Automated DataWarehouse Testing
How to create a DataOps process for Data Quality and Data Testing
In this article, we're going to take a look at the world of DataOps and explore an increasingly common challenge:
"How can we operationalize Data Quality and Data Testing in a complex DataWarehouse environment?"
At BiG EVAL, we've seen an increase in demand for Data Quality and Data Testing use cases within DataOps environments.
As teams grapple with the complexity of managing multiple analytics environments, it has become an urgent priority to devise a more innovative way to deliver large-scale, automated data testing.
What is DataOps?
Before we discuss DataOps Data Testing, we need to provide a brief overview of DataOps.
DataOps is a relatively young discipline that has gained traction as companies recognize the need for a more effective way to manage their data landscape complexity, particularly with business intelligence and data analytics.
Data warehousing has become a valuable deployment use case for DataOps due to the enormous volumes of data and the number of inbound/outbound pipelines that operational teams need to control when delivering a data analytics capability.
DataOps aims to simplify the organization and orchestration of core data functions in much the same way that DevOps did for controlling software.
The DevOps fundamentals of agile, lean and continuous delivery still apply to DataOps, but applying agile approaches to data increases the difficulty to a whole new level.
(Note: We provide links to useful articles/research on DataOps at the end of this article).
Why has DataOps Testing become a problem for Data Warehousing and Analytics?
Even a modest data warehouse operation still needs to coordinate thousands of changes every year to ensure business users have access to accurate and timely information.
To maintain trust in the data, data analytics operations teams need to ensure all changes get thoroughly tested across all environments (e.g. Dev, QA, Pre-Production and Production).
This requirement for data testing presents a challenge because:
The Data Quality Dimension
A further challenge is that DataOps Data Testing doesn't just require a processing change to trigger a round of testing.
The DataOps team needs to ensure any data flowing through the pipelines and systems of a data analytics environment are continuously monitored and assessed against a comprehensive library of data quality rules.
DataOps presents a different level of complexity compared to DevOps
The added complexity of data is what makes DataOps intrinsically more complex than DevOps:
Building a Data Warehouse Testing capability that is fit for DataOps deployment
This next section of the article will discuss some of the most recent Data Warehousing use cases we've been involved with, highlighting the benefit of integrating a Data Testing capability within a DataOps environment.
What do you need from a DataOps Data Testing solution?
When we talk with clients, here are some of the typical challenges they're looking to solve:
Closing the gap between business needs and DataOps delivery
Most data teams have a perennial challenge of keeping up with the business demand for analytics.
Unfortunately, most companies rely on outdated data testing practices. With the volume and frequency of data tests required in a busy data analytics environment, it's easy for cracks to appear.
The diagram below highlights a simplified Data Warehouse environment found in many data analytics operations:
As you can see, this is a standard Data Warehouse configuration that requires extensive data testing at various locations.
So let's walk through each stage of testing and explain how to approach it for optimal success.
Source and Target System Data Quality Assurance
You've probably heard the phrase garbage-in, garbage-out.
It's a sad reality that many Data Warehouse environments ignore the most critical data test of all – monitoring the quality of inbound and outbound data flowing from production systems into the analytics platform and onwards to further operational, analytical or reporting systems.
To address this, you need to deploy a data quality management testing/monitoring capability at the point of 'data ingestion' before the data enters your Data Warehouse.
As you see in the diagram below, we deploy our BiG EVAL Data Quality Management tool within the client analytics environment to perform this function:
There are some profound benefits from deploying a tool such as BiG EVAL Data Quality Management in this context:
Data Test Automation
Data Warehouse environments are continually evolving and updating, hence the need for a DataOps capability to serve the insatiable analytics appetites of the modern enterprise.
Your data testing solution needs to fit into this paradigm.
With DataOps, each time a developer makes a feature change, they need to execute an associated set of data tests to confirm they won't break anything when that feature is promoted to the production environment.
As each feature is added, the number of tests increases exponentially because even a simple piece of code or logic can require a large number of data tests. The testing team soon becomes a bottleneck due to 'test saturation' if forced to test scripts and routines manually.
This ability to align your data testing requirements to the scale and volume required for DataOps is one of the increasingly common use cases we see right now for Data Test Automation.
When you synchronize data testing with continuous development and integration, you also ensure that any feature releases are gated (in a similar way to the Data Quality gates earlier). Changes can't pass into production without the required data tests being executed and successfully achieving their predefined thresholds.
In the 'old world' of manual data testing, it was common for features to be released without appropriate testing simply because the testing resource was limited, and the timescales required were impractical.
With the automated data testing approach we provide with BiG EVAL, everything changes. You can harmonize data testing with each sprint and feature drop coming out of your DataOps process.
Let's explore how we can approach this.
What are the DataOps foundations your Data Testing approach needs to align with?
Continuous Integration and Deployment
Operating within a continuous delivery pattern requires development teams to work with a trusted source of testing rules, just as they work with a single code source in the DevOps world.
With BiG EVAL, we've made it our mission to eliminate the traditional approach of 'scattered' data testing rules. Our solution is built around a managed repository of data testing rules shared amongst your operational environments whilst retaining a 'single source of truth'.
Data Pipeline Orchestration
Orchestration is delivered through a range of operational workflows that call and coordinate the many data pipeline functions required to successfully move data around the analytics environment or other data processing stages.
As such, you could argue that orchestration provides the 'engine room' of your DataOps capability.
Your data quality and data testing routines need to be called from the orchestration workflows.
You can't deliver DataOps with manual testing, hence the need for automated testing.
Executing automated tests under a DataOps environment means that test scripts and specifications need to be created in advance with every new analytics feature or update.
With test automation, it's much easier to run your tests regularly and often. You're also not just testing code but the data itself.
The image below provides a quick overview of where BiG EVAL Data Test Automation is deployed, giving you a sense of the breadth of testing scenarios you need to support in a DataOps environment:
With the focus on the increased deployment speed that DataOps must support, there is an added need to monitor the entire data analytics environment to ensure any defects and anomalies are trapped before they cause serious harm.
Monitoring and testing need to work in tandem. Once issues are detected, new data testing rules can be created to ensure future defects are trapped within the source data environment, and any code releases don't create defects.
Putting it all together
In the following diagram, we've highlighted where Data Test Automation and Data Quality Testing needs to be executed to support DataOps in a standard Data Warehousing configuration.
Of course, we're biased at BiG EVAL in that our testing solutions operate exceptionally well in this configuration.
Still, whatever testing solution you opt for, you'll find that for your DataOps environment to function, you'll need to deploy this type of arrangement.
Resources and Next Steps
If you want to learn more about automating data tests and data quality within a DataOps environment, the following resources may be helpful.
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