ETL testing is performed before data is moved into a production data warehouse system. It is also known as table balancing or production reconciliation. The main goal of ETL testing is to identify and mitigate data defects.
Using tools is imperative to conduct ETL testing considering the volume of data. Here is a list of top 5 ETL Testing Tools with Key features and download links :
ETL Testing Tools: Top Picks
iCEDQ is a DataOps platform for Data Testing and Monitoring. Its rules-based auditing approach enables organizations to automate ETL Testing, Cloud Data Migration Testing, and Production Data Monitoring.
- Highly scalable in-memory engine to evaluate billions of records
- Create rules to test transformations, duplicates, schema, Type II Dimension, etc.
- Connect to Databases, Files, API, BI Reports. 50+ connectors are available.
- Enables DataOps by allowing users to integrate with any Scheduling, GIT, or DevOps tool.
- Integrate with different enterprise products like Slack, Jira, ServiceNow, Alation, Manta.
- Single Sign-On, Advanced RBAC, and Encryption features
- Use the inbuilt Dashboard or enterprise reporting tools like Tableau, Power BI, and Qlik to generate reports for more insight.
- Deploy it anywhere, On-Prem, AWS, Azure, or GCP.
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.
- Users can perform queries on any data source, explore metadata, analyze data, discover data by data profiling, prepare by performing transformations and cleansing, and snapshot data to assist with data reconciliation, business rule and transformations validation.
- Bulk comparison capability to facilitate technical data reconciliation across data landscape
- Alerting and notification capabilities from emails through creation of defect/incident management tickets.
- Can be used for testing analytics/BI tools like Tableau, Power BI, etc.
- Two-way integration with CICD tools (Jenkins, Jira, etc.) for DevOps / DataOps enablement.
DataQ is the next generation’s AI-based ETL/ELT Testing Platform and the first ever to support hybrid/multi-cloud. DataQ is built ground up to scale and handle structured/semi-structured data.
- AI-based automated data validation rules with the ability to augment business-specific rules using a drag-and-drop interface (for non-technical users) as well as SQL (for technical users).
- Out-of-the-box Integration with 30+ data sources & destinations (including Oracle & Snowflake).
- Optional native integration for Hadoop-based processing of concurrent jobs (to speed up the execution of validation jobs in case of large datasets).
- Capability to process any size of data (organizations have used DataQ to validate billions of records at a time).
- Multiple deployment options (SaaS, on-premises, VPC, etc.).
- Auto-detection of key columns across the source and destination datasets.
- Powerful integration options, including REST APIs, YAML, SDKs as well as a graphical user interface.
- Optional, AI-based optimization of compute resources utilization to keep the cost of validation jobs low (automated provisioning & de-provisioning of cloud instances while running validation jobs on Hadoop).
4) BiG EVAL
BiG EVAL automates testing tasks in data-oriented projects (data analytics, data warehouses, ETL/ELT, data migrations and more). The unique automation approach and the simple user interface guarantee same-day-benefits.
- Autopilot testing, driven by meta data, leverages the test coverage massively.
- Fully customizable test algorithms, rules and test behavior.
- Gallery with hundreds of best practices test case templates ready to be used by you.
- Deep insight analysis with clear dashboards and alerting processes.
- Integration with hundreds of tools (e.g. Jira, ServiceNow, Slack, Teams …).
- Embeddable into DataOps processes and DevOps CI/CD flows.
- Hundreds of connectors to any kind of data (RDBMS, APIs, Flatfiles, Business applications, SaaS …).
QuerySurge is ETL testing solution developed by RTTS. It is built specifically to automate the testing of Data Warehouses & Big Data. It ensures that the data extracted from data sources remains intact in the target systems as well.
- Improve data quality & data governance
- Accelerate your data delivery cycles
- Helps to automate manual testing effort
- Provide testing across the different platform like Oracle, Teradata, IBM, Amazon, Cloudera, etc.
- It speeds up testing process up to 1,000 x and also providing up to 100% data coverage
- It integrates an out-of-the-box DevOps solution for most Build, ETL & QA management software
- Deliver shareable, automated email reports and data health dashboards
6) Informatica Data Validation:
Informatica Data Validation is a popular ETL tool. It is one of the best data testing tools which integrates with the PowerCenter Repository and Integration Services. It enables developers and business analysts to create rules to test the mapped data.
- Informatica Data Validation provides complete solution for data validation along with data integrity
- Reduces programming efforts because of intuitive user interface and built-in operators
- Identifies and prevents data issues and provides greater business productivity
- It has Wizards to create test Query without the user’s need to write SQL
- This automated ETL testing tool also offers design Library and reusable Query Snippets
- It can analyze millions of rows and columns of data in minutes
- It helps to compare data from source files and data stores to the target Data Warehouse
- It can produce informative reports, updates, and auto-email results
Download link: https://www.informatica.com/in/etl-testing.html
QualiDi enables clients to reduce costs, achieve higher ROIs and accelerate time to market. This ETL tool automates every aspect of the testing lifecycle. It is one of the best ETL automation tools that enables clients to reduce costs, achieve higher ROIs and accelerate time to market
- Finding bad and non-compliant data
- Data integration testing
- Testing across platforms
- Managing test cycles through dashboards and reports
- Meaningful auto test data generation using constraints and referential integrity
- Automated test case generation for direct mappings
- Central test case repository allows test schedules for regression testing
- Test execution maintained in batches for regression and retesting
- Test execution results in dashboards and reports available at a click
- Built-in defect tracking and monitoring, interfacing with a third-party defect tracking tool
Download link: https://www.bitwiseglobal.com/in/
8) ETL Validator:
Datagaps ETL Validator is a Data warehouse testing tool. It is one of the best data migration testing tools that simplifies the testing of Data Integration, Data Warehouse, and Data Migration projects. It has an inbuilt ETL engine capable of comparing millions of records.
- Define rules for automatically validating data in every column in the incoming file
- Compare profile of target and source data
- Simplifies comparison of database Schema across environments
- Capability to assemble and schedule test plan
- Baseline and compare data to find differences
- Analyzes data across multiple systems
- It is one of the best data warehouse testing tools that allows web-based reporting
- REST API and continuous integration features.
- It offers Data Quality and Data Integration Testing
- Wizard Based Test Creation
- Enterprise Collaboration
- Container based security
- It provides scheduling Capabilities to the users
- It provides benchmarking Capabilities
- Reduce costs associated with testing data projects
❓ What is ETL Testing?
ETL testing is a software testing process which is done to ensure that the data that has been loaded from a source to the destination after business transformation is accurate. It also involves the verification of data at various middle stages that are being used between source and destination. ETL stands for Extract-Transform-Load.
💻 Which are the Best ETL Testing Tools?
Here are some of the best ETL testing tools:
💥 Why is ETL Testing required?
While data transportation from extraction to loading, there are some chances of occurring some human or system errors that can result in poor information transferred through the whole system. ETL testing helps to ensure such errors do not occur and eliminates such defects which can harm the whole system.
✅ How to do ETL Testing?
Below is a process on how to do ETL testing:
- Step 1) Identifying data sources and requirements
- Step 2) Data acquisition
- Step 3) Implement business logics and dimensional Modelling
- Step 4) Build and populate data
- Step 5) Build Reports