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 with a comprehensive list of the Top 5 Popular ETL Automation Testing Tools and describes their features briefly, along with a 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
Prerequisites
- Working knowledge of ETL applications.
- Basics understanding of ETL Automation Testing Tools.
What Is the ETL Testing Process?
Image Source: Altexsoft
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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Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line.
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- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the pipelines you set up. You need to edit the properties of the event object received in the transform method as a parameter to carry out the transformation. Hevo also offers drag-and-drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
- Connectors: Hevo supports 150+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, and PostgreSQL databases to name a few.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 150+ sources, that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
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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
Image Source: Software Testing Tools Guide
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
Image Source: Crunchbase
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
Image Source: QATest Lab
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
Image Source: 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
Image Source: Qualitest
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.
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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Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications to be visualized in a BI tool.
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Jayesh is a proficient Solutions Architect who has been a key player in the dynamic landscape of real-time analytics and solution architecture. At Hevo Data, he has not only scaled global solutions teams, but has also guided customers on data engineering best practices and architectural patterns. He has broad skill set spanning Apache Pinot, AWS, GCP, and data engineering