Data Engineering Automation and DevOps Training Course

 Data Engineering Automation and DevOps Training Course 

Introduction 

Data Engineering Automation and DevOps Training Course is designed to equip participants with practical knowledge and advanced technical competencies in modern data engineering, workflow automation, and DevOps practices. As organizations increasingly depend on big data analytics, cloud computing, artificial intelligence, and real-time data processing, the ability to develop, automate, deploy, and maintain reliable data infrastructure has become essential. This course explores data pipeline automation, Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes, continuous integration and continuous delivery (CI/CD), Infrastructure as Code (IaC), containerization, microservices, and cloud-native data engineering. Participants gain practical exposure to Python automation, Apache Airflow, Apache Spark, Docker, Kubernetes, Git, Jenkins, Terraform, and cloud-based data platforms to improve operational efficiency and accelerate enterprise data delivery. 

The course further examines how DataOps, DevOps, and automated data management frameworks support scalable, secure, and resilient data ecosystems. Participants develop competencies in automated testing, data quality monitoring, workflow orchestration, version control, deployment pipelines, infrastructure provisioning, observability, and performance optimization. Through practical exercises and international case studies, participants explore how organizations such as Netflix, Spotify, Amazon, and Google implement automated data workflows and cloud infrastructure to support large-scale operations. The training emphasizes modern data engineering architecture, agile development, platform engineering, DevSecOps, and intelligent automation, enabling organizations to reduce manual processes, minimize deployment failures, strengthen data governance, and achieve reliable data-driven decision-making. 

Course Objectives 

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

1. Understand modern data engineering automation and DevOps principles.
2. Design scalable automated data pipelines using ETL and ELT frameworks.
3. Implement workflow orchestration using Apache Airflow.
4. Apply Python scripting for data engineering automation.
5. Configure CI/CD pipelines for automated data deployment.
6. Implement Infrastructure as Code using Terraform.
7. Utilize Docker and Kubernetes for containerized data applications.
8. Apply Git-based version control and collaborative development.
9. Implement automated data quality testing and validation.
10. Integrate cloud computing platforms into data engineering workflows.
11. Apply DevSecOps practices for secure data infrastructure.
12. Establish monitoring, logging, and observability frameworks.
13. Optimize automated data workflows for scalability and reliability.

Organizational Benefits
 

1. Reduced operational costs through intelligent workflow automation.
2. Faster data pipeline development and deployment.
3. Improved data quality and processing accuracy.
4. Enhanced collaboration between data engineering and IT teams.
5. Increased infrastructure scalability and flexibility.
6. Reduced system downtime and deployment failures.
7. Stronger cybersecurity and data governance.
8. Improved business intelligence and analytics capabilities.
9. Greater efficiency through automated testing and monitoring.
10. Enhanced organizational competitiveness through modern DevOps adoption.

Target Audiences
 

1. Data Engineers and Data Architects.
2. DevOps Engineers and Cloud Engineers.
3. Database Administrators and System Administrators.
4. Software Developers and IT Professionals.
5. Business Intelligence and Analytics Professionals.
6. Data Scientists and Machine Learning Engineers.
7. IT Project Managers and Technical Consultants.
8. Digital Transformation and Technology Managers.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Data Engineering Automation and DevOps
 

• Introduction to data engineering automation and DevOps frameworks.
• DataOps principles and modern data engineering architecture.
• Automation lifecycle and agile development methodologies.
• Data infrastructure management and operational efficiency.
• Integration of DevOps into enterprise data environments.
• Case study: Netflix's automated data infrastructure and large-scale data operations.

Module 2: Automated Data Pipelines and Workflow Orchestration
 

• Designing automated ETL and ELT data pipelines.
• Workflow scheduling and orchestration using Apache Airflow.
• Python scripting for automated data extraction and transformation.
• Managing dependencies, triggers, and pipeline execution.
• Error handling, retries, and automated recovery mechanisms.
• Case study: Spotify's automated data workflows supporting music analytics.

Module 3: CI/CD for Data Engineering
 

• Introduction to continuous integration and continuous delivery.
• Building automated deployment pipelines using Jenkins.
• Git and GitHub for version control and collaboration.
• Automated testing and validation of data engineering code.
• Release management and deployment optimization.
• Case study: Google's continuous integration practices for large-scale software delivery.

Module 4: Containerization and Infrastructure as Code
 

• Containerizing data applications using Docker.
• Kubernetes orchestration and container management.
• Infrastructure provisioning using Terraform.
• Configuration management and automated infrastructure deployment.
• Managing scalable cloud-native data environments.
• Case study: Amazon's cloud infrastructure automation using AWS technologies.

Module 5: Cloud Data Engineering and Automation
 

• Cloud-based data engineering architecture and services.
• Automated data storage and processing using AWS, Azure, and Google Cloud.
• Cloud data integration and serverless computing.
• Automating data transfers and resource provisioning.
• Cloud security, access management, and cost optimization.
• Case study: Netflix's migration to AWS cloud infrastructure.

Module 6: Automated Data Quality, Testing and Governance
 

• Automated data validation and quality assurance frameworks.
• Data profiling, cleansing, and anomaly detection.
• Implementing data testing and monitoring workflows.
• Data lineage, metadata management, and governance.
• Automated compliance and data security controls.
• Case study: Airbnb's data quality and reliability engineering practices.

Module 7: DevSecOps, Monitoring and Observability
 

• Integrating security into automated data engineering pipelines.
• Continuous monitoring, logging, and performance tracking.
• Implementing observability using Prometheus and Grafana.
• Incident management and automated system recovery.
• Managing vulnerabilities and secure deployment processes.
• Case study: Google's site reliability engineering and monitoring frameworks.

Module 8: Advanced DataOps Integration and Capstone Project
 

• Integrating DataOps and DevOps into enterprise environments.
• Automating end-to-end data engineering workflows.
• Implementing scalable and fault-tolerant data architectures.
• Performance optimization and operational troubleshooting.
• Developing an automated data pipeline deployment project.
• Case study: Amazon's automated data processing and analytics ecosystem.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard DevOps tools.
• Hands-on Python programming and data pipeline automation exercises.
• Group projects involving CI/CD and Infrastructure as Code implementation.
• International case studies and real-world industry applications.
• Practical assessments, technical simulations, and capstone project presentations.

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.