What are the ETL testing processes and tools?

Organizations that perform meaningful business analytics collect data from multiple sources. You can use popular business intelligence (BI) tools to process large amounts of data, giving you valuable business insights. ETL (extract, transform, load) testing is required to perform this process correctly. In this article, you will learn more about the ETL testing process and various ETL testing tools.

What is an ETL test?

ETL (Extract, Transform, Load) is the process of extracting data from a bronze system, transforming the data according to business requirements, and finally loading the transformed data into the lot’s database. The ETL process plays an important role in data-related projects such as MDM, big data, and data migration.

ETL testing is the process of accrediting, validating, and validating data while avoiding data loss and duplicate records. This test method ensures that data transferred from heterogeneous sources to the central warehouse is performed according to strict compliance with conversion rules and all validity checks.


ETL test process:

Below are eight steps involved in the testing process.

Determining Business Requirements-Evaluate reporting needs, define business flows, and design data models based on customer expectations. The evaluator needs to clearly document, define, and understand the scope of the project.

You need to check the data source. You need to perform a data count to see if the column and table data types meet the specifications of your data model. You need to put the authentication key in the right place and remove the duplicate data. If not done correctly, compound reports can be misleading or inaccurate.

Start designing your test case. Define transformation rules, create SQL scripts, and design ETL mapping scenarios. You should also verify the mapping documentation to make sure it contains all the information.

Extracting data from a bronze system-You need to run ETL tests based on your business requirements. You need to identify the types of failures and defects found during the test. You must find and resolve the flaws, fix the error, and finally close the error report before proceeding to the next step.

Apply transformation logic: Make sure the data is transformed to match the scheme of the target data store correctly. Check the alignment, data threshold, and check the data flow. This ensures that the mapping document matches the data type of each column and table.

Data must be uploaded to the destination store-record counts must be performed before and after the data is moved from storage to data storage. You need to make sure that invalid data is rejected and the default is accepted.

Compile the detailed report. Review the filters, options, layouts, and execution capabilities of the summary report. This report gives stakeholders and decision makers the results and details of the testing process.

Here are some of the best ETL test tools:

Informatica Data Validation: This tool integrates repositories and integration services with power supplies. This allows analysts and developers to create guidelines so that they can test the mapped information. This tool provides a complete data validation and data integrity solution. Information issues are identified and avoided.

QualiDi: Each element of the test cycle is an automated test with this tool. This allows customers to improve ROI, reduce costs and reduce marketing time. Data traceability is provided to the target database, depending on your requirements. Supports faster delivery and features of your project.

QuerySurge: A solution developed by RTTS for ETL testing. Designed to automate big data testing and data storage. This tool improves data quality and data control. The data transmission cycle runs faster. This tool can be tested on a variety of platforms such as IBM, Teradata, Oracle, Amazon and Cloudera.

SSISTester: The SSISTester user interface allows you to monitor test execution in real-time scenarios. Testing is easy to implement with intuitive access to packages, database resources, and more. This tool has a built-in project template. Test parameters such as test errors and currently running tests are provided by SSISTester. You can easily save and send the test results.

Data Gap ETL Validator-This tool is for data warehousing. Project testing has been simplified for data storage, data migration, and data integration. The built-in ETL engine in this tool allows you to compare millions of documents.

This means that if you need more information about ETL testing from an industry real-time perspective, you can contact a good software testing service company that offers a valuable strategic solution.