ETL Validator

DataOps ETL Validator is the most comprehensive Data Validation & ETL Testing Automation Tool

Datagaps ETL Validator

Datagaps ETL Validator earns Informatica’s Seal of Approval – Best of ETL Testing Tools

Benefits of End-to-End Data & ETL Validation using ETL Validator

Comprehensive ETL/ELT validation tool to automate the testing of data migration and data warehouse projects with easy to use Low-code, No-code Component based test creation and Drag & Drop User Interface.

Data Validation automation for Relational, Flat Files, XML, NoSQL, Cloud, and Big Data Sources.

100% Data Validation

Validate 100% of the data & not just a few rows


Cost Reduction of over 70%

Reduces cost by automating the test case execution

Reduced Time to Market

Reduces time to market by shortening the testing time

Key Features

Connects to over 40+ Data Sources

Supports access to all legacy and modern data sources such as Snowflake, Synapse and file based data sources such as AWS S3, ADLS and GCS.

No-code/Low-code based ETL transformation testing

Supports the ability to transform data using built-in processor components or use Python for more complex needs.

AI-powered Data Quality assessment

Compare billions of records between your source and target to validate your ETL project. Leverage AI for data quality testing - embrace data observability.

ETL Testing Automation

Comes with an inbuilt ETL engine capable of extracting & comparing billions of records from multiple data sources while executing test cases in parallel.

Visual Test Case Builder

Has a unique visual test case builder with drag & drop capabilities and a query builder that enables defining tests without manually typing in queries

Data Quality Testing

Provides built-in data quality test for common data quality checks with the ability to create complex data quality rules from scratch or by using our Query Builder to define rules to verify that the data conforms to your quality standards.

Data Profile Testing

Compare aggregate data such as counts, sums, patterns, and distinct counts of any data asset. Profiles can be compared to highlight aggregate deltas between a source and target to decide if further testing is required. ​Our unique AI capabilities can be enabled to view historical trends to find anomalies through observability.​

DB Metadata Testing

Helps audit changes to the metadata – data types, lengths, indexes. Simplifies comparison of database schema across environments.​

Baselining Capabilities

This powerful feature can be used for testing of incremental ETL, slowly changing dimensions and ETL regression testing.

End-to-End Data Testing

Supports data comparison across heterogeneous platforms, including the most popular relational databases, Snowflake, XML, JSON and other file or DB structures.

Flat File Testing

Define rules for automatically validating data in each column in the incoming file. Has a builtin file watcher that automatically detects new files and kicks off tests.

Enterprise Collaboration

Capability to assemble and schedule test plans. Email notification, web reporting and ALM integration enables sharing of test results.

Case Study

ETL Automation and Validation Process

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FAQ's about ETL Validator

What is ETL?

ETL stands for Extract, Transform, Load, a process that involves extracting data from various sources, transforming it to fit operational needs, and loading it into a target database or data warehouse.

What is ETL Testing?

ETL Testing involves verifying the accuracy, integrity, and completeness of data as it moves through the ETL process to ensure it meets business rules and requirements.

How to Automate ETL Testing?

Automating ETL Testing can be achieved using tools that automate data comparison, validation, and transformation tests, significantly speeding up the testing cycle and reducing manual labor.

How does ETL Validator automate ETL testing?

ETL Validator automates ETL testing by providing intuitive interfaces for creating test cases without extensive coding. It uses wizard-driven processes to automate data completeness, consistency checks, and transformation tests, streamlining the validation of ETL processes.

What methods does ETL Validator use for data validation?

ETL Validator employs methods like comparing data between source and target systems, testing for data validity, accuracy, and executing data quality rules to ensure the integrity of the data throughout the ETL process.

How is metadata validated in ETL testing with ETL Validator?

Metadata is validated by ensuring that data structures conform to predefined data dictionary standards. ETL Validator checks for consistency in metadata across various data sources and targets.

Strategies for validating large data volumes in ETL testing

For large data volumes, ETL Validator uses methods like sampling, partitioning data into manageable chunks, and employing high-performance comparison techniques, ensuring efficient and effective validation.

Checklist for ETL testing validation with ETL Validator

This includes verifying data completeness, consistency, transformation accuracy, referential integrity, performance metrics, and ensuring adherence to business rules and data quality standards. 

Understanding data validation in ETL testing

Data validation in ETL testing involves ensuring that the data being transferred and transformed is accurate, consistent, and aligns with the intended business logic and requirements.

What is schema validation in ETL testing and how it's done?

Schema validation ensures that the data structure (tables, columns, data types) matches the predefined schema. This is done by comparing the metadata against the schema definitions.

Types of validations crucial in the ETL process

Crucial validations include data completeness, transformation accuracy, data quality checks, schema validation, and performance validation to ensure the ETL process meets its intended objectives.

ETL Validator Resources

Try ETL Validator free for 14 days or contact us for a demo


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