14 Best ETL Testing Tools in 2024 (Top Selective)

List and comparison of the best open source, free and commercial ETL Testing tools on the market:

Almost all the IT companies today, highly depend on data flow as a large amount of information is made available for access and one can get everything that is required.

This is where the concept of ETL and ETL Testing comes into the picture. Basically, ETL is abbreviated as Extraction, Transformation, and Loading. Presently ETL Testing is performed using SQL scripting or using spreadsheets which may be a time-consuming and error-prone approach.

In this article, we will have detailed discussions on several concepts viz. ETL, ETL Process, ETL testing, and different approaches used for it along with the most popular ETL testing tools.

Also read => ETL Testing Tips

What is ETL Testing?

#1) As mentioned previously ETL stands for Extraction, Transformation, and Loading are considered to be the three prime database functions.

• Extraction: Reading data from the database.
• Transformation: Converting the extracted data into the required form to store in another database.
• Loading: Writing the data into the target database.

#2) ETL is used to transfer or migrate the data from one database to another, to prepare data marts or data warehouses.

The following diagram elaborates the ETL Process in a precise way:

List of the Most Popular ETL Testing Tools

Like automation testing, ETL Testing can also be automated. Automated ETL Testing reduces time consumption during the testing process and helps to maintain accuracy.

Few ETL Testing Automation Tools are used to perform ETL Testing more effectively and rapidly.

Given below is a list of the top tools for ETL testing:

1. RightData
2. Integrate.io
3. iCEDQ
4. BiG EVAL
5. Informatica Data Validation
6. QuerySurge
7. Datagaps ETL Validator
8. QualiDI
9. Talend Open Studio for Data Integration
10. Codoid’s ETL Testing Services
11. Data Centric Testing
12. SSISTester
13. TestBench
14. DataQ

Here is a detailed review of these tools.

#1) RightData

RDt is a self-service ETL/Data Integrations testing tool designed to help business and technology teams with the automation of data quality assurance and data quality control processes.

RDt’s intuitive interface allows users to validate and reconcile data between datasets regardless of the differences in the data model or the data source type. It is designed to work efficiently for data platforms with high complexity and huge volumes.

Key Features:

• Powerful universal query studio where users can perform queries on any data source (RDBMS, SAP, Files, Bigdata, Dashboards, Reports, Rest APIs, etc.), explore metadata, analyze data, discover data by data profiling, prepare by performing transformations and cleansing, and snapshot data to assist with data reconciliation, business rules, and transformations validation.
• Using RDt, users can perform field-to-field data comparisons regardless of the differences in the data model and structure between source and target.
• It comes with a pre-delivered set of validation rules along with a custom business rule builder.
• RDt has bulk comparison capacities to facilitate technical data reconciliation across the project landscape (e.g. compare production environment data with UAT, etc.)
• Robust alerting and notification capabilities starting from emails through automatic creation of defect/incident management tools of your choice.
• RDt’s data quality metrics and data quality dimension dashboard allow data platform owners an insight into the health of their data platform with drill-down capabilities into the scenarios and exact records and fields causing the validation failures.
• RDt can be used for testing analytics/BI tools like Tableau, Power BI, Qlik, SSRS, Business Objects Webi, SAP Bex, etc.
• RDt’s two-way integration with CICD tools (Jenkins, Jira, BitBucket, etc.) assists your data team’s journey of DevOps enablement through DataOps.

#2) Integrate.io

Integrate.io is a data integration, ETL, and ELT platform. This cloud-based platform will streamline data processing. It provides an intuitive graphic interface to implement an ETL, ELT, or a replication solution. With Integrate.io you will be able to perform out-of-the-box data transformations.

Key Features:

• Integrate.io’s workflow engine will help you to orchestrate and schedule data pipelines.
• You will be able to implement complex data preparation functions by using rich expression language.
• It has the functionalities to schedule jobs, monitor job progress, and status as well as sample data outputs, and ensure correctness and validity.
• Integrate.io’s platform will let you integrate data from more than 100 data stores and SaaS applications.
• Integrate.io offers both low-code or no-code options.

#3) iCEDQ

iCEDQ enables Left Shift Approach, which is central to DataOps. We recommend starting early in the non-production phase to test data and continuously monitor the production data.

iCEDQ’s rules-based approach empowers users to automate ETL Testing, Cloud Data Migration Testing, Big Data Testing, and Product Data Monitoring.

Key Features:

• An in-memory engine that can evaluate billions of records at scale.
• Enables users to do transformation testing, duplicate data testing, schema testing, Type II dimension testing, and much more.
• Advance groovy scripting for data prep, cleansing, triggering API’s, shell scripts, or any external process.
• Import custom Java libraries or create reusable test functions.
• Implement DataOps by integrating with any Scheduling, Orchestration, GIT, or DevOps tool.
• Push results to Slack, Jira, ServiceNow, Alation, Manta, or any enterprise product.
• Single Sign-On, Advanced role-based access control, and Encryption features.
• Use the inbuilt Dashboard module or enterprise reporting tools like Tableau, Power BI, and Qlik to generate reports for more insight.
• Deploy anywhere. On-Prem or in AWS, Azure, GCP, IBM Cloud, Oracle Cloud, or other platforms.

#4) BiG EVAL

BiG EVAL is a comprehensive suite of software tools aimed at leveraging the value of enterprise data by continuously validating and monitoring quality. It automates testing tasks during ETL and DWH development and provides quality metrics in production.

Features:

• Autopilot testing for agile development, driven by metadata from your database or metadata repository.
• Data Quality Measuring and Assisted Problem Solving.
• High-performance in-memory scripting and rules engine.
• Abstraction of any kind of data (RDBMS, APIs, Flatfiles, Business applications cloud / on-premises).
• Clear modern dashboards and alerting processes.
• Embeddable into DevOps CI/CD flows, ticket systems, and more.
• BiG EVAL checks data against your very own and scenario-specific quality criteria.
• User-defined test cases give great flexibility when you need your own testing algorithms.

#5) Informatica Data Validation

Informatica Data Validation is a GUI based ETL Testing tool which is used to extract, transform and load (ETL). The testing includes a comparison of tables before and after data migration.

This type of testing ensures data integrity, i.e. the volume of data is correctly loaded and is in the expected format into the destination system.

Key Features:

• Informatica Validation tool is a comprehensive ETL Testing tool which does not require any programming skill.
• It provides automation during ETL testing which ensures if the data is delivered correctly and is in the expected format into the destination system.
• It helps to complete data validation and reconciliation in the testing and production environment.
• It reduces the risk of introducing errors during transformation and avoids bad data being transformed into the destination system.
• Informatica Data Validation is useful in the Development, Testing and Production environment where it is necessary to validate the data integrity before moving into the production system.
• 50 to 90% of cost and effort can be saved using the Informatica Data Validation tool.
• Informatica Data Validation provides a complete solution for data validation along with data integrity.
• Reduces programming efforts and business risks due to an intuitive user interface and built-in operators.
• Identifies and prevents data quality issues and provide greater business productivity.
• Allows 64% free trial and 36% paid service that reduces the time and cost required for data validation.

#6) QuerySurge

QuerySurge tool is specifically built for testing of Big Data and Data warehouse. It ensures that the data extracted and loaded from the source system to the destination system is correct and is as per the expected format. Any issues or differences are identified very quickly by QuerySurge.

Key Features:

• QuerySurge is an automated tool for Big Data Testing and ETL Testing.
• It improves the data quality and accelerates testing cycles.
• It validates data using the Query Wizard.
• It saves time & cost by automating manual efforts and schedules tests for a specific time.
• QuerySurge supports ETL Testing across various platforms like IBM, Oracle, Microsoft, SAP.
• It helps to build test scenarios and test suit along with configurable reports without specific knowledge of SQL.
• It generates email reports through an automated process.
• Reusable query snippet to generate reusable code.
• It provides a collaborative view of data health.
• QuerySurge can be integrated with HP ALM, TFS, IBM Rational Quality Manager.
• Verifies, converts, and upgrades data through the ETL process.
• It is a commercial tool that connects source and target data and also supports real-time progress of test scenarios.

#7) Datagaps ETL Validator

ETL Validator tool is designed for ETL Testing and Big Data Testing. It is a solution for data integration projects. The testing of such data integration project includes various data types, huge volume, and various source platforms.

ETL Validator helps to overcome such challenges using automation which further helps to reduce the cost and to minimize efforts.

• ETL Validator has an inbuilt ETL engine which compares millions of records from various databases or flat files.
• ETL Validator is data testing tool specifically designed for automated data warehouse testing.
• Visual Test Case Builder with drag and drop capability.
• ETL Validator has features of Query Builder which writes the test cases without manually typing any queries.
• Compare aggregate data such as count, sum, distinct count etc.
• Simplifies the comparison of database schema across various environment which includes data type, index, length, etc.
• ETL Validator supports various platforms such as Hadoop, XML, Flat files etc.
• It supports email notification, web reporting etc.
• It can be integrated with HP ALM which results in sharing of test results across various platforms.
• ETL Validator is used to check Data Validity, Data Accuracy and also to perform Metadata Testing.
• Checks Referential Integrity, Data Integrity, Data Completeness and Data Transformation.
• It is a commercial tool with 30 days trial and requires zero custom programming and improves business productivity.

#8) QualiDI

QualiDi is an automated testing platform which offers end to end testing and ETL Testing. It automates ETL Testing and improves the effectiveness of ETL Testing. It also reduces the testing cycle and improves data quality.

QualiDI identifies bad data and non-compliant data very easily. QualiDI reduces the regression cycle and data validation.

Key Features:

• QualiDI creates automated test cases and it also provides support for automated data comparison.
• It offers data traceability and test case traceability.
• It has a centralized repository for requirements, test cases, and test results.
• It can be integrated with HPQC, Hadoop, etc.
• QualiDI identifies a defect in the early stage which in turn reduces the cost.
• It supports email notifications.
• It supports the continuous integration process.
• It supports Agile development and the rapid delivery of sprints.
• QualiDI manages complex BI Testing cycles and eliminates human error and data quality maintenance.

#9) Talend Open Studio for Data Integration

Talend Open Studio for Data Integration is an open-source tool that makes ETL Testing easier. This includes all ETL Testing functionality and additional continuous delivery mechanisms. With the help of Talend Data Integration tool, a user can run the ETL jobs on the remote servers that too with a variety of operating systems.

ETL Testing ensures that data is transformed from the source system to the target without any data loss and thereby adhering to transformation rules.

Key Features:

• Talend Data Integration supports any type of relational database, Flat files, etc.
• Integrated GUI which simplifies the design and development of ETL processes.
• Talend Data Integration has inbuilt data connectors with more than 900 components.
• It detects business ambiguity and inconsistency in transformation rules quickly.
• It supports remote job execution.
• Identifies defects at an early stage to reduce costs.
• It provides quantitative and qualitative metrics based on ETL best practices.
• Context switching is possible between
• ETL development, ETL testing, and ETL production environment.
• Real-time data flow tracking along with detailed execution statistics.

#10) Codoid’s ETL Testing Services

Codoid’s ETL and data warehouse testing service includes data migration and data validation from the source to the target system. ETL Testing ensures that there is no data error, no bad data or data loss while loading data from the source to the target system.

It quickly identifies any data errors or any other general errors that occurred during the  ETL process.

Key Features:

• Codoid’s ETL Testing service ensures data quality in the data warehouse and data completeness validation from the source to the target system.
• ETL Testing and data validation ensure that the business information transformed from source to target system is accurate and reliable.
• The automated testing process performs data validation during and post data migration and prevents any data corruption.
• Data validation includes count, aggregates, and spot checks between the target and actual data.
• The automated testing process verifies if data type, data length, indexes are accurately transformed and loaded into the target system.
• Data quality Testing prevents data errors, bad data or any syntax issues.

#11) Data-Centric Testing

Data-Centric testing tool performs robust data validation to avoid any glitches such as data loss or data inconsistency during data transformation. It compares data between systems and ensures that the data loaded into the target system is exactly matching with the source system in terms of data volume, data type, format, etc.

Key Features:

• Data-Centric Testing is build to perform ETL Testing and Data warehouse testing.
• Data-Centric Testing is the largest and oldest testing practice.
• It offers ETL Testing, data migration, and reconciliation.
• It supports various relational databases, Flat files, etc.
• Efficient Data validation with 100% data coverage.
• Data-Centric Testing also supports comprehensive reporting.
• The automated process of data validation generates SQL queries which result in the reduction of cost and efforts.
• It offers a comparison between heterogeneous databases like Oracle & SQL Server and ensures that the data in both systems is in the correct format.

#12) SSISTester

SSISTester is a framework that helps in the unit and integration testing of SSIS packages. It also helps to create ETL processes in a test-driven environment which thereby helps to identify errors in the development process.

There are a number of packages created while implementing ETL processes and these need to be tested during unit testing. An integration test is also a “Live test”.

Key Features:

• The unit test creates and verifies tests and once execution gets complete it performs a clean-up job.
• Integration test verifies that all packages are satisfied post-execution of the unit test.
• Tests are created in a simple way as the user creates it in Visual Studio.
• Real-time debugging of a test is possible using SSISTester.
• Monitoring of test execution with user-friendly GUI.
• Test results are exported in HTML format.
• It removes external dependencies by using fake source and destination addresses.
• For the creation of tests, it supports any .NET language.

#13) TestBench

TestBench is a database management and verification tool. It is a unique solution which addresses all issues related to the database. User managed data rollback to improve testing productivity and accuracy.

It also helps to reduce environment downtime. TestBench reports all inserted, updated, and deleted transactions which are performed in a test environment and capture the status of the data before and after the transaction.

Key Features:

• It always maintains data confidentiality to protect data.
• It has a restoration point for an application when a user wants to return back to a specific point.
• It improves decision making knowledge.
• It customizes data sets to improve test efficiency.
• It helps with maximum test coverage and helps reduce time and money.
• Data privacy rules ensure that live data is not available in the test environment.
• Results are compared with various databases. The results include differences in tables & operation performed on tables.
• TestBench analyzes the relationship between the tables and maintains the referential integrity between tables.

#14) DataQ

DataQ provides various tools for quickly identifying data issues. The platform is very intuitive and designed for both developers and testers. It is built ground up for the high volume of data, so whether you have hundreds of records or billions, we have you covered.

• Automate ETL Testing and Monitoring.
• Data Migration Testing with auto-detection of keys.
• Data Quality Monitoring – Freshness, Distribution, Volume, Schema, Completeness, Accuracy.
• Auto Suggestion of Data Quality rules.
• Cross-reference data validation across multiple data sources.
• Can connect to over 40 different data sources, various file formats, Kafka, and API out of the box.
• Ability to create a library of custom functions.
• Schema Validation
• Data Profile comparison
• Compute resources are initialized and terminated on demand.
• On-prem and cloud-agnostic solution.
• Jira, Slack, Teams integration.

Points to Remember

While performing ETL testing, several factors are to be kept in mind by the testers.

Some of them are listed below:

• Apply suitable business transformation logic.
• Execute backend data-driven tests.
• Create and execute absolute test cases, test plans, and test harness.
• Assure accuracy of data transformation, scalability and performance.
• Make sure E

• TL application reports invalid values.
• Unit tests should be created as targeted standards.

ETL Testing Process

ETL Testing Process is similar to other testing processes and includes some stages.

They are:

• Identifying business requirements
• Test Planning
• Designing test cases and test data
• Test execution and bug reporting
• Summarizing reports
• Test closure

Types of ETL Testing

ETL Testing can be classified into the following categories according to the testing process that is being followed.

#1) Production Validation Testing:

It is also called Table balancing or product reconciliation. It is performed on data before or while being moved into the production system in the correct order.

#2) Source To Target Testing:

This type of ETL Testing is performed to validate the data values after data transformation.

#3) Application Upgrade:

It is used to check whether the data is extracted from an older application or new application or repository.

#4) Data Transformation Testing:

Multiple SQL queries are required to be run for each and every row to verify data transformation standards.

#5) Data Completeness Testing:

This type of testing is performed to verify if the expected data is loaded at the appropriate destination as per the predefined standards.

I would also like to compare ETL Testing with Database Testing but before that let us have a look at the types of ETL Testing with respect to database testing.

Given below are the Types of ETL Testing with respect to Database Testing:

#1) Constraint Testing:

Testers should test whether the data is mapped accurately from source to destination while checking for it testers need to focus on some key checks (constraints).

They are:

• NOT NULL
• UNIQUE
• Primary Key
• Foreign Key
• Check
• NULL
• Default

#2) Duplicate Check Testing:

Source and target tables contain a huge amount of data with frequently repeated values, in such case testers follow some database queries to find such duplication.

#3) Navigation Testing:

Navigation concerns with the GUI of an application. The user finds an application friendly when he gets easy and relevant navigation throughout the entire system. The tester must focus on avoiding irrelevant navigation from the user’s point of view.

#4) Initialization Testing:

Initialization Testing is performed to check the combination of hardware and software requirements along with the platform it is installed on.

#5) Attribute Check Testing:

This testing is performed to verify if all the attributes of both the source and target system are the same

From the above listing one may consider that ETL Testing is quite similar to Database Testing but the fact is ETL Testing is concerned with Data Warehouse Testing and not Database Testing.

There are several other facts due to which ETL Testing differs from Database Testing.

Let’s have a quick look at what they are:

• The primary goal of Database Testing is to check if the data follows the rules and standards of the data model, on the other hand, ETL Testing checks if data is moved or mapped as expected.
• Database Testing focuses on maintaining a primary key-foreign key relationship while ETL Testing verifies for data transformation as per the requirement or expectation and is the same at the source and target system.
• Database Testing recognizes missing data whereas ETL Testing determines duplicate data.
  
• Database Testing is used for data integration and ETL Testing for enterprise business intelligence reporting
• These are some major differences that make ETL Testing different from Database Testing.

Given below is the table showing the list of ETL Bugs:

How to Create Test Cases in ETL Testing

The primary goal of ETL testing is to ensure whether the extracted and transformed data is loaded accurately from the source to the destination system. ETL testing includes two documents, they are:

#1) ETL Mapping Sheets: This document contains information about the source & destination tables and their references. Mapping sheet provides help to create big SQL queries while performing ETL Testing.

#2) Database schema for Source and Destination table: It should be kept updated in the mapping sheet with database schema to perform data validation.

Final Thoughts

ETL Testing is not only a tester’s duty but it also involves developers, business analysts, database administrators (DBA), and even the users. The ETL Testing process has become vital as it is required to make strategic decisions at regular time intervals.

Suggested reading =>> Best ETL Automation Tools

ETL Testing is being considered as Enterprise Testing as it requires a good knowledge of SDLC, SQL queries, ETL procedures, etc.

Let us know if we have missed out on any of the tools on the above list and also suggest the ones that you use for ETL Testing in your daily routine.