ETL QA for Freshers

About Course
ETL QA (Extract, Transform, Load Quality Assurance) is the process of testing ETL workflows to ensure data is extracted, transformed, and loaded correctly into a target system, such as a data warehouse. It ensures data accuracy, integrity, and performance while verifying that business requirements are met.
Why ETL QA Skills Are In Demand
- Explosion of Data-Driven Decision-Making
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- As organizations increasingly rely on data to drive their decisions, data quality and accuracy are paramount.
- ETL processes (Extract, Transform, Load) are at the heart of how data is handled, and QA teams ensure that data is reliable and error-free.
- Growth of ELT (Extract, Load, Transform) Pipelines
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- With the rise of cloud data platforms (like Snowflake, Redshift, BigQuery), ETL is evolving into more complex ELT workflows.
- QA in these environments is crucial to ensure data transformation logic is validated and integrated across multiple sources.
- Data Governance and Compliance
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- Data governance is a big concern for organizations — ensuring data is accurate, consistent, and traceable.
- QA roles now also cover data integrity checks, audit trails, and data privacy, especially in regulated industries (finance, healthcare, etc.).
Course Content
ETL QA
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Introduction to ETL & Data Warehousing
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Understanding Data
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Introduction to Data Modelling
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ETL Testing Fundamentals
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Types of ETL Testing
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ETL Testing Process & Lifecycle
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Test Case Design & Execution
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Defect Management in ETL Testing
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SQL for ETL Testing
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ETL Tools & Hands-on Practice
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Data Validations
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Real-Time Projects
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Validate ETL Pipelines
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Snowflake Overview
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Interview Preparation