ETL Testing
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Overview
Students Prerequisites
Course Curriculum
Duration of the Course
Instructor Profile
Overview
Extract, transform, load (ETL) is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s). The ETL process became a popular concept in the 1970s and is often used in data warehousing. ETL can transform dissimilar data sets into an unified structure.
Students Prerequisites
Basic knowledge of:
- SQL
- ETL fundamentals
- Data Warehouse concepts
- Query language
Course Curriculum
Module 1: Introduction to ETL and Data Warehousing
- What is ETL Testing?
- Importance of ETL Testing.
- Roles and responsibilities of ETL testers.
- Data Warehousing Basics
- What is a Data Warehouse (DWH)?
- OLTP vs. OLAP systems.
- ETL Process Overview (Extract, Transform, Load).
- ETL Testing Workflow
- Understanding requirements.
- Preparing test plans and strategies.
- Common ETL testing challenges.
Module 2: Data Warehouse Architecture
- Key Components
- Staging area, data warehouse, and data marts.
- Metadata repository.
- ETL tools (Informatica, Talend, SSIS).
- Schema Design
- Star Schema.
- Snowflake Schema.
- Types of Data Models
- Conceptual, logical, and physical models.
Module 3: Types of ETL Testing
- Data Validation Testing
- Data completeness and accuracy.
- Validating source and target data.
- Data Transformation Testing
- Business rules validation.
- Ensuring data transformation accuracy.
- Data Quality Testing
- Checking for duplicates, nulls, and data truncation.
- Ensuring data consistency and referential integrity.
- Performance and Scalability Testing
- Load testing on large datasets.
- Monitoring ETL performance and optimization.
- Regression Testing
- Testing the impact of changes on existing workflows.
- Incremental Data Load Testing
- Validating delta loads.
- Ensuring data synchronization.
Module 4: ETL Testing Process
- Understanding Requirements
- Gathering functional and non-functional requirements.
- Mapping documents and business rules.
- Creating Test Cases
- Writing source-to-target mapping test cases.
- Test case prioritization.
- Test Data Preparation
- Generating sample datasets.
- Using SQL for test data verification.
- Test Execution
- Validating data at each ETL stage (Extract, Transform, Load).
- Using automated and manual testing approaches.
Module 5: SQL for ETL Testing
- SQL Basics
- DML operations: SELECT, INSERT, UPDATE, DELETE.
- Aggregate functions and joins.
- Writing complex queries.
- Advanced SQL
- Subqueries and nested queries.
- Window functions.
- Temporary tables and CTEs (Common Table Expressions).
- Data Validation
- Querying source and target systems.
- Using SQL to compare datasets.
Module 6: ETL Tools Overview
- Popular ETL Tools
- Informatica PowerCenter.
- Talend Open Studio.
- Microsoft SQL Server Integration Services (SSIS).
- Apache NiFi.
- Hands-on
- Installing and configuring ETL tools.
- Building simple ETL workflows.
Module 7: Automation in ETL Testing
- ETL Test Automation Tools
- QuerySurge.
- Informatica Data Validation.
- Talend Testing.
- Selenium (for integration with UI testing).
- Creating Test Scripts
- Writing reusable test scripts.
- Scheduling and monitoring automated tests.
- Integrating with CI/CD
- Continuous testing in data pipelines.
- Using Jenkins or GitLab for ETL testing workflows.
Module 8: Data Integration and Big Data Testing
- Big Data ETL Testing
- Validating data in Hadoop/Hive/Spark environments.
- Testing large datasets for scalability.
- Data Integration Testing
- Verifying integration between multiple data sources.
- Testing APIs and data streams.
Module 9: Error Handling and Debugging
- ETL Error Types
- Common ETL errors (syntax, data mismatch, performance issues).
- Identifying and debugging issues in ETL pipelines.
- Logs and Reports
- Monitoring ETL logs for error tracking.
- Generating detailed test reports.
Module 10: Performance Optimization
- ETL Process Optimization
- Identifying bottlenecks in ETL workflows.
- Implementing parallelism and partitioning.
- Database Optimization
- Indexing, partitioning, and query tuning.
- Using database performance tools.
Module 11: Reporting and Documentation
- Test Summary Report
- Capturing test results and defects.
- Metrics for data validation and quality.
- Test Artifacts
- Documenting test plans, cases, and scripts.
- Maintaining source-to-target mapping documents.
Duration of the Course
30 Days (also available fast track course with short term
- Flexible Schedules
- Live Online Training
Instructor Profile
- Training by highly experienced and certified professionals
- No slideshow (PPT) training, fully Hand-on training
- Interactive session with interview QA’s
- Real-time projects scenarios & Certification Help
- 24 X 7 Support