The ETL Tool Testing Certified Course is designed to provide learners with comprehensive knowledge and practical skills in testing Extract, Transform, and Load (ETL) processes, which are critical in data warehousing and business intelligence systems. This course covers the fundamentals of ETL, the testing lifecycle, data validation techniques, and defect reporting, while also focusing on real-time scenarios such as source-to-target data verification, performance testing, and data integrity checks. With hands-on exposure to leading ETL and data warehouse tools, participants will gain the ability to ensure data accuracy, consistency, and reliability across complex data systems. By the end of the course, learners will be equipped to handle ETL testing projects confidently and meet the growing industry demand for skilled ETL testers.
Module 1: Introduction to ETL & Data Warehousing Basics of ETL (Extract, Transform, Load) Importance of ETL testing in data-driven industries Data warehouse architecture, OLTP vs OLAP Schema types: Star, Snowflake, and Galaxy.
Module 2: ETL Testing Fundamentals ETL testing lifecycle & process flow Types of ETL testing (data completeness, data quality, regression, performance, etc.) Challenges in ETL testing Real-world case studies.
Module 3: SQL for ETL Testing Writing queries for data validation Joins, aggregations, subqueries, window functions Source-to-target data verification Advanced SQL techniques for testers.
Module 4: Data Mapping & Data Validation Source-to-target mapping document (STTM) Data validation techniques Data profiling & data quality checks Handling nulls, duplicates, and transformations.
Module 5: ETL Testing Tools Introduction to Informatica, Talend, SSIS, IBM DataStage Testing approaches in different tools Hands-on exercises with one ETL tool Tool comparison & industry use cases.
Module 6: Cloud ETL & Data Warehouse Testing Cloud-based ETL concepts Testing with Snowflake, AWS Glue, and Azure Data Factory Validating cloud-based data pipelines Security & compliance testing in cloud ETL.
Module 7: Big Data ETL Testing Hadoop ecosystem overview (HDFS, Hive, Spark) ETL testing in big data environments Hive queries for data validation Challenges in big data ETL testing.
Module 8: Test Automation in ETL Need for automation in ETL testing Automation using Python, Selenium, and SQL scripts Frameworks for ETL test automation Case studies of automation projects.
Module 9: Performance & Scalability Testing Performance testing concepts in ETL Using Apache JMeter for ETL job performance testing Bottleneck identification in ETL pipelines Scalability & load testing.
Module 10: Live Project & Industry Practices End-to-end ETL testing project (from raw source to BI dashboard) Test case design & execution Defect tracking with JIRA/ALM Industry best practices & interview preparation.
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Email: coursedivine@gmail.com
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