ETL testing: A comprehensive guide to ensuring data quality and integration

Introduction to ETL testing

If you’re integrating and migrating data to a new system using an Extract, Transform, and Load (ETL) process, it’s important to be sure that your data quality is high. One of the best ways to do this is with ETL testing, which evaluates whether your data is complete, accurate, and reliable — and if it has been properly loaded into your new system or data warehouse. Without ETL testing, businesses run the risk of making decisions using inaccurate or incomplete data. This can have negative impacts on revenue, strategy, and customer experience.

Here, we take a look at ETL testing and how it impacts data quality.

What is ETL (Extract, Transform, Load)

Extract/transform/load (ETL) is a data integration approach that pulls information from various sources, transforms it into defined formats and styles, then loads it into a database, a data warehouse, or some other destination.

What is ETL testing?

ETL testing is a process that verifies that the data coming from source systems has been extracted completely, transferred correctly, and loaded in the appropriate format — effectively letting you know if you have high data quality. It will identify duplicate data or data loss and any missing or incorrect data.

An ETL testing process makes sure that data transfers happen with strict adherence to transformation rules and comply with validity checks. It is different than data reconciliation used in database testing in that ETL testing is applied to data warehouse systems and used to obtain relevant information for analytics and business intelligence.

When should you use ETL testing?

It is important to use ETL testing in the following situations:

• After loading data into a new data warehouse for the first time
• After adding a new data source to an existing data warehouse
• After a data migration
• When moving data
• Anytime there are concerns with data quality or ETL process performance

Anytime you are moving or integrating data, you want to make certain that your data quality is high before you use it for analytics, business intelligence, or decision-making. If you’ve been tasked with ETL testing, you will be asked to take on some important responsibilities.

An ETL tester’s responsibilities and required skills

An ETL tester’s role is important in safeguarding the business’s data quality. Here are some key responsibilities of an ETL tester:

• Prepare and plan for testing by developing a testing strategy, a test plan, and test cases for the process
• Analyze source data for data quality concerns throughout the ETL process
• Execute test cases to validate the ETL process
• Identify defects and issues in the ETL process and work with teams to rectify them
• Communicate testing results with development teams, stakeholders, and other decision-makers
• Incorporate learnings and best practices to improve the ETL testing process over time

Overall, an ETL tester is a guardian of data quality for the organization, and should have a voice in all major discussions about data used in business intelligence and other use cases.

The ETL testing process: stages and best practices

Effective ETL testing detects problems with the source data early on — before it is loaded to the data repository — as well as finding inconsistencies or ambiguities in business rules intended to guide data transformation and integration. The process can generally be broken down into eight stages:

1. Identify business requirements — Design the data model, define business flow, and assess reporting needs based on client expectations. It’s important to start here so the scope of the project is clearly defined, documented, and understood fully by testers.
2. Validate data sources — Perform a data count check and verify that the table and column data types meet specifications of the data model. Make sure check keys are in place and remove duplicate data. If not done correctly, the aggregate report could be inaccurate or misleading.
3. Design test cases — Design ETL mapping scenarios, create SQL scripts, and define transformational rules. It is important to validate the mapping document as well, to ensure it contains all of the needed information.
4. Extract data from source systems — Execute ETL tests per business requirement. Identify types of bugs or defects encountered during testing and make a report. It is important to detect and reproduce any defects, report, fix the bug, and close bug report — before continuing to Stage 5.
5. Apply transformation logic — Ensure data is transformed to match the schema of the target data warehouse. Check the data threshold and alignment and validate data flow. This ensures that the data type matches the mapping document for each column and table.
6. Load data into target warehouse — Perform a record count check before and after data is moved from staging to the data warehouse. Confirm that invalid data is rejected and that the default values are accepted.
7. Summary report — Verify the layout, options, filters, and the export functionality of the summary report. This report lets decision-makers and other stakeholders know details and results of the testing process. If any step was not completed, the report lets them know why.
8. Test Closure — File test closure. You can now move forward with ETL knowing that your data quality is solid.

9 types of ETL tests: ensuring data quality and functionality

ETL testing fits into four general categories: new system testing (data obtained from varied sources), migration testing (data transferred from source systems to a data warehouse), change testing (new data added to a data warehouse), and report testing (validating data, making calculations).

ETL tests that may be executed in each stage are:

1. Production validation, also called “production reconciliation” or “table balancing,” validates data in production systems and compares it against source data. This guards data against faulty logic, failed loads, or operational processes that are not loaded to the system.
2. Source to target count testing verifies that the number of records loaded into the target database matches the expected record count.
3. Source to target data testing ensures projected data is added to the target system without loss or truncation, and that the data values meet expectations after transformation.
4. Metadata testing performs data type, length, index, and constraint checks of ETL application metadata (load statistics, reconciliation totals, and data quality metrics).
5. Performance testing makes sure that data is loaded into the data warehouse within expected time frames and that the test server response to multiple users and transactions is adequate for performance and scalability.
6. Data transformation testing runs SQL queries for each row to verify that the data has been correctly transformed according to business rules.
7. Data quality testing runs syntax tests (invalid characters, pattern, case order) and reference tests (number, date, precision, null check) to make sure the ETL application accepts default values and rejects and reports invalid data.
8. Data integration testing confirms that the data from all sources has loaded to the target data warehouse correctly and checks threshold values.
9. Report testing reviews data in summary report, verifying layout and functionality are as expected, and makes calculations.

Testing during the ETL process can also include user acceptance testing, GUI testing, and application migration tests to ensure the ETL architecture performs well on other platforms. Incremental ETL tests can verify that new records and updates are processed as expected.

ETL testing challenges

ETL testing can have challenges. Identifying them early in the ETL process can prevent bottlenecks and costly delays. Some of the common challenges include:

• Potential complexity of data transformations. Transformations of large datasets can be time-consuming and complex.
• Unhealthy data. Data is often messy and full of errors; ETL testing needs clean, accurate data to have healthy results.
• Resource intensiveness. ETL testing can be resource intensive when dealing with large, complex source systems.
• Data source changes. Changes to data sources impact the completeness and accuracy of data quality.
• Complex processes. Complex data integrations and business processes can cause problems.
• Slow performance. Slow processing or slow end-to-end performance caused by massive data volumes can impact data accuracy and completeness.
• Finding team members. Difficulty finding people with ETL and data health expertise.

ETL testing tools

There are numerous ETL testing tools, both open source and commercial solutions, to help make testing easier and more productive. ETL testing tools increase IT productivity and simplify the process of retrieving information from big data to gain insights. The tools contain procedures and rules for extracting and processing data, and eliminate the need for traditional programming methods that are labor-intensive and expensive.

Another benefit is that ETL testing tools have built-in compatibility with cloud data warehouse, ERP, and CRM platforms such as Amazon Web Services, Salesforce, Oracle, Informatica, Kinesis, Google Cloud Platform, NetSuite, and more.

Whether you choose open source or commercial tools, here are some things to look for when comparing ETL testing tools:

• Graphical interface to simplify the design and development of ETL processes.
• Automatic code generation to speed development and reduce errors.
• Built-in data connectors that can access data stored in commonly used file formats, a database, a packaged application, or a legacy system.
• Content management facilities that enable context switching for ETL development, testing, and production environments.
• Sophisticated debugging tools that let you track data flows in real time and generate reports on row-by-row behavior.

Cloud-native ETL tools designed specifically for cloud computing architecture enable a business to reap the full benefits of a data warehouse endeavor.

The future of ETL testing: AI, cloud, and big data

Impact of artificial intelligence (AI) on ETL testing and development

Organizations that rely on hand-coded scripts and in-house tools for manual testing lose efficiency and the ability to scale with today’s evolving ETL cloud technologies. Fast-paced, agile DevOps teams that churn out multiple software application updates daily —using automated, continuous deployment practices — are common today. The drive to move to the cloud and cloud warehouses, as well as the push towards automation, speed, and scalability, require cloud-based ETL testing tools.

Organizations need automated data integration with ETL testing tools that can process larger amounts of data autonomously — without need for human intervention — in real time. The waterfall approach (identify a problem in the data stream, fix it, test the schema, load the data to the data warehouse, and analyze it) is being replaced with cloud-native, agile solutions.

Data management cloud architectures and AI smart data integration assistants are emerging new trends. AI brings speed, scalability, and more accuracy to ETL testing. ETL testing tools that are AI-based can meet the volume and complexity of multiple data sources and help deliver faster data quality results so businesses can integrate and migrate data faster, with more confidence.

Getting started with ETL testing

Extensive ETL testing gives an enterprise confidence in the integrity of its big data and the business intelligence gained from that data, and lowers business risk. Talend Open Studio for Data Integration is an industry-leading, open source ETL development and testing tool. With millions of downloads since 2006, it is free to use under an Apache license.

Subscription-based Talend Data Integration includes the same ETL testing functionality as well as enterprise class continuous delivery mechanisms to facilitate teamwork and to run ETL testing jobs on remote systems. It also contains an audit tool for qualitative and quantitative ETL metrics.