The SQL LENGTH function returns the number of characters in a string. The LENGTH function is available in many Database Management Systems (DBMS).
The LENGTH Function Syntax
LENGTH Function Notes
If the input string is empty, the LENGTH returns 0.
If the input string is NULL, the LENGTH returns NULL.
Length Function Across Databases
When working as a technical consultant, one has to work with customer’s databases and as you move from one database to another you will find that the function commands may vary–assuming the database has an equivalent function.
Working with VQL and SQL Server got me thing about the LENGTH() function, so, here is a quick references list, which does include the SQL Server.
I’ve tried to explain the difference between OLTP systems and a Data Warehouse to my managers many times, as I’ve worked at a hospital as a Data Warehouse Manager/data analyst for many years. Why was the list that came from the operational applications different than the one that came from the Data Warehouse? Why couldn’t I just get a list of patients that were laying in the hospital right now from the Data Warehouse? So I explained, and explained again, and explained to another manager, and another. You get the picture. In this article I will explain this very same thing to you. So you know how to explain this to your manager. Or, if you are a manager, you might understand what your data analyst can and cannot give you.
OLTP stands for On Line Transactional Processing. With other words: getting your data directly from the operational systems to make reports. An operational system is a system that is used for the day to day processes. For example: When a patient checks in, his or her information gets entered into a Patient Information System. The doctor put scheduled tests, a diagnoses and a treatment plan in there as well. Doctors, nurses and other people working with patients use this system on a daily basis to enter and get detailed information on their patients. The way the data is stored within operational systems is so the data can be used efficiently by the people working directly on the product, or with the patient in this case.
A Data Warehouse is a big database that fills itself with data from operational systems. It is used solely for reporting and analytical purposes. No one uses this data for day to day operations. The beauty of a Data Warehouse is, among others, that you can combine the data from the different operational systems. You can actually combine the number of patients in a department with the number of nurses for example. You can see how far a doctor is behind schedule and find the cause of that by looking at the patients. Does he run late with elderly patients? Is there a particular diagnoses that takes more time? Or does he just oversleep a lot? You can use this information to look at the past, see trends, so you can plan for the future.
The difference between OLTP and Data Warehousing
This is how a Data Warehouse works:
The data gets entered into the operational systems. Then the ETL processes Extract this data from these systems, Transforms the data so it will fit neatly into the Data Warehouse, and then Loads it into the Data Warehouse. After that reports are formed with a reporting tool, from the data that lies in the Data Warehouse.
This is how OLTP works:
Reports are directly made from the data inside the database of the operational systems. Some operational systems come with their own reporting tool, but you can always use a standalone reporting tool to make reports form the operational databases.
Pro’s and Con’s
There is no strain on the operational systems during business hours
As you can schedule the ETL processes to run during the hours the least amount of people are using the operational system, you won’t disturb the operational processes. And when you need to run a large query, the operational systems won’t be affected, as you are working directly on the Data Warehouse database.
Data from different systems can be combined
It is possible to combine finance and productivity data for example. As the ETL process transforms the data so it can be combined.
Data is optimized for making queries and reports
You use different data in reports than you use on a day to day base. A Data Warehouse is built for this. For instance: most Data Warehouses have a separate date table where the weekday, day, month and year is saved. You can make a query to derive the weekday from a date, but that takes processing time. By using a separate table like this you’ll save time and decrease the strain on the database.
Data is saved longer than in the source systems
The source systems need to have their old records deleted when they are no longer used in the day to day operations. So they get deleted to gain performance.
You always look at the past
A Data Warehouse is updated once a night, or even just once a week. That means that you never have the latest data. Staying with the hospital example: you never knew how many patients are in the hospital are right now. Or what surgeon didn’t show up on time this morning.
You don’t have all the data
A Data Warehouse is built for discovering trends, showing the big picture. The little details, the ones not used in trends, get discarded during the ETL process.
Data isn’t the same as the data in the source systems
Because the data is older than those of the source systems it will always be a little different. But also because of the Transformation step in the ETL process, data will be a little different. It doesn’t mean one or the other is wrong. It’s just a different way of looking at the data. For example: the Data Warehouse at the hospital excluded all transactions that were marked as cancelled. If you try to get the same reports from both systems, and don’t exclude the cancelled transactions in the source system, you’ll get different results.
online transactional processing (OLTP)
You get real time data
If someone is entering a new record now, you’ll see it right away in your report. No delays.
You’ve got all the details
You have access to all the details that the employees have entered into the system. No grouping, no skipping records, just all the raw data that’s available.
You are putting strain on an application during business hours.
When you are making a large query, you can take processing space that would otherwise be available to the people that need to work with this system for their day to day operations. And if you make an error, by for instance forgetting to put a date filter on your query, you could even bring the system down so no one can use it anymore.
You can’t compare the data with data from other sources.
Even when the systems are similar. Like an HR system and a payroll system that use each other to work. Data is always going to be different because it is granulated on a different level, or not all data is relevant for both systems.
You don’t have access to old data
To keep the applications at peak performance, old data, that’s irrelevant to day to day operations is deleted.
Data is optimized to suit day to day operations
And not for report making. This means you’ll have to get creative with your queries to get the data you need.
So what method should you use?
That all depends on what you need at that moment. If you need detailed information about things that are happening now, you should use OLTP. If you are looking for trends, or insights on a higher level, you should use a Data Warehouse.
Concerning databases, the acronym ACID means: Atomicity, Consistency, Isolation, and Durability.
Why is ACID important?
Atomicity, Consistency, Isolation, and Durability (ACID) are import to database, because ACID is a set of properties that guarantee that database transactions are processed reliably.
Where is the ACID Concept described?
Originally described by Theo Haerder and Andreas Reuter, 1983, in ‘Principles of Transaction-Oriented Database Recovery’, the ACID concept has been codified in ISO/IEC 10026-1:1992, Section 4
What is Atomicity?
Atomicity ensures that only two possible results from transactions, which are changing multiple data sets:
either the entire transaction completes successfully and is committed as a work unit
or, if part of the transaction fails, all transaction data can be rolled back to databases previously unchanged dataset
What is Consistency?
To provide consistency a transaction either creates a new valid data state or, if any failure occurs, returns all data to its state, which existed before the transaction started. Also, if a transaction is successful, then all changes to the system will have been properly completed, the data saved, and the system is in a valid state.
What is Isolation?
Isolation keeps each transaction’s view of database consistent while that transaction is running, regardless of any changes that are performed by other transactions. Thus, allowing each transaction to operate, as if it were the only transaction.
What is Durability?
Durability ensures that the database will keep track of pending changes in such a way that the state of the database is not affected, if a transaction processing is interrupted. When restarted, databases must return to a consistent state providing all previously saved/committed transaction data
Here is a table quick reference of some common database and/or connection types, which use connection level isolation and the equivalent isolation levels. This quick reference may prove useful as a job aid reference, when working with and making decisions about isolation level usage.