ETL (Extract, Transform and Load) forms a core backbone for amalgamating the data into a single repository and making it ready for analysis use. Today, due to an exponential surge in the volume and type of data, ETL has added challenges like complexity, number, and maintenance of the process. Therefore, it is critical to adopt a Test-Driven Development (TDD) approach for ETL as well. This will enable organizations to ensure their data is of the highest quality and help businesses trust that data.
This article provides you a comprehensive list of the Top 5 Popular ETL Automation Testing Tools and describes their features briefly, along with few limitations of leveraging these tools. It also outlines the need for ETL Automation Testing tools and provides directives on certain factors that will help companies choose the best tool to meet their business needs.
Table of Contents
- Introduction to the ETL Testing Process
- Understanding the Features of ETL Automation Testing Tools
- Best ETL Automation Testing Tools
- Working knowledge of ETL applications.
- Basics understanding of ETL Automation Testing Tools.
Introduction to 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, 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, Cloud-based Data Warehouse.
- Identifying valid test data to cover all testing scenarios.
Understanding the Features of ETL Automation Testing Tools
ETL Automation Testing reduces manual error, time consumption during the complete 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 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, 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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Best ETL Automation Testing Tools
The article has covered the basics of the ETL Testing process and the desired features in 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 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, 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.
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.
QuerySurge tool is specifically built for testing of Big Data and Data Warehouse 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 automation 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, 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.
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.Visit our Website to Explore Hevo
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