When DevOps first came into the market, it meant a game-changing breakthrough for software teams regarding delivery capability. Following the same concepts of delivering value and assuring quality, DataOps aims to achieve the same results with data and analytics for businesses.
One of the most common misconceptions about DataOps is that it’s just DevOps geared towards data analytics – but it’s more than that. However, before we dive into the nuances, it’s vital to understand what they share in common: efficient project delivery.
Farewell, Waterfall (Hello, Agile).
Before DevOps, project delivery meant talking Waterfall, which we inherited from the more conventional engineering forms. Waterfall defines a fixed scope by setting estimates on time and cost, but there’s a catch.
Buildings, roads, bridges, and shopping malls follow repeatable and predictable quantities. Software, on the other hand, does not follow the same rule – meet Agile.
Agile flips the Waterfall concept by setting an estimate on the scope while defining fixed values on time and cost.
While Waterfall achieves delivery only at the end of the scope, Agile delivers a product with each iteration, making businesses notice a tangible value on their investments much sooner – at least, ideally.
Without DevOps, Agile Is Just A Concept.
The very promise of Agile is delivering a working product with every iteration, which usually happens in two-week sprints.
Considering we must achieve clear communication among disparate teams and overcome the technical challenges of integrating code as we go, it’s almost utopic to deliver anything on such short notice – unless we talk DevOps.
Born under a mindset of team collaboration focused on both Continuous Integration (CI) and Continuous Delivery (CD), DevOps closes the gap between development and value delivery by following this cycle:
Plan > Develop > Test > CI > Release > Monitor > Repeat
- CI seamlessly integrates new code into a shared repository
- CD builds, tests, and releases new products with one-click deployments
Alright, but if DevOps is so robust and well-defined, then why can’t we steer data and analytics projects with it, too?
DataOps – More Than Just DevOps For Data.
Data and Analytics don’t quite match with ordinary software projects. Instead, they relate more to business intelligence. To understand this better, let’s take a closer look at the Data & Analytics Pipeline.
Operational Data > Apply Transformations & Business Rules > Data Delivery
The Data Delivery may be a Data Store, Data Lake, Data warehouse, Data Mart or Data Virtualization environment from the data is consumed
With DataOps, we copy the business’ Operational Data to create Transforms that will follow our business rules. With them, we provide Data Delivery from which Data Scientists and Data Analysts can understand what’s going on with our business.
DataOps vs. DevOps – The Differences.
- Delivers value based on software engineering
- Assures quality upon code reviews, rigorous continuous testing, and close monitoring
- Users’ mindset geared towards coding, avantgarde tech, and complex tools
- A typical DevOps process follows Develop > Build > Test > Deploy > Run
- Team collaboration focused on software developers and IT teams
- Delivers value based on data engineering
- Assures quality upon data governance and process control
- Users’ mindset geared towards analyzing data, building predictive models, and rendering visually appealing information
- A typical DataOps process follows Sandbox Management > Develop > Innovation Pipeline Orchestration > Test > Deploy > Value Pipeline Orchestration > Monitor
- Team collaboration focused on data analysts, IT teams, and even customers