AWS DataOps and Pipeline Automation Training Course

 AWS DataOps and Pipeline Automation Training Course 

Introduction 

AWS DataOps and Pipeline Automation Training Course is designed to equip professionals with practical skills in modern data engineering, cloud-based data operations, and automated data pipeline management using Amazon Web Services (AWS). The course explores data ingestion, data integration, data orchestration, workflow automation, continuous integration and continuous delivery (CI/CD), infrastructure as code, and cloud-native data processing. Participants will develop expertise in AWS Glue, Amazon S3, Amazon Redshift, AWS Step Functions, Amazon Kinesis, AWS Lambda, and Amazon CloudWatch to build scalable, secure, reliable, and automated data pipelines. Emphasis is placed on DataOps best practices, DevOps integration, data quality management, metadata governance, and real-time data processing. 

Through practical exercises and international case studies, participants will learn to automate end-to-end data workflows, implement monitoring frameworks, optimize data pipeline performance, and establish continuous testing and deployment processes. The training addresses modern enterprise requirements for agile data engineering, big data automation, cloud analytics, and operational efficiency. Participants will explore real-world applications from financial services, healthcare, telecommunications, e-commerce, and logistics, gaining the competencies required to implement resilient AWS data architectures and accelerate digital transformation initiatives. 

Course Objectives 

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

1. Understand AWS DataOps principles and modern data engineering architectures.
2. Design automated, scalable, cloud-native data pipelines.
3. Implement data ingestion using AWS Glue, Lambda, and Amazon Kinesis.
4. Automate data workflows using AWS Step Functions.
5. Apply CI/CD pipelines for data engineering deployments.
6. Implement Infrastructure as Code using AWS CloudFormation.
7. Develop automated data quality and validation frameworks.
8. Configure data pipeline monitoring using Amazon CloudWatch.
9. Integrate AWS services for batch and real-time processing.
10. Apply DevOps practices to data pipeline management.
11. Implement data security, access controls, and governance.
12. Optimize cloud data processing performance and costs.
13. Troubleshoot pipeline failures and implement automated recovery.

Organizational Benefits
 

1. Reduced operational costs through workflow automation.
2. Improved data delivery speed and reliability.
3. Enhanced enterprise data quality and consistency.
4. Increased scalability of cloud data infrastructure.
5. Improved collaboration between data and DevOps teams.
6. Faster deployment of data engineering solutions.
7. Strengthened data security and governance.
8. Reduced manual intervention and operational errors.
9. Improved business intelligence and analytics capabilities.
10. Enhanced organizational digital transformation readiness.

Target Audiences
 

1. Data engineers and cloud data architects.
2. AWS cloud engineers and administrators.
3. DevOps and DataOps professionals.
4. Database administrators and developers.
5. Business intelligence and analytics professionals.
6. Data scientists and machine learning engineers.
7. IT operations and automation specialists.
8. Enterprise technology managers and consultants.

Course Duration: 5 days

Course Modules

Module 1: Introduction to AWS DataOps and Cloud Data Engineering
 

• DataOps principles, lifecycle, and architecture.
• AWS data engineering ecosystem and service integration.
• Data pipeline automation fundamentals.
• DataOps collaboration and agile methodologies.
• Enterprise adoption case study: Netflix data engineering operations.
• AWS cloud-native data architecture design.

Module 2: Data Ingestion and Integration Automation
 

• Data ingestion using AWS Glue and Amazon S3.
• Serverless ingestion with AWS Lambda.
• Real-time streaming using Amazon Kinesis.
• API-based data integration and extraction.
• Case study: Spotify-style large-scale streaming data ingestion.
• Automated ingestion scheduling and error handling.

Module 3: AWS Data Pipeline Development and Orchestration
 

• Building workflows using AWS Step Functions.
• Pipeline orchestration and dependency management.
• Integrating AWS Glue, Lambda, and S3.
• Batch processing and event-driven automation.
• Case study: Global e-commerce order processing pipelines.
• Workflow optimization and automated recovery.

Module 4: CI/CD and Infrastructure as Code
 

• CI/CD principles for data engineering.
• Automated deployments using AWS CodePipeline.
• Version control and collaboration using Git.
• Infrastructure provisioning with AWS CloudFormation.
• Case study: Enterprise cloud deployment automation.
• Testing and release management strategies.

Module 5: Data Quality, Security, and Governance
 

• Automated data validation and quality checks.
• Metadata management using AWS Glue Data Catalog.
• Access control using AWS Identity and Access Management (IAM).
• Encryption, compliance, and audit management.
• Case study: Healthcare data governance and validation.
• Data lineage and governance automation.

Module 6: Pipeline Monitoring and Performance Optimization
 

• Pipeline monitoring using Amazon CloudWatch.
• Logging, alerting, and failure detection.
• Data processing performance optimization.
• Cost monitoring and resource utilization.
• Case study: Financial services data pipeline monitoring.
• Automated incident response and troubleshooting.

Module 7: Real-Time DataOps and Advanced Automation
 

• Event-driven data pipeline architectures.
• Streaming analytics using Amazon Kinesis.
• Serverless automation using AWS Lambda.
• Integrating automated data processing workflows.
• Case study: Global telecommunications real-time analytics.
• Scalable data pipeline management.

Module 8: Practical Implementation and Capstone Project
 

• Designing an end-to-end AWS DataOps architecture.
• Developing automated ingestion and transformation pipelines.
• Implementing CI/CD and workflow orchestration.
• Configuring monitoring, testing, and deployment.
• Case study: Global logistics data integration and automation.
• Capstone project presentation and performance evaluation.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical AWS laboratory exercises and guided simulations.
• Hands-on data pipeline development and automation projects.
• International case studies and enterprise application scenarios.
• Group discussions, collaborative problem-solving, and assessments.
• Capstone implementation and participant 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.