15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Data Quality Analysts
- Data Architects
- Data Scientists
- DevOps Engineers
- QA Engineers
- Anyone needing data testing and validation skills
Session Objectives
- Understand the fundamentals of data engineering testing and validation.
- Master data quality checks and validation techniques.
- Utilize automated testing frameworks for data pipelines.
- Implement data profiling and schema validation.
- Design and build data testing strategies for various data workflows.
- Optimize data testing for performance and reliability.
- Troubleshoot and address common issues in data testing implementations.
- Implement data governance and compliance testing.
- Understand how to handle large-scale data testing.
- Integrate data testing with CI/CD pipelines.
- Explore advanced data testing patterns (e.g., contract testing, data anomaly detection).
- Apply real world use cases for data engineering testing.
- Leverage data testing tools and frameworks for efficient validation.
About the Course
Eliminate silent data failures and safeguard downstream analytics with our Data Engineering Testing and Validation Training Course. Tailored for data engineers, quality analysts, and platform architects, this hands-on program equips you to build rigorous, automated testing suites across complex data pipelines. Guided by data quality experts, you will master schema enforcement, automated unit/integration testing for pipelines, continuous data assertions, and CI/CD validation frameworks—enabling your team to guarantee data accuracy, maintain structural integrity, and deploy production-ready data systems with confidence.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of data engineering testing and validation.
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Workshop 1.2:
Overview of data quality checks, pipeline validation, and automated testing.
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Workshop 1.3:
Setting up a data testing development environment.
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Workshop 1.4:
Introduction to data testing tools and frameworks.
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Workshop 1.5:
Best practices for data testing.
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Workshop 2.1:
Mastering data quality checks and validation techniques.
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Workshop 2.2:
Utilizing data profiling and schema validation.
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Workshop 2.3:
Implementing data consistency and integrity checks.
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Workshop 2.4:
Designing and building data quality validation pipelines.
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Workshop 2.5:
Best practices for data quality.
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Workshop 3.1:
Utilizing automated testing frameworks for data pipelines.
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Workshop 3.2:
Implementing unit testing and integration testing.
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Workshop 3.3:
Designing and building automated test suites.
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Workshop 3.4:
Optimizing testing for continuous integration.
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Workshop 3.5:
Best practices for automated testing.
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Workshop 4.1:
Implementing data profiling and schema validation.
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Workshop 4.2:
Utilizing data profiling tools and techniques.
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Workshop 4.3:
Designing and building schema validation pipelines.
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Workshop 4.4:
Optimizing validation for data integrity.
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Workshop 4.5:
Best practices for schema validation.
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Workshop 5.1:
Designing and building data testing strategies for various data workflows.
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Workshop 5.2:
Utilizing test-driven development (TDD) for data pipelines.
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Workshop 5.3:
Implementing testing strategies for ETL and data warehousing.
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Workshop 5.4:
Designing efficient data testing plans.
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Workshop 5.5:
Best practices for testing strategies.
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Workshop 6.1:
Optimizing data testing for performance and reliability.
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Workshop 6.2:
Utilizing load testing and stress testing.
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Workshop 6.3:
Implementing performance monitoring and analysis.
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Workshop 6.4:
Designing reliable testing environments.
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Workshop 6.5:
Best practices for performance testing.
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Workshop 7.1:
Troubleshooting and addressing common issues in data testing implementations.
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Workshop 7.2:
Analyzing test logs and error messages.
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Workshop 7.3:
Utilizing problem-solving techniques for resolution.
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Workshop 7.4:
Resolving common testing errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data governance and compliance testing.
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Workshop 8.2:
Utilizing data security and access control testing.
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Workshop 8.3:
Designing and building compliance testing frameworks.
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Workshop 8.4:
Optimizing testing for regulatory requirements.
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Workshop 8.5:
Best practices for governance.
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Workshop 9.1:
Integrating data testing with CI/CD pipelines.
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Workshop 9.2:
Utilizing CI/CD tools for automated testing.
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Workshop 9.3:
Implementing continuous data validation.
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Workshop 9.4:
Designing efficient testing integrations.
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Workshop 9.5:
Best practices for CI/CD integration.
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Workshop 10.1:
Understanding how to handle large-scale data testing.
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Workshop 10.2:
Utilizing distributed testing frameworks.
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Workshop 10.3:
Implementing data sampling and aggregation for testing.
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Workshop 10.4:
Designing scalable testing solutions.
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Workshop 10.5:
Best practices for large scale testing.
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Workshop 11.1:
Exploring advanced data testing patterns (contract testing, data anomaly detection).
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Workshop 11.2:
Utilizing contract testing for data integration.
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Workshop 11.3:
Implementing data anomaly detection for quality assurance.
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Workshop 11.4:
Designing and building advanced testing solutions.
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Workshop 11.5:
Optimizing advanced patterns for specific applications.
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Workshop 11.6:
Best practices for advanced patterns.
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Workshop 12.1:
Implementing data testing for e-commerce data pipelines.
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Workshop 12.2:
Utilizing data validation for financial transaction systems.
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Workshop 12.3:
Implementing data quality checks for healthcare data.
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Workshop 12.4:
Utilizing data testing for IoT data streams.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing data testing tools and frameworks (Great Expectations, dbt).
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Workshop 13.2:
Implementing data validation with specific tools.
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Workshop 13.3:
Designing and building automated testing scripts.
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Workshop 13.4:
Optimizing tool usage for efficient validation.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing test monitoring and metrics.
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Workshop 14.2:
Utilizing test coverage and performance metrics.
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Workshop 14.3:
Designing and building test dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in data engineering testing.
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Workshop 15.2:
Utilizing AI for automated test generation.
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Workshop 15.3:
Implementing data testing in cloud-native environments.
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Workshop 15.4:
Best practices for future applications.
