The ETL (Extract, Transform, Load) process is at the core of modern data management, ensuring that data is accurately moved and transformed between systems. However, even the most well-designed ETL pipelines can suffer from errors compromising data quality and accuracy.
This is where ETL testing comes into play, serving as a critical checkpoint to validate data integrity throughout the ETL process. Automation in ETL testing has become essential to keep up with the growing volume of data and the need for rapid iteration. It enhances efficiency, reduces human error, and ensures scalability in managing large datasets.
What Is the ETL Testing Process?
ETL Testing is a process enabling a user to test by validating and comparing source data to destination data. It is typically done before data is moved into a production Data Warehouse system. It is sometimes also called Table Balancing or Production Reconciliation.
You can test for the following in the ETL Testing process:
- Data Mapping: Mapping between the source data attributes to destination data attributes.
- Data Quality: Identifying if the data is loaded with the correct format and attributes.
- Data Integrity: Validating the number of records between the source and target systems.
However, ETL Testing faces a couple of challenges as well. These challenges are as follows:
- Comparing large volumes of data manually is highly prone to error.
- Testing data across heterogeneous data sources such as On-Premise Databases, Flat Files, and Cloud-based Data Warehouses.
- Identifying valid test data to cover all testing scenarios.
What Are the Features of ETL Automation Testing Tools?
ETL Automation Testing reduces manual error, and time consumption during the complete automated ETL process and helps to maintain data accuracy. Here is a list of features you should look for in an ETL Automation Testing tool:
- Graphical User Interface: An ETL Automation Testing tool should enable users to create automated ETL tests as well as reduce the time it takes to implement those tests.
- Data Validation Engine: The ability to compare and validate high-volume data across databases, and files with different formats.
- Data Connectors: Choose an ETL Automation Testing tool that supports native integration with databases, files, and APIs.
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How to Select the Right ETL Automation Testing Tool
Choosing the right ETL automation testing tool is crucial for ensuring the effectiveness and efficiency of your data validation processes. Here are some key criteria to consider:
- Compatibility with Existing ETL Processes and Data Sources: The tool should seamlessly integrate with your current ETL processes and support the data sources you work with. Compatibility ensures the tool can interact with your ETL pipelines without significant modifications.
- Ease of Use and Learning Curve: Consider how intuitive the tool is for your team to use. A shorter learning curve means your team can quickly start using the tool effectively, which is especially important if your team includes members with varying levels of technical expertise.
- Scalability and Performance for Large Datasets: As your data grows, so should your testing tool’s ability to handle it. Ensure the tool can scale to meet the demands of large datasets without compromising performance.
- Integration with Other Tools in the Data Stack: The tool should integrate well with other tools in your data ecosystem, such as data warehouses, BI tools, and CI/CD pipelines. This enables smoother workflows and better overall system efficiency.
- Cost Considerations and Budget: Budget is always a factor. Evaluate the tool’s cost against your budget while considering its value in terms of features, support, and long-term benefits.
The article has covered the basics of the ETL Testing process and the desired features of an ETL Automation Testing tool. In the following sections, you will be looking at the 5 best ETL Automation Testing tools in the marketplace. The tools are as follows:
- iCEDQ
- RightData
- QuerySurge
- BiG EVAL
- Datagaps ETL Validator
1. iCEDQ
iCEDQ is a DataOps Platform for Testing and Monitoring. It has a rules-based approach that enables organizations for ETL Automation Testing, Data Migration, and Production Data Monitoring.
Key features of iCEDQ that make it an indispensable ETL Automation Testing tool are as follows:
- In-memory ETL Testing engine.
- 50+ connectors are available to connect Databases, Files, API, and BI Reports.
- Easy to collaborate with your team using an easy-to-use GUI.
- iCEDQ offers an in-built scheduler that allows users to schedule any job inside iCEDQ. It also allows its users to schedule batches and rules using an external scheduling tool like Control-M, Tidal, and Autosys to name a few.
2. RightData
RightData is a self-service ETL Automation Testing tool designed to help business teams with Data Integrity Assurance, Continuous Data Quality Control with automated validation and reconciliation capabilities.
Key features of RightData that make it an indispensable tool are as follows:
- Bulk validation capability to facilitate Data Reconciliation across the data landscape.
- Users can explore metadata, analyze, discover data by Data Profiling, and Snapshot Data to assist with Data Reconciliation.
- Better administration and control setting provisions to manage users, connections.
3. QuerySurge
QuerySurge tool is specifically built for testing of Big Data and Data warehouses which leverage analytics for providing Smart Data Testing solutions.
Key features of QuerySurge that make it an indispensable tool are as follows:
- Query Wizards allow both novice and experienced team members to quickly validate their data with no specific programming knowledge required.
- It provides a collaborative view of data health and also supports the real-time progress of test scenarios.
- Native support for multiple platforms like Oracle, Teradata, IBM, Amazon, Cloudera, etc.
4. BiG EVAL
BiG EVAL is a lightweight software solution dedicated to testing automation within any data-oriented project. It helps in the ETL automation of testing tasks during the data sync process, developing a DWH, and providing quality metrics in production.
Key features of BiG EVAL that make it an indispensable ETL Automation Testing tool are as follows:
- Autopilot testing is driven by metadata from your database schema or a metadata repository.
- Clear dashboards and alerting processes.
- Intuitive self-learning user interface.
- Data Quality Measuring and Assisted Problem Solving.
- BiG EVAL implements Metadata Based Testing as well, which automatically applies test cases to the whole Data Warehouse based on the metadata. This allows you to implement the test cases once for just one entity and apply them to all entities by the push of a button. The metadata can be pulled from Data Automation tools, Metadata Management Systems, manual lists, and technical metadata from Database Management Systems.
5. Datagaps ETL Validator
Datagaps ETL Validator is a Warehouse Testing tool. It simplifies the testing for Data Integration, Data Warehouse, and Data Migration projects and provides a comprehensive Data Testing Automation Platform.
Key features of Datagaps ETL Validator that make it an indispensable tool are as follows:
- It has a unique visual Test Case Builder with drag & drop capabilities and a Query Builder that enables defining tests without manually typing in queries.
- It provides a data model-driven interface for defining data rules to verify that the data conforms to quality standards and a range of values.
- It supports the comparison of data across heterogeneous data platforms including relational databases, Hadoop, XML, and Flat Files.
- Datagaps ETL Validator comes with an inbuilt ETL engine that can extract and compare millions of records from multiple data sources. This is carried out while simultaneously executing test cases.
- Datagaps ETL Validator also allows you to compare aggregate data such as sum, counts, distinct counts, between the source and target. This is a more targeted comparison as opposed to simply comparing large volumes of data.
- Its baselining capabilities can be used for testing incremental ETL. This can be used while slowly changing dimensions and carrying out ETL Regression Testing.
Comparison of Popular Tools
Regarding ETL automation testing, several tools stand out for their unique features and capabilities. Below is a feature-by-feature comparison of five leading ETL automation testing tools:
| Feature | iCEDQ | RightData | QuerySurge | BiG EVAL | Datagaps |
| Compatibility | High | High | High | Medium | High |
| Ease of Use | Medium | High | Medium | High | Medium |
| Scalability | High | High | High | Medium | High |
| Integration Capabilities | High | Medium | High | High | High |
| Cost | Premium | Medium | Premium | Medium | Medium |
- iCEDQ: Known for its robust features and compatibility with a wide range of ETL processes, iCEDQ is a premium tool with high scalability and excellent integration capabilities. However, it may have a steeper learning curve compared to others.
- RightData: Offers a user-friendly interface and strong compatibility, making it a great option for teams looking for ease of use. It’s scalable and performs well with large datasets, providing a balanced mix of features at a medium price point.
- QuerySurge: Designed specifically for testing data warehouses, QuerySurge excels in integration with other tools and supports complex ETL testing scenarios. It’s a premium option with high performance, though it might require more technical expertise.
- BiG EVAL: This tool is well-regarded for its ease of use and integration capabilities. While it may not scale as well as others for extremely large datasets, it offers a solid feature set at a medium cost.
- Datagaps ETL Validator: This highly compatible and scalable tool is designed to handle large datasets efficiently. It’s a medium-cost solution that balances performance with ease of use, making it a versatile choice.
Conclusion
This article gave a comprehensive list of the Top 5 ETL Automation Testing tools along with the top features for each of the tools. It also provided you with a brief overview of the ETL Automation Testing process and the challenges associated with it.
Overall, ETL Automation Testing tools play a pivotal role in Data Analytics today due to the sheer volume of data leveraged to make strategic decisions at regular time intervals.
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Share your thoughts with us on reading about top 5 ETL automation tools in the comment section below. Let us know what tools you use.
Jayesh Asrani is a Solutions Architect with deep technical expertise in real-time analytics and solution architecture. At Hevo Data, he has been instrumental in scaling global solutions teams and guiding customers through best practices in data engineering. Jayesh's proficiency in Apache Pinot, AWS, GCP, and other data engineering technologies allows him to deliver tailored solutions that address complex challenges, ensuring efficient and effective data management.
