Administration Tool.

Virtual DataPort Administration Guide

Technology – Why Business Intelligence (BI) needs a Semantic Data Model

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A semantic data model is a method of organizing and representing corporate data that reflects the meaning and relationships among data items. This method of organizing data helps end users access data autonomously using familiar business terms such as revenue, product, or customer via the BI (business intelligence) and other analytics tools. The use of a semantic model offers a consolidated, unified view of data across the business allowing end-users to obtain valuable insights quickly from large, complex, and diverse data sets.

What is the purpose of semantic data modeling in BI and data virtualization?

A semantic data model sits between a reporting tool and the original database in order to assist end-users with reporting. It is the main entry point for accessing data for most organizations when they are running ad hoc queries or creating reports and dashboards. It facilitates reporting and improvements in various areas, such as:

  • No relationships or joins for end-users to worry about because they’ve already been handled in the semantic data model
  • Data such as invoice data, salesforce data, and inventory data have all been pre-integrated for end-users to consume.
  • Columns have been renamed into user-friendly names such as Invoice Amount as opposed to INVAMT.
  • The model includes powerful time-oriented calculations such as Percentage in sales since last quarter, sales year-to-date, and sales increase year over year.
  • Business logic and calculations are centralized in the semantic data model in order to reduce the risk of incorrect recalculations.
  • Data security can be incorporated. This might include exposing certain measurements to only authorized end-users and/or standard row-level security.

A well-designed semantic data model with agile tooling allows end-users to learn and understand how altering their queries results in different outcomes. It also gives them independence from IT while having confidence that their results are correct.

Technology – Denodo SQL Type Mapping

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denodo 7.0 saves some manual coding when building the ‘Base Views’ by performing some initial data type conversions from ANSI SQL type to denodo Virtual DataPort data types. So, where is a quick reference mapping to show to what the denodo Virtual DataPort Data Type mappings are:

ANSI SQL types To Virtual DataPort Data types Mapping

ANSI SQL TypeVirtual DataPort Type
BIT (n)blob
BIT VARYING (n)blob
BOOLboolean
BYTEAblob
CHAR (n)text
CHARACTER (n)text
CHARACTER VARYING (n)text
DATElocaldate
DECIMALdouble
DECIMAL (n)double
DECIMAL (n, m)double
DOUBLE PRECISIONdouble
FLOATfloat
FLOAT4float
FLOAT8double
INT2int
INT4int
INT8long
INTEGERint
NCHAR (n)text
NUMERICdouble
NUMERIC (n)double
NUMERIC (n, m)double
NVARCHAR (n)text
REALfloat
SMALLINTint
TEXTtext
TIMESTAMPtimestamp
TIMESTAMP WITH TIME ZONEtimestamptz
TIMESTAMPTZtimestamptz
TIMEtime
TIMETZtime
VARBITblob
VARCHARtext
VARCHAR ( MAX )text
VARCHAR (n)text

ANSI SQL Type Conversion Notes

  • The function CAST truncates the output when converting a value to a text, when these two conditions are met:
  1. You specify a SQL type with a length for the target data type. E.g. VARCHAR(20).
  2. And, this length is lower than the length of the input value.
  • When casting a boolean to an integertrue is mapped to 1 and false to 0.

Related References

denodo 8.0 / User Manuals / Virtual DataPort VQL Guide / Functions / Conversion Functions

Technology – Analytics Model Types

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Every day, businesses are creating around 2.5 quintillion bytes of data, making it increasingly difficult to make sense and get valuable information from this data. And while this data can reveal a lot about customer bases, users, and market patterns and trends, if not tamed and analyzed, this data is just useless. Therefore, for organizations to realize the full value of this big data, it has to be processed. This way, businesses can pull powerful insights from this stockpile of bits.

And thanks to artificial intelligence and machine learning, we can now do away with mundane spreadsheets as a tool to process data. Through the various AI and ML-enabled data analytics models, we can now transform the vast volumes of data into actionable insights that businesses can use to scale operational goals, increase savings, drive efficiency and comply with industry-specific requirements.

We can broadly classify data analytics into three distinct models:

  • Descriptive
  • Predictive
  • Prescriptive

Let’s examine each of these analytics models and their applications.

Descriptive Analytics. A Look Into What happened?

How can an organization or an industry understand what happened in the past to make decisions for the future? Well, through descriptive analytics.

Descriptive analytics is the gateway to the past. It helps us gain insights into what has happened. Descriptive analytics allows organizations to look at historical data and gain actionable insights that can be used to make decisions for “the now” and the future, upon further analysis.

For many businesses, descriptive analytics is at the core of their everyday processes. It is the basis for setting goals. For instance, descriptive analytics can be used to set goals for a better customer experience. By looking at the number of tickets raised in the past and their resolutions, businesses can use ticketing trends to plan for the future.

Some everyday applications of descriptive analytics include:

  • Reporting of new trends and disruptive market changes
  • Tabulation of social metrics such as the number of tweets, followers gained over some time, or Facebook likes garnered on a post.
  • Summarizing past events such as customer retention, regional sales, or marketing campaigns success.

To enhance their decision-making capabilities businesses have to reduce the data further to allow them to make better future predictions. That’s where predictive analytics comes in.

Predictive Analytics takes Descriptive Data One Step Further

Using both new and historical data sets predictive analytics to help businesses model and forecast what might happen in the future. Using various data mining and statistical algorithms, we can leverage the power of AI and machine learning to analyze currently available data and model it to make predictions about future behaviors, trends, risks, and opportunities. The goal is to go beyond the data surface of “what has happened and why it has happened” and identify what will happen.

Predictive data analytics allows organizations to be prepared and become more proactive, and therefore make decisions based on data and not assumptions. It is a robust model that is being used by businesses to increase their competitiveness and protect their bottom line.

The predictive analytics process is a step-by-step process that requires analysts to:

  • Define project deliverables and business objectives
  • Collect historical and new transactional data
  • Analyze the data to identify useful information. This analysis can be through inspection, data cleaning, data transformation, and data modeling.
  • Use various statistical models to test and validate the assumptions.
  • Create accurate predictive models about the future.
  • Deploy the data to guide your day-to-data actions and decision-making processes.
  • Manage and monitor the model performance to ensure that you’re getting the expected results.

Instances Where Predictive Analytics Can be Used

  • Propel marketing campaigns and reach customer service objectives.
  • Improve operations by forecasting inventory and managing resources optimally.
  • Fraud detection such as false insurance claims or inaccurate credit applications
  • Risk management and assessment
  • Determine the best direct marketing strategies and identify the most appropriate channels.
  • Help in underwriting by predicting the chances of bankruptcy, default, or illness.
  • Health care: Use predictive analytics to determine health-related risk and make informed clinical support decisions.

Prescriptive Analytics: Developing Actionable Insights from Descriptive Data

Prescriptive analytics helps us to find the best course of action for a given situation. By studying interactions between the past, the present, and the possible future scenarios, prescriptive analytics can provide businesses with the decision-making power to take advantage of future opportunities while minimizing risks.

Using Artificial Intelligence (AI) and Machine Learning (ML), we can use prescriptive analytics to automatically process new data sets as they are available and provide the most viable decision options in a manner beyond any human capabilities.

When effectively used, it can help businesses avoid the immediate uncertainties resulting from changing conditions by providing them with fact-based best and worst-case scenarios. It can help organizations limit their risks, prevent fraud, fast-track business goals, increase operational efficiencies, and create more loyal customers.

Bringing It All Together

As you can see, different big data analytics models can help you add more sense to raw, complex data by leveraging AI and machine learning. When effectively done, descriptive, predictive, and prescriptive analytics can help businesses realize better efficiencies, allocate resources more wisely, and deliver superior customer success most cost-effectively. But ideally, if you wish to gain meaningful insights from predictive or even prescriptive analytics, you must start with descriptive analytics and then build up from there.

Descriptive vs Predictive vs Prescriptive Analytics

Technology – Denodo Virtual Dataport (VDP) Naming Convention Guidance

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Denodo provides some general Virtual Dataport naming convention recommendations and guidance.  First, there is the general guidance for basic Virtual Dataport object types, and secondly, more detailed naming guidance recommends.      

Denodo Basic Virtual Dataport (VDP) Object Prefix Recommendations

  • Associations Prefix: a_{name}
  • Base Views Prefix: bv_{SystemName}_{TableName}
  • Data Sources Prefix: ds_{name}
  • Integration View Prefix: iv_{name}
  • JMS Listeners Prefix: jms_{name}
  • Interfaces Prefix: i_{name}
  • Web Service Prefix: ws_{name}

Virtual Dataport (VDP) High-Level Project Structure

Different layers are identified when creating logical folders hierarchies within each Data Virtualization project.  The recommended high-Level project folders are:

Connectivity

  • Connectivity, where related physical systems, data sources, and base views are part of this folder.

Integration

  • Integration views include the combinations and transformations views for the next layers. Not directly consumed views at this level.

Business Entities

  • Business Entities include Canonical business entities exposed to all users.

Report Views

  • Report Views include Pre-built reports and analysis frequently consumed by users.

Data Services

  • Data Services include web services for publishing views from other levels. It can contain views need for data formatting and manipulation.

Associations

  • This folder stores associations.

JMS listeners

  • This folder stores JMS listeners

Stored procedures

  • This folder stores custom stored procedures developed using the VDP API.

Denodo Knowledge Base VDP Naming Conventions

Additional more detailed naming convention and Virtual Dataport organization guidance are available in the denodo Community Knowledge Base, under Operations

Denodo Knowledge Base Virtual Dataport (VDP) Naming Conventions Online Page

Denodo Scheduler Naming Conventions

Technology – Using Logical Data Lakes

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Today, data-driven decision making is at the center of all things. The emergence of data science and machine learning has further reinforced the importance of data as the most critical commodity in today’s world. From FAAMG (the biggest five tech companies: Facebook, Amazon, Apple, Microsoft, and Google) to governments and non-profits, everyone is busy leveraging the power of data to achieve final goals. Unfortunately, this growing demand for data has exposed the inefficiency of the current systems to support the ever-growing data needs. This inefficiency is what led to the evolution of what we today know as Logical Data Lakes.

What Is a Logical Data Lake?

In simple words, a data lake is a data repository that is capable of storing any data in its original format. As opposed to traditional data sources that use the ETL (Extract, Transform, and Load) strategy, data lakes work on the ELT (Extract, Load, and Transform) strategy. This means data does not have to be first transformed and then loaded, which essentially translates into reduced time and efforts. Logical data lakes have captured the attention of millions as they do away with the need to integrate data from different data repositories. Thus, with this open access to data, companies can now begin to draw correlations between separate data entities and use this exercise to their advantage.

Primary Use Case Scenarios of Data Lakes

Logical data lakes are a relatively new concept, and thus, readers can benefit from some knowledge of how logical data lakes can be used in real-life scenarios.

To conduct Experimental Analysis of Data:

  • Logical data lakes can play an essential role in the experimental analysis of data to establish its value. Since data lakes work on the ELT strategy, they grant deftness and speed to processes during such experiments.

To store and analyze IoT Data:

  • Logical data lakes can efficiently store the Internet of Things type of data. Data lakes are capable of storing both relational as well as non-relational data. Under logical data lakes, it is not mandatory to define the structure or schema of the data stored. Moreover, logical data lakes can run analytics on IoT data and come up with ways to enhance quality and reduce operational cost.

To improve Customer Interaction:

  • Logical data lakes can methodically combine CRM data with social media analytics to give businesses an understanding of customer behavior as well as customer churn and its various causes.

To create a Data Warehouse:

  • Logical data lakes contain raw data. Data warehouses, on the other hand, store structured and filtered data. Creating a data lake is the first step in the process of data warehouse creation. A data lake may also be used to augment a data warehouse.

To support reporting and analytical function:

  • Data lakes can also be used to support the reporting and analytical function in organizations. By storing maximum data in a single repository, logical data lakes make it easier to analyze all data to come up with relevant and valuable findings.

A logical data lake is a comparatively new area of study. However, it can be said with certainty that logical data lakes will revolutionize the traditional data theories.

Technology – A 720-Degree View of the Customer

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The 360-degree view of the consumer is a well-explored concept, but it is not adequate in the digital age. Every firm, whether it is Google or Amazon, is deploying tools to understand customers in a bid to serve them better. A 360-degree view demanded that a company consults its internal data to segment customers and create marketing strategies. It has become imperative for companies to look outside their channels, to platforms like social media and reviews to gain insight into the motivations of their customers. The 720-degree view of the customer is further discussed below.

What is the 720-degree view of the customer?

A 720-degree view of the customer refers to a three-dimensional understanding of customers, based on deep analytics. It includes information on every customer’s level of influence, buying behavior, needs, and patterns. A 720-degree view will enable retailers to offer relevant products and experiences and to predict future behavior. If done right, this concept should assist retailers leverage on emerging technologies, mobile commerce, social media, cloud-based services, and analytics to sustain lifelong customer relationships

What Does a 720-Degree View of the Customer Entail?

Every business desires to cut costs, gain an edge over its competitors, and grow its customer base. So how exactly will a 720-degree view of the customer help a firm advance its cause?

Social Media

Social media channels help retailers interact more effectively and deeply with their customers. It offers reliable insights into what customers would appreciate in products, services, and marketing campaigns. Retailers can not only evaluate feedback, but they can also deliver real-time customer service. A business that integrates its services with social media will be able to assess customer behavior through tools like dislikes and likes. Some platforms also enable customers to buy products directly.

Customer Analytics


Customer analytics will construct more detailed customer profiles by integrating different data sources like demographics, transactional data, and location. When this internal data is added to information from external channels like social media, the result is a comprehensive view of the customer’s needs and wants. A firm will subsequently implement more-informed decisions on inventory, supply chain management, pricing, marketing, customer segmentation, and marketing. Analytics further come in handy when monitoring transactions, personalized services, waiting times, website performance.

Mobile Commerce

The modern customer demands convenience and device compatibility. Mobile commerce also accounts for a significant amount of retail sales, and retailers can explore multi-channel shopping experiences. By leveraging a 720-degree view of every customer, firms can provide consumers with the personalized experiences and flexibility they want. Marketing campaigns will also be very targeted as they will be based on the transactional behaviors of customers. Mobile commerce can take the form of mobile applications for secure payment systems, targeted messaging, and push notifications to inform consumers of special offers. The goal should be to provide differentiated shopper analytics.

Cloud

Cloud-based solutions provide real-time data across multiple channels, which illustrates an enhanced of customer. Real-time analytics influence decision-making in retail and they also harmonize the physical and retail digital environments. The management will be empowered to detect sales trends as transactions take place.

The Importance of the 720-Degree Customer View

Traditional marketers were all about marketing to groups of similar individuals, which is often termed as segmentation. This technique is, however, giving way to the more effective concept of personalized marketing. Marketing is currently channeled through a host of platforms, including social media, affiliate marketing, pay-per-click, and mobile. The modern marketer has to integrate the information from all these sources and match them to a real name and address. Companies can no longer depend on a fragmented view of the customer, as there has to be an emphasis on personalization. A 720-degree customer view can offer benefits like:

Customer Acquisition

Firms can improve customer acquisition by depending on the segment differences revealed from a new database of customer intelligence. Consumer analytics will expose any opportunities to be taken advantage of while external data sources will reveal competitor tactics. There are always segment opportunities in any market, which are best revealed by real-time consumer data.

Cutting Costs

Marketers who rely on enhanced digital data can contribute to cost management in a firm. It takes less investment to serve loyal and satisfied consumers because a firm is directing addressing their needs. Technology can be used to set customized pricing goals and to segment customers effectively.

New Products and Pricing

Real-time data, in addition to third-party information, have a crucial impact on pricing. Only firms with a robust and relevant competitor and customer analytics and data can take advantage of this importance. Marketers with a 720-degree view of the consumer across many channels will be able to utilize opportunities for new products and personalized pricing to support business growth

Advance Customer Engagement

The first 360 degrees include an enterprise-wide and timely view of all consumer interactions with the firm. The other 360 degrees consists of the customer’s relevant online interactions, which supplements the internal data a company holds. The modern customer is making their buying decisions online, and it is where purchasing decisions are influenced. Can you predict a surge in demand before your competitors? A 720-degree view will help you anticipate trends while monitoring the current ones.

720-degree Customer View and Big Data

Firms are always trying to make decision-making as accurate as possible, and this is being made more accessible by Big Data and analytics. To deliver customer-centric experiences, businesses require a 720-degree view of every customer collected with the help of in-depth analysis.

Big Data analytical capabilities enable monitoring of after-sales service-associated processes and the effective management of technology for customer satisfaction. A firm invested in being in front of the curve should maintain relevant databases of external and internal data with global smart meters. Designing specific products to various segments is made easier with the use of Big Data analytics. The analytics will also improve asset utilization and fault prediction. Big Data helps a company maintain a clearly-defined roadmap for growth

Conclusion

It is the dream of every enterprise to tap into customer behavior and create a rich profile for each customer. The importance of personalized customer experiences cannot be understated in the digital era. The objective remains to develop products that can be advertised and delivered to customers who want them, via their preferred platforms, and at a lower cost. 

10 Denodo Data Virtualization Use Cases

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Data virtualization is a data management approach that allows retrieving and manipulation of data without requiring technical data details like where the data is physically located or how the data is formatted at the source.
Denodo is a data virtualization platform that offers more use cases than those supported by many data virtualization products available today. The platform supports a variety of operational, big data, web integration, and typical data management use cases helpful to technical and business teams.
By offering real-time access to comprehensive information, Denodo helps businesses across industries execute complex processes efficiently. Here are 10 Denodo data virtualization use cases.

1. Big data analytics

Denodo is a popular data virtualization tool for examining large data sets to uncover hidden patterns, market trends, and unknown correlations, among other analytical information that can help in making informed decisions. 

2. Mainstream business intelligence and data warehousing

Denodo can collect corporate data from external data sources and operational systems to allow data consolidation, analysis as well as reporting to present actionable information to executives for better decision making. In this use case, the tool can offer real-time reporting, logical data warehouse, hybrid data virtualization, data warehouse extension, among many other related applications. 

3. Data discovery 

Denodo can also be used for self-service business intelligence and reporting as well as “What If” analytics. 

4. Agile application development

Data services requiring software development where requirements and solutions keep evolving via the collaborative effort of different teams and end-users can also benefit from Denodo. Examples include Agile service-oriented architecture and BPM (business process management) development, Agile portal & collaboration development as well as Agile mobile & cloud application development. 

5. Data abstraction for modernization and migration

Denodo also comes in handy when reducing big data sets to allow for data migration and modernizations. Specific applications for this use case include, but aren’t limited to data consolidation processes in mergers and acquisitions, legacy application modernization and data migration to the cloud.

6. B2B data services & integration

Denodo also supports big data services for business partners. The platform can integrate data via web automation. 

7. Cloud, web and B2B integration

Denodo can also be used in social media integration, competitive BI, web extraction, cloud application integration, cloud data services, and B2B integration via web automation. 

8. Data management & data services infrastructure

Denodo can be used for unified data governance, providing a canonical view of data, enterprise data services, virtual MDM, and enterprise business data glossary. 

9. Single view application

The platform can also be used for call centers, product catalogs, and vertical-specific data applications. 

10. Agile business intelligence

Last but not least, Denodo can be used in business intelligence projects to improve inefficiencies of traditional business intelligence. The platform can develop methodologies that enhance outcomes of business intelligence initiatives. Denodo can help businesses adapt to ever-changing business needs. Agile business intelligence ensures business intelligence teams and managers make better decisions in shorter periods.

Related References

Denodo > Data Virtualization Use Cases And Patterns

What is Development Operations (DevOps)?

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With modern businesses continually looking for ways to streamline their operations, DevOps has become a common approach to software delivery used by development and operation teams to set up, test, deploy, and assess applications.

To help you understand more about this approach, let’s briefly discuss DevOps.

What is DevOps?

DevOps comes from two words- ‘development and operations.’ It describes a set of IT practices, which seeks to have software developers and operations team work together on the same project in a more collaborative and free-flowing way.

In simple words, this is a culture that promotes cooperation between Development and Operations teams in an organization to ensure faster production in an automated, recurring manner.

The approach aims at breaking down traditional barriers that have existed between these two important teams of the IT department in any organization. When deployed smoothly, this approach can help reduce time and friction that occur when deploying new software applications in an organization.

These efforts lead to quicker development cycles, which ultimately save money and time, and give an organization a competitive edge against its rivals with longer, more ridged development cycles.

DevOps helps to increase the speed with which an organization delivers applications and services to customers, thereby competing favorably and actively in the market.

What Is Needed for DevOps to Be Successful Executed?

For an organization to appeal to customers, it must be agile, lean, and swift to respond to dynamic demands in the market.  For this to happen, all stakeholders in the delivery process have to work together.

Development teams, which focus on designing, developing, delivering, and running the software reliably and quickly, need to work with the operations team, which is tasked with the work of identifying and resolving problems in the software as soon as possible.

By having a common approach across software developers and operation teams, an organization will be able to monitor and analyze holdups and scale as quickly as possible. This way, they will be able to deliver and deploy reliable software in a shorter time.

We hope that our simplified guide has enabled you to understand what DevOps is and why it is important in modern organizations.

Technical – Big Data vs. Virtualization

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Globally, organizations are facing challenges emanating from data issues, including data consolidation, value, heterogeneity, and quality. At the same time, they have to deal with the aspect of Big Data. In other words, consolidating, organizing, and realizing the value of data in an organization has been a challenge over the years. To overcome these challenges, a series of strategies have been devised. For instance, organizations are actively leveraging on methods such as Data Warehouses, Data Marts, and Data Stores to meet their data assets requirements. Unfortunately, the time and resources required to deliver value using these legacy methods is a distressing issue. In most cases, typical Data Warehouses applied for business intelligence (BI) rely on batch processing to consolidate and present data assets. This traditional approach is affected by the latency of information.

Big Data

As the name suggests, Big Data describes a large volume of data that can either be structured or unstructured. It originates from business processes among other sources. Presently, artificial intelligence, mobile technology, social media, and the Internet of Things (IoT) have become new sources of vast amounts of data. In Big Data, the organization and consolidation matter more than the volume of the data. Ultimately, big data can be analyzed to generate insights that can be crucial in strategic decision making for a business.

Features of Big Data

The term Big Data is relatively new. However, the process of collecting and preserving vast amounts of information for different purposes has been there for decades. Big Data gained momentum recently with the three V’s features that include volume, velocity, and variety.

Volume: First, businesses gather information from a set of sources, such as social media, day-to-day operations, machine to machine data, weblogs, sensors, and so on. Traditionally, storing the data was a challenge. However, the requirement has been made possible by new technologies such as Hadoop.

Velocity: Another defining nature of Big Data is that it flows at an unprecedented rate that requires real-time processing. Organizations are gathering information from RFID tags, sensors, and other objects that need timely processing of data torrents.

Variety: In modern enterprises, information comes in different formats. For instance, a firm can gather numeric and structured data from traditional databases as well as unstructured emails, video, audio, business transactions, and texts.

Complexity: As mentioned above, Big Data comes from diverse sources and in varying formats. In effect, it becomes a challenge to consolidate, match, link, cleanse, or modify this data across an organizational system. Unfortunately, Big Data opportunities can only be explored when an organization successfully correlates relationships and connects multiple data sets to prevent it from spiraling out of control.

Variability: Big Data can have inconsistent flows within periodic peaks. For instance, in social media, a topic can be trending, which can tremendously increase collected data. Variability is also common while dealing with unstructured data.

Big Data Potential and Importance

The vast amount of data collected and preserved on a global scale will keep growing. This fact implies that there is more potential to generate crucial insights from this information. Unfortunately, due to various issues, only a small fraction of this data actually gets analyzed. There is a significant and untapped potential that businesses can explore to make proper and beneficial use of this information.

Analyzing Big Data allows businesses to make timely and effective decisions using raw data. In reality, organizations can gather data from diverse sources and process it to develop insights that can aid in reducing operational costs, production time, innovating new products, and making smarter decisions. Such benefits can be achieved when enterprises combine Big Data with analytic techniques, such as text analytics, predictive analytics, machine learning, natural language processing, data mining and so on.

Big Data Application Areas

Practically, Big Data can be used in nearly all industries. In the financial sector, a significant amount of data is gathered from diverse sources, which requires banks and insurance companies to innovate ways to manage Big Data. This industry aims at understanding and satisfying their customers while meeting regulatory compliance and preventing fraud. In effect, banks can exploit Big Data using advanced analytics to generate insights required to make smart decisions.

In the education sector, Big Data can be employed to make vital improvements on school systems, quality of education and curriculums. For instance, Big Data can be analyzed to assess students’ progress and to design support systems for professors and tutors.

Healthcare providers, on the other hand, collect patients’ records and design various treatment plans. In the healthcare sector, practitioners and service providers are required to offer accurate and timely treatment that is transparent to meet the stringent regulations in the industry and to enhance the quality of life. In this case, Big Data can be managed to uncover insights that can be used to improve the quality of service.

Governments and different authorities can apply analytics to Big Data to create the understanding required to manage social utilities and to develop solutions necessary to solve common problems, such as city congestion, crime, and drug use. However, governments must also consider other issues such as privacy and confidentiality while dealing with Big Data.

In manufacturing and processing, Big Data offers insights that stakeholders can use to efficiently use raw materials to output quality products. Manufacturers can perform analytics on big data to generate ideas that can be used to increase market share, enhance safety, minimize wastage, and solve other challenges faster.

In the retail sector, companies rely heavily on customer loyalty to maintain market share in a highly competitive market. In this case, managing big data can help retailers to understand the best methods to utilize in marketing their products to existing and potential consumers, and also to sustain relationships.

Challenges Handling Big Data

With the introduction of Big Data, the challenge of consolidating and creating value on data assets becomes magnified. Today, organizations are expected to handle increased data velocity, variety, and volume. It is now a business necessity to deal with traditional enterprise data and Big Data. Traditional relational databases are suitable for storing, processing, and managing low-latency data. Big Data has increased volume, variety, and velocity, making it difficult for legacy database systems to efficiently handle it.

Failing to act on this challenge implies that enterprises cannot tap the opportunities presented by data generated from diverse sources, such as machine sensors, weblogs, social media, and so on. On the contrary, organizations that will explore Big Data capabilities amidst its challenges will remain competitive. It is necessary for businesses to integrate diverse systems with Big Data platforms in a meaningful manner, as heterogeneity of data environments continue to increase.

Virtualization

Virtualization involves turning physical computing resources, such as databases and servers into multiple systems. The concept consists of making the function of an IT resource simulated in software, making it identical to the corresponding physical object. Virtualization technique uses abstraction to create a software application to appear and operate like hardware to provide a series of benefits ranging from flexibility, scalability, performance, and reliability.

Typically, virtualization is made possible using virtual machines (VMs) implemented in microprocessors with necessary hardware support and OS-level implementations to enhance computational productivity. VMs offers additional convenience, security, and integrity with little resource overhead.

Benefits of Virtualization

Achieving the economics of wide-scale functional virtualization using available technologies is easy to improve reliability by employing virtualization offered by cloud service providers on fully redundant and standby basis. Traditionally, organizations would deploy several services to operate at a fraction of their capacity to meet increased processing and storage demands. These requirements resulted in increased operating costs and inefficiencies. With the introduction of virtualization, the software can be used to simulate functionalities of hardware. In effect, businesses can outstandingly eliminate the possibility of system failures. At the same time, the technology significantly reduces capital expense components of IT budgets. In future, more resources will be spent on operating, than acquisition expenses. Company funds will be channeled to service providers instead of purchasing expensive equipment and hiring local personnel.

Overall, virtualization enables IT functions across business divisions and industries to be performed more efficiently, flexibly, inexpensively, and productively. The technology meaningfully eliminates expensive traditional implementations.

Apart from reducing capital and operating costs for organizations, virtualization minimizes and eliminates downtime. It also increases IT productivity, responsiveness, and agility. The technology provides faster provisioning of resources and applications. In case of incidents, virtualization allows fast disaster recovery that maintains business continuity.

Types of Virtualization

There are various types of virtualization, such as a server, network, and desktop virtualization.

In server virtualization, more than one operating system runs on a single physical server to increase IT efficiency, reduce costs, achieve timely workload deployment, improve availability and enhance performance.

Network virtualization involves reproducing a physical network to allow applications to run on a virtual system. This type of virtualization provides operational benefits and hardware independence.

In desktop virtualization, desktops and applications are virtualized and delivered to different divisions and branches in a company. Desktop virtualization supports outsourced, offshore, and mobile workers who can access simulate desktop on tablets and iPads.

Characteristics of Virtualization

Some of the features of virtualization that support the efficiency and performance of the technology include:

Partitioning: In virtualization, several applications, database systems, and operating systems are supported by a single physical system since the technology allows partitioning of limited IT resources.

Isolation: Virtual machines can be isolated from the physical systems hosting them. In effect, if a single virtual instance breaks down, the other machine, as well as the host hardware components, will not be affected.

Encapsulation: A virtual machine can be presented as a single file while abstracting other features. This makes it possible for users to identify the VM based on a role it plays.

Data Virtualization – A Solution for Big Data Challenges

Virtualization can be viewed as a strategy that helps derive information value when needed. The technology can be used to add a level of efficiency that makes big data applications a reality. To enjoy the benefits of big data, organizations need to abstract data from different reinforcements. In other words, virtualization can be deployed to provide partitioning, encapsulation, and isolation that abstracts the complexities of Big Data stores to make it easy to integrate data from multiple stores with other data from systems used in an enterprise.

Virtualization enables ease of access to Big Data. The two technologies can be combined and configured using the software. As a result, the approach makes it possible to present an extensive collection of disassociated and structured and unstructured data ranging from application and weblogs, operating system configuration, network flows, security events, to storage metrics.

Virtualization improves storage and analysis capabilities on Big Data. As mentioned earlier, the current traditional relational databases are incapable of addressing growing needs inherent to Big Data. Today, there is an increase in special purpose applications for processing varied and unstructured big data. The tools can be used to extract value from Big Data efficiently while minimizing unnecessary data replication. Virtualization tools also make it possible for enterprises to access numerous data sources by integrating them with legacy relational data centers, data warehouses, and other files that can be used in business intelligence. Ultimately, companies can deploy virtualization to achieve a reliable way to handle complexity, volume, and heterogeneity of information collected from diverse sources. The integrated solutions will also meet other business needs for near-real-time information processing and agility.

In conclusion, it is evident that the value of Big Data comes from processing information gathered from diverse sources in an enterprise. Virtualizing big data offers numerous benefits that cannot be realized while using physical infrastructure and traditional database systems. It provides simplification of Big Data infrastructure that reduces operational costs and time to results. Shortly, Big Data use cases will shift from theoretical possibilities to multiple use patterns that feature powerful analytics and affordable archival of vast datasets. Virtualization will be crucial in exploiting Big Data presented as abstracted data services.

What Is Machine Learning?

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Machine Learning

Machine learning is Artificial Intelligence (AI) which enables a system to learn from data rather than through explicit programming.  Machine learning uses algorithms that iteratively learn from data to improve, describe data, and predict outcomes.  As the algorithms ingest training data to produce a more precise machine learning model. Once trained, the machine learning model, when provided data will generate predictions based on the data that taught the model.  Machine learning is a crucial ingredient for creating modern analytics models.