Organizations in order to perform meaningful business analysis gather data from multiple sources. Popular Business Intelligence (BI) tools can be used for processing large amounts of data, so that valuable business insights can be obtained. To carry out this process meticulously, ETL (Extract, Transform, Load) testing is required. In this article, you will know about what is ETL testing process and the various ETL testing tools.
What is ETL testing?
ETL (Extract, Transform, Load) is a process where data is extracted from source systems, then based on business requirements the data is transformed and finally, the transformed data is loaded into the destination database. ETL processes play a key role in data-related projects such as MDM, Big Data, and Data Migration.
ETL testing refers to the process of qualifying, verifying, and validating data while preventing data loss and duplicate records. This testing method ensures that the data that is being transferred from heterogeneous sources to the central warehouse happens in line with strict adherence to transformation rules and in compliance with all the validity checks.
ETL Testing process:
Following are the eight steps involved in the testing process:
Ascertaining business requirements: Assess reporting needs, define the business flow and design the data model based on client expectations. The project scope should be clearly documented, defined, and understood by testers.
Data sources need to be validated: A data count check needs to be performed and then verify whether the column data type and table meet the specifications of the data model. Check keys should be in the proper place and duplicate data needs to be removed. If done incorrectly, then the aggregate report could be misleading or inaccurate.
Initiate designing test cases: define transformational rules, create SQL scripts, and design ETL mapping scenarios. The mapping document needs to be validated as well, in order to ensure that it contains all the information.
Data extraction from source systems: ETL tests need to be executed as per business requirements. The types of bugs and defects need to be identified that have been encountered during testing. Defects need to be detected and resolved, bugs need to be fixed, and then finally the bug report has to be closed before moving on to the next step.
Apply the logic of transformation: Make sure the data is transformed so that the schema of a target data warehouse is properly matched. Check alignment, data threshold, and validate data flow. This ensures that the mapping document is matched with the data type for each column and table.
Data needs to be loaded into the target warehouse: A record count check needs to be performed before and after the data is moved from staging to the data warehouse. The invalid data needs to be confirmed that it is rejected and that the default values have been accepted.
Prepare an in-depth report: Verify the filters, options, layout and export functionality of the summary report. This report will let the stakeholders and decision-makers know the results and details of the testing process.
Following are some of the best ETL testing tools:
Informatica data validation: This tool integrates integration services and repositories with a power center. It allows analysts and developers to develop guidelines so that the mapped information can be tested. This tool offers data integrity solutions and full data validation. Information problems are identified and avoided.
QualiDi: Every element of the test cycle is automated testing through this tool. It allows customers to increase their ROI, decrease expenses and speed up the market time. Based on requirements, data traceability is provided to a target database. Faster project delivery and functionality are supported.
QuerySurge: It is an RTTS-developed solution for ETL testing. It is designed for the automation of big data testing and data storage. The quality of data governance and data is improved by this tool. Data transmission cycles are carried out at a faster pace. This tool can provide testing on various platforms such as IBM, Teradata, Oracle, Amazon and Cloudera.
SSISTester: The monitoring of test executions is allowed by the UI of SSISTester in a real-time scenario. Tests can be implemented easily as it provides an intuitive way to access packages, database resources, etc. This tool has a built-in project template. Parameters of tests like the errors in the test, currently executed tests are provided by SSISTester. Test results can be easily saved and sent.
Data Gaps ETL Validator: This tool is for the data warehouse. The testing of projects is simplified for data warehouse, data migration, and data integration. Millions of documents can be compared through an embedded ETL engine present in this tool.
Conclusion: If you are looking forward to in-depth insights on ETL testing from a real-time industry viewpoint, then do get connected with a premium software testing services company that will provide you strategic solutions of deep value.