CI/CD for Analytics Engineering Training Course

 CI/CD for Analytics Engineering Training Course 

Introduction 

CI/CD for Analytics Engineering Training Course is designed to equip professionals with advanced knowledge and practical skills in Continuous Integration and Continuous Delivery (CI/CD), modern analytics engineering, automated data workflows, and enterprise data pipeline management. The course explores how organizations can implement DevOps principles, version control, automated testing, data transformation, and deployment automation to improve the reliability, scalability, and performance of modern data platforms. Participants will gain practical experience with Git, GitHub Actions, GitLab CI/CD, dbt, Apache Airflow, Docker, and cloud-based analytics environments to streamline data development and deployment processes. 

The training emphasizes modern data engineering practices, infrastructure as code, automated data quality testing, workflow orchestration, monitoring, and secure deployment strategies. Through practical exercises and international case studies, participants will learn how to establish effective CI/CD pipelines for analytics projects, manage development environments, automate data model validation, and implement continuous monitoring. The course also examines enterprise applications of CI/CD in cloud data warehouses, business intelligence, and large-scale analytics ecosystems, enabling participants to deliver trusted, production-ready data products while reducing deployment risks, operational costs, and development bottlenecks. 

Course Objectives 

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

1. Understand CI/CD principles and their application in analytics engineering.
2. Design automated data integration and deployment pipelines.
3. Implement Git-based version control and collaborative development.
4. Configure GitHub Actions and GitLab CI/CD workflows.
5. Automate dbt model testing and deployment processes.
6. Apply DevOps practices to modern data engineering environments.
7. Implement automated data quality and validation frameworks.
8. Manage cloud-based analytics deployment environments.
9. Integrate infrastructure as code into analytics workflows.
10. Apply containerization using Docker for reproducible environments.
11. Implement workflow orchestration and scheduling automation.
12. Strengthen data governance, security, and deployment compliance.
13. Monitor, troubleshoot, and optimize production analytics pipelines.

Organizational Benefits
 

1. Improved analytics deployment efficiency and automation.
2. Reduced operational errors and deployment failures.
3. Enhanced data quality and reliability.
4. Faster delivery of business intelligence solutions.
5. Improved collaboration between engineering and analytics teams.
6. Reduced development and maintenance costs.
7. Stronger data governance and security compliance.
8. Increased scalability of enterprise data platforms.
9. Improved monitoring and operational visibility.
10. Greater organizational agility and innovation.

Target Audiences
 

1. Analytics Engineers and Data Engineers.
2. Business Intelligence Developers.
3. Data Scientists and Data Analysts.
4. DevOps and Platform Engineers.
5. Database Administrators.
6. Cloud Solutions Architects.
7. Software Development Professionals.
8. IT Managers and Data Engineering Team Leaders.

Course Duration: 10 days
 
Course Modules

Module 1: Fundamentals of CI/CD for Analytics Engineering
 

• Introduction to continuous integration and continuous delivery.
• DevOps principles and modern analytics engineering.
• CI/CD architecture and lifecycle management.
• DataOps and automated analytics workflows.
• CI/CD tools and technology ecosystems.
• Case study: Netflix's automated software delivery practices.

Module 2: Version Control with Git and GitHub
 

• Git fundamentals and repository management.
• Branching, merging, and pull requests.
• Collaborative analytics development.
• Conflict resolution and release tagging.
• Repository documentation and governance.
• Case study: Microsoft's GitHub-based collaborative development.

Module 3: CI/CD Pipeline Architecture and Design
 

• CI/CD pipeline components and architecture.
• Pipeline triggers and automated execution.
• Build, test, and deployment stages.
• Pipeline configuration and optimization.
• Managing development and production environments.
• Case study: Spotify's continuous delivery engineering practices.

Module 4: GitHub Actions for Analytics Automation
 

• GitHub Actions architecture and workflows.
• YAML configuration and workflow automation.
• Automated testing and build execution.
• Secrets management and environment variables.
• Workflow monitoring and troubleshooting.
• Case study: GitHub Actions for automated analytics project validation.

Module 5: GitLab CI/CD and Pipeline Management
 

• GitLab repositories and pipeline architecture.
• GitLab runners and job configuration.
• Pipeline stages and dependency management.
• Automated deployment and approval workflows.
• Pipeline security and performance optimization.
• Case study: Enterprise GitLab CI/CD implementation.

Module 6: dbt Development and Automated Testing
 

• dbt architecture and analytics transformation.
• SQL-based data modeling and modular development.
• dbt tests and documentation generation.
• Incremental models and dependency management.
• Automated dbt deployment workflows.
• Case study: Modern dbt-based analytics engineering at enterprise scale.

Module 7: Automated Data Quality and Validation
 

• Data quality frameworks and validation strategies.
• Schema testing and integrity checks.
• Automated testing of analytical datasets.
• Data observability and anomaly detection.
• Managing data quality failures.
• Case study: Airbnb's data quality and testing practices.

Module 8: CI/CD for Cloud Data Warehouses
 

• Cloud analytics architecture and deployment.
• Snowflake and Google BigQuery integration.
• Amazon Redshift deployment workflows.
• Automated SQL execution and validation.
• Cloud environment configuration management.
• Case study: Cloud-based analytics deployment in global enterprises.

Module 9: Docker and Containerization
 

• Containerization fundamentals for analytics.
• Docker images and container management.
• Reproducible analytics development environments.
• Container-based testing and deployment.
• Docker integration with CI/CD pipelines.
• Case study: Containerized data engineering workflows at scale.

Module 10: Workflow Orchestration and Automation
 

• Apache Airflow architecture and DAG development.
• Scheduling and dependency management.
• Automated pipeline execution.
• Failure handling and workflow recovery.
• Integrating orchestration with CI/CD.
• Case study: Airbnb's Apache Airflow workflow orchestration.

Module 11: Infrastructure as Code and Environment Management
 

• Infrastructure as code fundamentals.
• Terraform for analytics infrastructure.
• Environment provisioning and configuration.
• Automated infrastructure deployment.
• Version-controlled infrastructure management.
• Case study: Cloud infrastructure automation using Terraform.

Module 12: Security, Governance and Compliance
 

• CI/CD security principles and access management.
• Secrets management and credential protection.
• Data privacy and regulatory compliance.
• Role-based access control and audit trails.
• Secure deployment and approval mechanisms.
• Case study: Financial-sector secure deployment and governance practices.

Module 13: Monitoring, Logging and Pipeline Observability
 

• CI/CD monitoring and performance metrics.
• Pipeline logging and error tracking.
• Data observability and alert management.
• Deployment failure detection and recovery.
• Production pipeline optimization.
• Case study: Uber's large-scale data infrastructure monitoring.

Module 14: Advanced Deployment Strategies and Optimization
 

• Blue-green and canary deployment strategies.
• Automated rollback and release management.
• CI/CD pipeline performance optimization.
• Parallel execution and deployment automation.
• Managing complex analytics dependencies.
• Case study: Google and Amazon's progressive deployment approaches.

Module 15: Practical CI/CD Analytics Engineering Project
 

• Designing an end-to-end analytics CI/CD pipeline.
• Integrating Git, dbt, and automated testing.
• Implementing cloud-based deployment workflows.
• Monitoring, troubleshooting, and validating pipelines.
• Developing production-ready analytics documentation.
• Case study: Building an automated enterprise analytics pipeline using global best practices.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard CI/CD tools.
• Hands-on Git, GitHub Actions, GitLab, and dbt exercises.
• Real-world analytics engineering projects and simulations.
• International case studies and collaborative group assignments.
• Practical pipeline configuration, testing, and deployment exercises.
• Individual assessments and troubleshooting activities.
• Final project presentation and competency evaluation.

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.