DevOps for Data Engineering Training Course

 DevOps for Data Engineering Training Course 

Introduction 

DevOps for Data Engineering Training Course is designed to equip professionals with the technical expertise and practical skills required to implement DevOps principles within modern data engineering environments. As organizations increasingly adopt cloud computing, big data analytics, artificial intelligence, and real-time data processing, the need for automated, scalable, secure, and reliable data infrastructure continues to grow. This course explores DataOps, Continuous Integration and Continuous Delivery (CI/CD), Infrastructure as Code (IaC), automated data pipelines, containerization, orchestration, version control, and cloud-native data engineering. Participants will learn how to integrate software engineering best practices into data workflows, streamline development processes, improve deployment reliability, and establish robust data pipeline management frameworks. 

The training also focuses on modern DevOps tools and technologies, including Git, Docker, Kubernetes, Jenkins, Terraform, Apache Airflow, and cloud-based data platforms. Through practical exercises, collaborative projects, and international case studies, participants will develop competencies in automated testing, pipeline monitoring, configuration management, data quality assurance, and continuous deployment. The programme examines how leading organizations leverage DevOps practices to accelerate analytics delivery, minimize operational risks, and improve collaboration between data engineers, software developers, and operations teams. By the end of the course, participants will be equipped to design and implement enterprise-grade DevOps strategies that support scalable data engineering, operational excellence, and digital transformation. 

Course Objectives 

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

1. Understand DevOps principles and their application in modern data engineering.
2. Implement DataOps frameworks for efficient data pipeline development.
3. Design automated CI/CD workflows for data engineering projects.
4. Apply Git-based version control and collaborative development practices.
5. Deploy containerized data engineering applications using Docker.
6. Manage scalable workloads using Kubernetes orchestration.
7. Implement Infrastructure as Code using Terraform.
8. Automate data workflows using Apache Airflow.
9. Apply automated testing and continuous data quality monitoring.
10. Strengthen cloud data infrastructure security and governance.
11. Implement observability, logging, and performance monitoring.
12. Optimize data pipeline reliability, scalability, and deployment efficiency.
13. Develop enterprise DevOps strategies supporting continuous improvement.

Organizational Benefits
 

1. Accelerates data engineering delivery through workflow automation.
2. Reduces operational costs and manual deployment activities.
3. Improves data pipeline reliability and availability.
4. Strengthens collaboration between development and operations teams.
5. Enhances data quality through automated testing.
6. Minimizes deployment errors and infrastructure downtime.
7. Improves cloud resource utilization and scalability.
8. Strengthens cybersecurity and infrastructure governance.
9. Supports faster analytics and business intelligence delivery.
10. Promotes innovation and enterprise digital transformation.

Target Audiences
 

1. Data Engineers and Senior Data Engineers.
2. DevOps Engineers and Cloud Engineers.
3. Data Architects and Solution Architects.
4. Big Data and Analytics Professionals.
5. Software Developers and Platform Engineers.
6. Database Administrators and Data Platform Managers.
7. IT Infrastructure and Operations Managers.
8. Technology Consultants and Digital Transformation Leaders.

Course Duration: 5 days

Course Modules

Module 1: Introduction to DevOps for Data Engineering
 

• DevOps principles, culture, and lifecycle.
• DataOps frameworks and modern data engineering practices.
• Differences between traditional data engineering and DevOps.
• DevOps collaboration, automation, and agile methodologies.
• Enterprise DevOps adoption strategies.
• Case study: Netflix's automation and continuous delivery practices.

Module 2: Version Control and Collaborative Development
 

• Git fundamentals, branching, merging, and repositories.
• GitHub and GitLab for data engineering collaboration.
• Managing data pipeline code and configuration files.
• Code reviews, pull requests, and release management.
• Managing development environments and collaboration workflows.
• Case study: Microsoft's Git-based engineering collaboration.

Module 3: CI/CD for Data Engineering Pipelines
 

• Continuous Integration and Continuous Delivery fundamentals.
• Building automated CI/CD pipelines using Jenkins.
• Automated deployment of data workflows.
• Pipeline testing, validation, and release automation.
• Managing deployment environments and rollback strategies.
• Case study: Spotify's continuous delivery engineering practices.

Module 4: Containerization and Infrastructure as Code
 

• Docker architecture and containerized data applications.
• Kubernetes orchestration and workload management.
• Terraform for Infrastructure as Code automation.
• Configuration management and infrastructure provisioning.
• Cloud infrastructure deployment and scalability.
• Case study: Google Kubernetes-based infrastructure management.

Module 5: Data Pipeline Orchestration and Automation
 

• Apache Airflow architecture and workflow scheduling.
• DAG development and dependency management.
• Automating batch and real-time data pipelines.
• Integrating cloud platforms and data processing frameworks.
• Pipeline execution, recovery, and fault tolerance.
• Case study: Airbnb's use of Apache Airflow for workflow orchestration.

Module 6: Automated Testing and Data Quality Engineering
 

• Unit, integration, and end-to-end pipeline testing.
• Automated data validation and quality assurance.
• Implementing testing frameworks for data pipelines.
• Schema validation and data integrity monitoring.
• Managing test environments and continuous quality improvement.
• Case study: Great Expectations and enterprise data quality automation.

Module 7: Cloud DevOps, Security, and Monitoring
 

• DevOps implementation across AWS, Azure, and Google Cloud.
• Cloud infrastructure security and access management.
• Secrets management and data protection.
• Logging, monitoring, and observability frameworks.
• Performance optimization and incident management.
• Case study: Capital One's cloud-native DevOps and security practices.

Module 8: Enterprise DevOps Strategy and Capstone Project
 

• Designing enterprise DevOps architectures for data platforms.
• Implementing scalable DataOps operating models.
• DevOps governance, compliance, and risk management.
• Measuring deployment performance using DORA metrics.
• Building an automated end-to-end data engineering pipeline.
• Case study: LinkedIn's large-scale data infrastructure and engineering automation.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard DevOps tools.
• Hands-on laboratory exercises and configuration workshops.
• Group assignments and collaborative pipeline development.
• International case studies and real-world business scenarios.
• Capstone projects, participant presentations, and knowledge 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.