Automated Testing for Data Engineering Training Course

 Automated Testing for Data Engineering Training Course 

Introduction 

Automated Testing for Data Engineering Training Course is designed to equip data professionals with the technical knowledge and practical skills required to implement automated testing strategies across modern data engineering environments. As organizations increasingly adopt cloud-native data platforms, big data architectures, data warehouses, data lakes, and real-time data pipelines, ensuring data quality, accuracy, reliability, and consistency has become a critical business priority. This course explores automated data testing, data pipeline validation, unit testing, integration testing, regression testing, end-to-end testing, and continuous testing using modern tools and frameworks. Participants will develop expertise in testing data transformations, validating Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) workflows, detecting data anomalies, and implementing automated quality assurance within DataOps and DevOps environments. 

The training also focuses on modern data engineering testing frameworks, test-driven development, automated validation pipelines, and continuous integration and continuous delivery (CI/CD) practices. Through practical exercises and global industry case studies, participants will learn how leading organizations leverage automation to minimize data defects, improve pipeline performance, strengthen governance, and accelerate analytics delivery. The course covers tools such as Great Expectations, dbt tests, Apache Airflow, PyTest, Soda, and Databricks, enabling participants to build scalable, maintainable, and reliable automated testing solutions. By the end of the programme, participants will be prepared to establish enterprise-grade data testing strategies that support artificial intelligence, machine learning, business intelligence, cloud data engineering, and modern analytics ecosystems. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand automated testing principles and modern data engineering quality assurance frameworks.
2. Design comprehensive automated data testing strategies for enterprise data pipelines.
3. Implement unit testing for data transformations and processing logic.
4. Apply integration testing across complex ETL and ELT workflows.
5. Perform automated data validation using Great Expectations and Soda.
6. Develop test-driven data engineering practices using Python and PyTest.
7. Implement dbt testing for data models and warehouse transformations.
8. Automate regression testing and end-to-end data pipeline validation.
9. Integrate automated testing into CI/CD and DataOps workflows.
10. Detect data anomalies using automated monitoring and quality checks.
11. Improve cloud data pipeline reliability through continuous testing.
12. Apply data governance, observability, and quality assurance standards.
13. Build scalable automated testing frameworks for modern data platforms.

Organizational Benefits
 

1. Improved enterprise data quality and reliability.
2. Reduced operational costs associated with data defects.
3. Faster data pipeline development and deployment.
4. Enhanced business intelligence and analytics accuracy.
5. Improved regulatory compliance and data governance.
6. Reduced manual testing workloads through automation.
7. Increased productivity across data engineering teams.
8. Early detection of pipeline failures and data anomalies.
9. Greater confidence in artificial intelligence and machine learning datasets.
10. Improved scalability and resilience of enterprise data infrastructure.

Target Audiences
 

1. Data Engineers and Senior Data Engineers.
2. Data Architects and Solutions Architects.
3. Data Analysts and Analytics Engineers.
4. Database Administrators and Developers.
5. DevOps and DataOps Engineers.
6. Cloud Data Platform Specialists.
7. Data Quality and Governance Professionals.
8. Business Intelligence and ETL Developers.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Automated Testing for Data Engineering
 

• Principles of software testing and data quality assurance.
• Automated testing lifecycle and testing pyramid.
• Data engineering testing requirements and frameworks.
• Introduction to unit, integration, and regression testing.
• Testing challenges in cloud-native data architectures.
• Case study: Netflix data quality automation and large-scale data pipeline reliability.

Module 2: Python-Based Automated Data Testing
 

• Python testing fundamentals for data engineering.
• Writing automated unit tests using PyTest.
• DataFrame validation using Pandas and NumPy.
• Test fixtures, parameterization, and mocking techniques.
• Automating data transformation and validation scripts.
• Case study: Spotify-style automated validation of large-scale music recommendation datasets.

Module 3: Automated Testing with Great Expectations and Soda
 

• Introduction to Great Expectations testing frameworks.
• Creating data expectations and validation rules.
• Implementing automated data quality checks.
• Configuring Soda scans and data quality monitoring.
• Generating validation reports and managing test failures.
• Case study: Enterprise banking data validation using automated quality rules.

Module 4: dbt Testing and Data Warehouse Validation
 

• Introduction to dbt testing architecture.
• Implementing schema and data tests.
• Developing custom SQL-based validation tests.
• Testing incremental models and transformation dependencies.
• Integrating dbt tests into automated deployment workflows.
• Case study: Modern retail data warehouse validation using dbt testing practices.

Module 5: ETL, ELT and Data Pipeline Integration Testing
 

• Automated testing of Extract, Transform, Load pipelines.
• Integration testing across databases and cloud platforms.
• Validating source-to-target data consistency.
• Testing APIs, batch processing, and streaming pipelines.
• Managing dependencies and pipeline failure scenarios.
• Case study: Global e-commerce pipeline integration and transaction reconciliation.

Module 6: Regression Testing and End-to-End Automation
 

• Designing automated regression testing strategies.
• Implementing end-to-end data pipeline tests.
• Validating historical data and schema changes.
• Testing data migrations and transformation updates.
• Automating test execution and failure reporting.
• Case study: Financial services regression testing for enterprise reporting systems.

Module 7: CI/CD, DataOps and Continuous Testing
 

• Integrating automated testing into CI/CD pipelines.
• Implementing Git-based testing and version control.
• Automating tests using GitHub Actions and Jenkins.
• Deploying quality gates and continuous validation.
• Managing test environments and automated release processes.
• Case study: Microsoft Azure-based continuous testing for enterprise data platforms.

Module 8: Data Observability, Monitoring and Capstone Project
 

• Implementing automated data observability frameworks.
• Monitoring data freshness, volume, and distribution.
• Detecting anomalies and investigating pipeline failures.
• Developing automated alerts and quality dashboards.
• Building an enterprise automated testing framework.
• Case study: Airbnb-inspired data reliability monitoring and automated anomaly detection.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical exercises using Python, PyTest, dbt, and Great Expectations.
• Hands-on automated data testing and pipeline development.
• Group discussions and international industry case studies.
• Real-world simulations involving data quality failures.
• Collaborative capstone projects and technical assessments.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.