The ETL process vastly improves the usability of your data, transforming it into powerful information that may be used for analytics and business intelligence. But ETL isn't magic. You will need a method of verification to ensure your process complies with organizational needs. Testing your ETL process helps ensure that the data that moves from multiple sources into a warehouse is accurate and complete. As a result, that means your final data sets are a reliable foundation on which you can base your decisions.
Unfortunately, there is no one-size-fits-all type of ETL test. The complex and multi-faceted nature of most data sets requires that engineers take a customized approach to each ETL process, and create a testing protocol appropriate for each data model and projected output. The good news is that, despite this disparity, there are general categories of testing types as well as stages of testing implementation. These are the basics that help you get the most out of your ETL solution.
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Know Your Goals
Each organization uses its data in different ways. Depending on the type of information you collect, you must ensure not only accuracy but a high level of security. That makes each ETL data conversion process unique. An organization that uses ETL for the conversion of complex scientific or personal health data, for example, may test its ETL differently than an organization that processes marketing, financial or sales data.
At its most basic, ETL testing is designed to make sure you have all of your data. It can also do the following:
- Validate the accuracy of data
- Make sure it is in compliance with transformation rules
- Prevent data loss or duplication
Even those fundamental goals comes with certain qualifiers. For example, transformation rules may include high levels of encryption of personal information.
ETL testing can also do more than ensure security and accuracy. After all, the purpose of your ETL setup is so you can make effective use of the data you collect. Most likely, that data comes from a number of disparate sources and must be transformed into an actionable format before it can be useful to your business. In that transformation process, you want to make sure you're not losing any data, counting it twice, or introducing errors.
Testing also happens more than once. That's because each ETL project has output designed for a specific business purpose, from converting Salesforce data to collecting market study statistics. The transformation rules along the ETL pipeline make sure you not only get correct data, but output in the right format. Testing, therefore, not only spots and removes any bugs in corrupt data, but verifies that the ETL pipeline is working correctly and serving its specified purpose.
Basic Outline of the ETL Testing Process
Since testing is designed to identify any problems along the ETL pipeline, the general format of the process involves creating test cases and running them through the ETL before you impose the process on real data. As a general guideline, here's how that might take place:
- Define the Business Case. This phrase refers to the output of the data and what it's designed to achieve. At this point, you may also develop dimensional modeling of the data you plan to extract. This puts the data into context so it becomes a functional resource for a number of different queries. The data may be focused on a series of facts, such as a list of customer names. But a dimensional model would include a web of contextual information (ie number of sales, average dollar amount, frequency, and date of last purchase).
- Analyze Data Sources. Your data should be compliant with your dimensional model. It should also have a degree of consistency that makes it transformable according to the ETL process. If the metadata is inconsistent, you may use data mapping to ensure the data can undergo the entire ETL process successfully.
- Create and Run Test Cases. The design of test cases depends on the portion of the ETL process that's under analysis. In addition, the type of test may depend on external factors, such as the window of testing time available before the real ETL process must occur.
- Reporting on Test Outcomes. The final stage is a report on the test cases and what insight they provide into the ETL process. Specifically, they can show how the data may have to undergo modification before transformation or how the transformation rules are or are not capturing the output desired by the business case and the dimensional models.
Your test cases are the best way for you to confirm that your ETL is working the way it is supposed to, so you have full confidence in your final data set.
Categories of ETL Tests
The previous section outline a generic test case that can be run through an ETL pipeline in order to fix any errors before you put your real source data through the process. In addition to these general stages, you must also decide which type of ETL test you need to conduct and where you will implement it in your pipeline. This decision will largely be determined by organizational factors, such as your time and budget constraints:
- Unit or Component Tests. These are micro-tests that ensure ETL validity for a specific job.
- Integration or End-to-End Tests. These are broader, more complex tests that analyze the entire ETL pipeline, particularly when several jobs are engaged.
- Black Box Tests. This is similar to an end-to-end test, however, it looks only at the inputs and outputs to verify ETL success. It does not take an in-depth look at all stages of the ETL pipeline.
ETL Testing with Xplenty
Testing your ETL process is made even easier when you build pipelines with Xplenty. The drag-and-drop pipeline builder lets you create and modify your process without having to write code. In addition, Xplenty supports all major data warehouses and data targets, making it an easy fit for organizations across industry sectors. To learn more about how Xplenty can optimize your pipeline building and testing process, contact us today to schedule a demo and experience the platform for yourself.