15 Topics
10 Days
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
Fortify your data infrastructure with our Data Engineering Testing and Validation Training Course. This program is designed to equip you with the essential skills to ensure data quality through testing, enabling you to build reliable and trustworthy data systems. In today's data-driven world, mastering data testing and validation is crucial for organizations seeking to maintain data integrity and accuracy. Our data testing training course offers hands-on experience and expert guidance, empowering you to implement robust testing strategies for diverse data engineering workflows.
This quality data assurance training delves into the core concepts of data engineering testing, covering topics such as data quality checks, pipeline validation, and automated testing frameworks. You'll gain expertise in using industry-standard tools and techniques to ensure data quality through testing, meeting the demands of modern data-intensive environments. Whether you're a data engineer, data quality analyst, or data architect, this Data Engineering Testing and Validation course will empower you to build and maintain high-quality data solutions.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of data engineering testing and validation.
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Subtopic 1.2:
Overview of data quality checks, pipeline validation, and automated testing.
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Subtopic 1.3:
Setting up a data testing development environment.
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Subtopic 1.4:
Introduction to data testing tools and frameworks.
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Subtopic 1.5:
Best practices for data testing.
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Subtopic 2.1:
Mastering data quality checks and validation techniques.
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Subtopic 2.2:
Utilizing data profiling and schema validation.
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Subtopic 2.3:
Implementing data consistency and integrity checks.
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Subtopic 2.4:
Designing and building data quality validation pipelines.
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Subtopic 2.5:
Best practices for data quality.
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Subtopic 3.1:
Utilizing automated testing frameworks for data pipelines.
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Subtopic 3.2:
Implementing unit testing and integration testing.
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Subtopic 3.3:
Designing and building automated test suites.
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Subtopic 3.4:
Optimizing testing for continuous integration.
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Subtopic 3.5:
Best practices for automated testing.
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Subtopic 4.1:
Implementing data profiling and schema validation.
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Subtopic 4.2:
Utilizing data profiling tools and techniques.
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Subtopic 4.3:
Designing and building schema validation pipelines.
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Subtopic 4.4:
Optimizing validation for data integrity.
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Subtopic 4.5:
Best practices for schema validation.
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Subtopic 5.1:
Designing and building data testing strategies for various data workflows.
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Subtopic 5.2:
Utilizing test-driven development (TDD) for data pipelines.
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Subtopic 5.3:
Implementing testing strategies for ETL and data warehousing.
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Subtopic 5.4:
Designing efficient data testing plans.
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Subtopic 5.5:
Best practices for testing strategies.
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Subtopic 6.1:
Optimizing data testing for performance and reliability.
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Subtopic 6.2:
Utilizing load testing and stress testing.
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Subtopic 6.3:
Implementing performance monitoring and analysis.
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Subtopic 6.4:
Designing reliable testing environments.
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Subtopic 6.5:
Best practices for performance testing.
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Subtopic 7.1:
Troubleshooting and addressing common issues in data testing implementations.
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Subtopic 7.2:
Analyzing test logs and error messages.
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Subtopic 7.3:
Utilizing problem-solving techniques for resolution.
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Subtopic 7.4:
Resolving common testing errors.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing data governance and compliance testing.
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Subtopic 8.2:
Utilizing data security and access control testing.
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Subtopic 8.3:
Designing and building compliance testing frameworks.
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Subtopic 8.4:
Optimizing testing for regulatory requirements.
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Subtopic 8.5:
Best practices for governance.
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Subtopic 9.1:
Integrating data testing with CI/CD pipelines.
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Subtopic 9.2:
Utilizing CI/CD tools for automated testing.
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Subtopic 9.3:
Implementing continuous data validation.
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Subtopic 9.4:
Designing efficient testing integrations.
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Subtopic 9.5:
Best practices for CI/CD integration.
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Subtopic 10.1:
Understanding how to handle large-scale data testing.
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Subtopic 10.2:
Utilizing distributed testing frameworks.
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Subtopic 10.3:
Implementing data sampling and aggregation for testing.
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Subtopic 10.4:
Designing scalable testing solutions.
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Subtopic 10.5:
Best practices for large scale testing.
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Subtopic 11.1:
Exploring advanced data testing patterns (contract testing, data anomaly detection).
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Subtopic 11.2:
Utilizing contract testing for data integration.
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Subtopic 11.3:
Implementing data anomaly detection for quality assurance.
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Subtopic 11.4:
Designing and building advanced testing solutions.
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Subtopic 11.5:
Optimizing advanced patterns for specific applications.
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Subtopic 11.6:
Best practices for advanced patterns.
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Subtopic 12.1:
Implementing data testing for e-commerce data pipelines.
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Subtopic 12.2:
Utilizing data validation for financial transaction systems.
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Subtopic 12.3:
Implementing data quality checks for healthcare data.
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Subtopic 12.4:
Utilizing data testing for IoT data streams.
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Subtopic 12.5:
Best practices for real-world applications.
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Subtopic 13.1:
Utilizing data testing tools and frameworks (Great Expectations, dbt).
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Subtopic 13.2:
Implementing data validation with specific tools.
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Subtopic 13.3:
Designing and building automated testing scripts.
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Subtopic 13.4:
Optimizing tool usage for efficient validation.
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Subtopic 13.5:
Best practices for tool implementation.
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Subtopic 14.1:
Implementing test monitoring and metrics.
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Subtopic 14.2:
Utilizing test coverage and performance metrics.
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Subtopic 14.3:
Designing and building test dashboards.
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Subtopic 14.4:
Optimizing monitoring for real-time insights.
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Subtopic 14.5:
Best practices for monitoring.
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Subtopic 15.1:
Emerging trends in data engineering testing.
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Subtopic 15.2:
Utilizing AI for automated test generation.
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Subtopic 15.3:
Implementing data testing in cloud-native environments.
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Subtopic 15.4:
Best practices for future applications.
