ETL & Data Testing

Freshers aiming for Data Tester, ETL Tester or Data QA roles will find the course follows the work those jobs involve day to day. It also suits manual testers moving towards data work and junior analysts who are asked to validate reports. Basic SQL helps; for interview-style query practice see SQL interview mastery. Learners who want to build pipelines rather than test them can follow up with data engineering with ETL and PySpark.

Each lesson is a short reading card with narration and diagrams, such as a picture of rows flowing from source to staging to target, or a view of an anti-join that finds records missing from the target table. After the card, you answer MCQ practice items one at a time. Every answer comes with an explanation, so a wrong choice shows you why the correct option works. The course runs guided or self-paced, so you can follow the curriculum in order or move at your own pace. It is free with Abekus Pro, and otherwise it is paid in credits.

Udemy and Coursera host long video courses on ETL testing, often recorded as one tester working through one tool. That format suits learners who want to see a full test cycle. This course asks the questions that come up in real data testing: which join finds missing rows, whether a control total or a row-by-row check fits, how a slowly changing dimension should look after a load, or how severe a duplicate is. MCQ practice in short sessions suits learners who want to answer these quickly in interviews and on the job.

Set up two small tables in any database, one as source and one as target, and break the target on purpose: drop rows, duplicate keys, change a date format. Then write the SQL check that catches each problem. Answer each MCQ before reading the explanation. Revisit Joins for Comparison and Reconciliation Design after a few days, because they are the core of most data testing interviews. Finish by writing one defect report with query evidence for a mismatch you created.