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.
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.
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.
It is important to use ETL testing in the following situations:
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 role is important in safeguarding the business’s data quality. Here are some key responsibilities of an ETL tester:
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.
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:
See why Qlik and Talend are a Leader in the Gartner® Magic Quadrant™ for Data Integration Tools
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:
Category | ETL Tests |
---|---|
New System Testing | — Data quality testing — Metadata testing |
Migration Testing | — Data quality testing — Source to target count testing — Source to target data testing — Performance testing — Data transformation testing — Data integration testing |
Change Testing | — Data quality testing — Source to target count testing — Source to target data testing — Production validation — Data integration testing |
Report Testing | — Report testing |
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 can have challenges. Identifying them early in the ETL process can prevent bottlenecks and costly delays. Some of the common challenges include:
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:
Cloud-native ETL tools designed specifically for cloud computing architecture enable a business to reap the full benefits of a data warehouse endeavor.
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.
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.