ETL stands for extract transformation load testing. The term ETL testing refers to the method of loading data from a source system into a data warehouse. Data from the OLTP database is retrieved, converted into a meaningful schema, and then loaded into the data warehouse. Data warehouses can often provide data from non-OLTP systems such as text files, spreadsheets, or legacy systems in some situations.

  1. How to create ETL testing?
  2. The process can be broken down into eight stages

1. How to create ETL testing?

In the management field, ETL research can be used on a variety of platforms and databases. ETL testing check ensures the data is loaded correctly from the source to the destination if business transitions are correct. It entails data verification at several intermediate points between the source and the destination. When planning ETL testing cases, two texts are often used. There are the ETL Mapping Sheets and the Database Schema.

.   -ETL mapping sheets: These sheets include all of the information about the source and destination tables, including columns and lookup tables. Since ETL tests can involve complex SQL queries with several joins that need to be tested at various times, an ETL tester should have experience with SQL queries. When it comes to writing queries for data authentication, this move is extremely helpful.

.  DB Schema: It should be held on hand to double-check information in ETL mapping sheets.

The way ETL testing is done differs from how normal software testing is done. It’s almost impossible to disassemble the process and do unit testing (checking each piece of code). The goal here is to test the whole process from beginning to end. There is a lot of preparation involved, and a tester should have a thorough understanding of how this pipeline is set up and how to write complex test cases for it 1)Examine the paperwork surrounding the ETL operation. Developers and reviewers can review the ETL method documents – company criteria, technical specifications, and mapping specs – to learn how to do it.

2)Produce test results. Testers are often unable to use sensitive and restricted output data, necessitating the use of real-time synthetic data. Furthermore, in ETL, the accuracy of the input data is critical in determining the quality of the data migration process.

3)Make a list of test cases and scripts. Test cases explain what you want to check and how you’ll go about doing so. ETL test cases are usually written in SQL and consist of two queries: one to extract data from the source and another to extract data from the target storage.

4)Data completeness and accuracy: As source material will continue to change, we must ensure that data was extracted correctly, that no data was lost, and that no duplicates exist.

2. The process can be broken down into eight stages

1.Identify business requirements — Create a data model, identify business flow, and evaluate reporting requirements based on customer requirements. It’s critical to begin here so that the project’s reach is well described, registered, and completely comprehended by testers.

2. Validate data sources — Run a data count to make sure the table and column data types follow the data model’s requirements. Ensure the search keys are in order and that redundant data is removed. The composite report could be incorrect or misleading if not completed properly.

3.Design test cases — Build SQL scripts, design ETL mapping scenarios, and define transformational rules. Validating the mapping paper is therefore essential to ensure that it includes all of the necessary material.

4.Extract data from source systems — Run ETL tests following company needs. Make a summary detailing the kinds of glitches or flaws found during testing. Before moving on to Step 5, it’s critical to identify and replicate any bugs, then notify, patch, address, and close the bug report.

5.Apply transformation logic — Make sure that the data is converted to fit the target data warehouse’s schema. Check for data thresholds, alignment, and data flow validation. This means that the data format for each column and table fits the mapping text.

6.Load data into the goal warehouse — Before and after data is transferred from staging to the data warehouse, perform a log count search. Ascertain the invalid data is rejected and default values are allowed.

7.Check the configuration, alternatives, filters, and export capabilities of the overview study. This article informs decision-makers and stakeholders of the research process’s specifics and outcomes, as well as whether or not any steps were completed, i.e. “out of scope,” and why.

8.Closure of the test — This is the end of the test.

(Test scenario and cases)


  • validation-

It compares the arrangement of the source and target tables to the mapping sheets that refer to them. It checks whether the source and destination data types are the same or different. # It checks the data form length for both the source and destination. It checks whether the formats and types of data fields are defined or not. It checks a column’s name against the mapping doc.

  • Mapping Doc Validation-

It checks whether or not related information is included in the mapping doc. Every mapping doc is also checked for change-logs management.

  • Data Consistency Issues-

Even if the semantic meaning is the same, the data form and length in tables and fields can differ. It will look at credibility limits to see how they are being used properly

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional.