CI/CD for Data Pipelines Training Course

 CI/CD for Data Pipelines Training Course 

Introduction 

Continuous Integration and Continuous Deployment (CI/CD) for Data Pipelines Training Course provides comprehensive practical knowledge and technical skills for automating, testing, deploying, and maintaining modern data engineering workflows. The course explores DevOps principles, data pipeline automation, version control, infrastructure as code, automated testing, data quality assurance, workflow orchestration, and deployment strategies. Participants gain practical experience using modern technologies such as Git, GitHub Actions, GitLab CI/CD, Apache Airflow, Docker, Kubernetes, Terraform, and cloud-native data engineering platforms. Emphasis is placed on building scalable, secure, reliable, and production-ready data pipelines that support real-time analytics, enterprise data integration, business intelligence, and cloud data warehouse environments. 

The training further examines advanced CI/CD implementation strategies for data engineering teams, including automated pipeline validation, continuous testing, deployment monitoring, rollback mechanisms, configuration management, and release management. Through practical exercises and international industry case studies, participants explore how leading organizations automate data workflows, minimize deployment failures, improve data reliability, and accelerate analytics delivery. The programme also addresses emerging trends in DataOps, MLOps integration, cloud-native automation, GitOps, and AI-driven pipeline monitoring, equipping professionals to implement efficient and resilient data delivery practices across modern enterprise environments. 

Course Objectives 

• Understand CI/CD principles and their application in modern data engineering.
• Design automated and scalable data pipeline deployment workflows.
• Implement Git-based version control and collaborative development.
• Configure automated testing and data validation frameworks.
• Apply DevOps and DataOps practices to enterprise data environments.
• Integrate Apache Airflow with CI/CD automation tools.
• Implement containerization using Docker and Kubernetes.
• Automate infrastructure provisioning using Terraform.
• Develop continuous integration workflows using GitHub Actions and GitLab.
• Apply data quality engineering and automated pipeline monitoring.
• Implement secure deployment practices and secrets management.
• Establish automated rollback and release management strategies.
• Optimize production data pipelines for reliability and performance.

Organizational Benefits
 

• Reduced data pipeline deployment time and operational costs.
• Improved data quality through automated testing.
• Increased productivity across data engineering teams.
• Reduced production errors and deployment failures.
• Enhanced collaboration between development and operations teams.
• Improved scalability of enterprise data infrastructure.
• Stronger data governance and security compliance.
• Faster delivery of business intelligence and analytics.
• Improved operational resilience and system reliability.
• Standardized data engineering and deployment processes.

Target Audiences
 

• Data Engineers and Analytics Engineers.
• DevOps Engineers and Cloud Engineers.
• Data Architects and Enterprise Architects.
• Data Scientists and Machine Learning Engineers.
• Database Administrators and Data Warehouse Specialists.
• Software Developers and Platform Engineers.
• IT Managers and Technical Project Managers.
• Business Intelligence and Data Operations Professionals.

Course Duration: 5 days

Course Modules

Module 1: Foundations of CI/CD for Data Pipelines
 

• CI/CD concepts, DevOps principles, and DataOps fundamentals.
• Differences between software CI/CD and data pipeline CI/CD.
• Data pipeline lifecycle management and automation.
• Version control strategies for data engineering projects.
• Continuous delivery architecture and deployment workflows.
• Case study: Netflix's automated data infrastructure and continuous delivery practices.

Module 2: Version Control and Collaborative Data Engineering
 

• Git fundamentals, branching strategies, and repository management.
• GitHub and GitLab for collaborative pipeline development.
• Managing SQL scripts, Python code, and configuration files.
• Code reviews, merge requests, and automated validation.
• Managing pipeline releases across development environments.
• Case study: Google's engineering workflows and large-scale collaborative software development.

Module 3: Automated Testing and Data Quality Assurance
 

• Unit testing, integration testing, and regression testing.
• Data validation using Great Expectations and Pytest.
• Schema validation and data contract testing.
• Automated testing of ETL and ELT pipelines.
• Test-driven development for reliable data engineering.
• Case study: Airbnb's data quality and reliability engineering practices.

Module 4: CI/CD Pipeline Automation Tools
 

• Building workflows using GitHub Actions and GitLab CI/CD.
• Automating pipeline builds, tests, and deployments.
• Configuring workflow triggers and execution stages.
• Managing environment variables and deployment secrets.
• Implementing automated notifications and failure handling.
• Case study: Spotify's continuous integration and engineering automation practices.

Module 5: Data Pipeline Orchestration and Containerization
 

• Apache Airflow DAG development and deployment automation.
• Integrating Docker into data engineering workflows.
• Container orchestration using Kubernetes.
• Scheduling, monitoring, and managing pipeline execution.
• Automating dependencies and environment configurations.
• Case study: Uber's large-scale data processing and workflow orchestration practices.

Module 6: Cloud Infrastructure and Infrastructure as Code
 

• Infrastructure provisioning using Terraform.
• CI/CD integration with AWS, Microsoft Azure, and Google Cloud.
• Automated deployment of cloud data pipelines.
• Managing cloud storage, databases, and data warehouses.
• Infrastructure configuration, security, and scalability.
• Case study: Capital One's cloud-native engineering and infrastructure automation practices.

Module 7: Deployment Strategies, Monitoring, and Rollback
 

• Blue-green deployment and canary release strategies.
• Continuous monitoring and pipeline performance optimization.
• Automated rollback and recovery mechanisms.
• Logging, alerting, and incident management.
• Production release management and deployment governance.
• Case study: Amazon's automated deployment and production reliability practices.

Module 8: Advanced DataOps and CI/CD Implementation
 

• Integrating CI/CD with DataOps and MLOps environments.
• Implementing GitOps and automated configuration management.
• CI/CD security, access controls, and compliance.
• Developing an end-to-end automated data pipeline.
• Capstone implementation, testing, and production deployment.
• Case study: LinkedIn's data infrastructure and large-scale analytics engineering practices.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard CI/CD tools.
• Hands-on exercises involving automated data pipeline development.
• Real-world international case studies and group assignments.
• Collaborative projects, deployment simulations, and assessments.
• Capstone exercises involving end-to-end CI/CD implementation.

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.