In today’s data-driven economy, organizations rely on seamless, scalable, and high-quality data pipelines to power analytics, AI, and digital transformation initiatives. As data ecosystems grow in complexity, the need for continuous integration and continuous delivery (CI/CD) practices tailored specifically for data engineering has become essential. This hands-on course equips professionals with the advanced tools, strategies, and workflows to automate every stage of the data pipeline lifecycle — from ingestion and transformation to testing, deployment, and monitoring. Participants will master a wide range of modern DevOps tools including Git, Docker, Kubernetes, Airflow, Terraform, and CI/CD platforms like GitHub Actions and Jenkins — all applied through a data-centric lens. Through real-world use cases, participants will learn to embed automated testing, implement robust data validations, ensure version control for code and schemas, and deploy secure, scalable pipelines across hybrid and cloud environments. Whether you're building batch, streaming, or ML pipelines, this course ensures your workflows are resilient, production-ready, and fully automated for agility and reliability.

Duration: 10 Days

Target Audience
• Data Engineers
• DevOps Engineers
• Cloud Data Architects
• Data Platform Engineers
• ML Engineers
• DataOps Teams
• ETL Developers
• Analytics Engineers
• Database Administrators
• Technical Leads managing data platforms

Course Objectives
• Understand the principles of CI/CD in the context of data pipelines
• Design robust, automated workflows for building and testing data processes
• Implement data quality checks and validations at various pipeline stages
• Apply version control for code, data schemas, and transformation logic
• Set up pipeline monitoring, logging, and alerting systems
• Integrate containerized data pipelines into CI/CD pipelines using Docker and Kubernetes
• Automate infrastructure provisioning using IaC tools
• Deploy model and data artifacts using MLflow or similar tools
• Use GitOps methodologies for reproducible and secure deployments
• Enhance collaboration and reduce data pipeline delivery time

Course Modules

Module 1: Introduction to CI/CD for Data Systems
• Overview of CI/CD concepts
• Challenges unique to CI/CD for data pipelines
• CI/CD vs DataOps
• Toolchains and platforms overview
• Building a DevOps culture in data teams

Module 2: Data Pipeline Design Patterns
• Batch vs streaming architectures
• Layered data architecture principles
• Data modeling best practices
• Orchestrating modular pipeline components
• Handling late-arriving or malformed data

Module 3: Version Control for Data Engineering
• Git for data workflows
• Branching strategies for pipelines
• Tracking changes in ETL logic and SQL scripts
• Versioning data schemas
• Using DVC or LakeFS for data version control

Module 4: Infrastructure as Code (IaC)
• Introduction to Terraform and Pulumi
• Setting up cloud data platforms via IaC
• Managing secrets and configuration
• IaC deployment in CI/CD workflows
• Testing infrastructure changes

Module 5: Pipeline Orchestration Tools
• Apache Airflow basics
• Directed Acyclic Graphs (DAGs) and scheduling
• Dynamic pipelines and task dependencies
• Integrating Airflow with GitHub Actions or Jenkins
• Monitoring and alerting within orchestrators

Module 6: Continuous Integration for Data Pipelines
• Automating pipeline tests and linting
• Unit and integration tests for data flows
• Schema evolution tests
• GitHub Actions/Jenkins/Bitbucket pipelines
• Mocking databases and APIs in CI

Module 7: Continuous Delivery & Deployment
• Blue-green and canary deployments for data jobs
• Deployment to cloud (GCP, AWS, Azure)
• Parameterized and templated deployments
• Managing production environments
• Rollbacks and version pinning

Module 8: Data Quality & Validation Automation
• Data testing frameworks (Great Expectations, Deequ)
• Automating data profile checks
• Null, range, and distribution checks
• Validating schema conformance
• Setting up alerts for data test failures

Module 9: GitOps for Data Engineering
• GitOps workflows and principles
• Declarative deployment of pipelines
• GitOps tools: Argo CD, Flux
• Secrets and credentials management
• Auditing and rollback mechanisms

Module 10: Dockerizing Data Pipelines
• Docker basics for data engineers
• Creating reproducible pipeline containers
• Best practices for Dockerfile for ETL/ML jobs
• Managing container registry
• Running containers in CI environments

Module 11: Kubernetes for Data Workloads
• Basics of Kubernetes concepts
• Helm for pipeline deployment
• K8s operators for Spark, Flink, Airflow
• Resource limits and autoscaling
• Monitoring pods and logs

Module 12: Monitoring and Observability
• Pipeline observability stack overview
• Setting up Prometheus and Grafana
• Custom metrics for data pipelines
• Alerting on failures and delays
• Tracing lineage with OpenLineage

Module 13: ML & Analytics Artifacts Deployment
• CI/CD for machine learning pipelines
• Model tracking with MLflow
• Deploying Jupyter workflows to production
• Automating model promotion
• Managing notebooks as code

Module 14: Security in CI/CD for Data Pipelines
• Secrets management in CI/CD
• Access control for pipelines
• Secure coding practices
• Security scans in CI/CD flows
• Compliance logging and audit trails

Module 15: End-to-End Project Implementation
• Planning a full CI/CD pipeline for data platform
• Building CI/CD workflows from scratch
• Integrating all tooling covered
• Simulating real-world deployment scenarios
• Reviewing and refining for continuous improvement

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.