Cloud-Native Data Engineering with AWS Training Course

 Cloud-Native Data Engineering with AWS Training Course 

Introduction 

Cloud-Native Data Engineering with AWS Training Course provides comprehensive, practical knowledge and technical expertise in designing, developing, deploying, and managing modern cloud-native data engineering solutions using Amazon Web Services (AWS). The course explores advanced cloud data architecture, scalable data pipelines, serverless computing, distributed data processing, data lakes, real-time data streaming, and automated data orchestration. Participants gain practical experience with AWS Glue, Amazon S3, Amazon Redshift, Amazon Kinesis, AWS Lambda, Amazon EMR, and AWS Step Functions to build resilient, secure, and cost-effective data engineering ecosystems. Emphasis is placed on cloud-native architecture, Infrastructure as Code, DevOps automation, data governance, and enterprise data integration to support modern digital transformation initiatives. 

The training also examines emerging trends in cloud data engineering, including event-driven architectures, serverless ETL, real-time analytics, data mesh, artificial intelligence-driven data processing, and machine learning integration. Through practical exercises and international industry case studies, participants develop the ability to implement highly available data platforms, optimize cloud computing resources, automate complex workflows, and establish reliable data quality frameworks. The programme equips professionals with industry-relevant competencies for managing enterprise-scale data infrastructure, accelerating business intelligence, improving operational efficiency, and delivering data-driven innovation using AWS cloud technologies. 

Course Objectives 

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

1. Design scalable cloud-native data engineering architectures using AWS.
2. Develop automated ETL and ELT data pipelines.
3. Implement serverless data processing using AWS Lambda.
4. Build enterprise data lakes using Amazon S3.
5. Configure real-time data streaming using Amazon Kinesis.
6. Implement cloud data warehousing using Amazon Redshift.
7. Apply distributed data processing using Amazon EMR.
8. Automate workflows using AWS Step Functions.
9. Implement Infrastructure as Code and DevOps automation.
10. Apply data governance, security, and compliance frameworks.
11. Optimize cloud infrastructure performance and costs.
12. Integrate machine learning and artificial intelligence into data pipelines.
13. Implement monitoring, observability, and fault-tolerant data architectures.

Organizational Benefits
 

1. Improved enterprise data management and accessibility.
2. Reduced infrastructure and operational costs.
3. Faster data processing and analytics capabilities.
4. Enhanced business intelligence and reporting.
5. Improved data security and regulatory compliance.
6. Increased scalability and infrastructure flexibility.
7. Automated data integration and processing workflows.
8. Greater operational efficiency and productivity.
9. Improved real-time business decision-making.
10. Strengthened digital transformation and innovation.

Target Audiences
 

1. Data engineers and cloud data architects.
2. AWS cloud solutions architects.
3. Database administrators and developers.
4. Business intelligence and analytics professionals.
5. Big data engineers and administrators.
6. DevOps and cloud infrastructure engineers.
7. Machine learning and artificial intelligence engineers.
8. IT managers and enterprise technology consultants.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Cloud-Native Data Engineering
 

• Cloud-native data engineering principles and architecture.
• AWS global infrastructure and cloud service models.
• Microservices, containers, and serverless computing.
• AWS data engineering ecosystem and service integration.
• Scalability, availability, and distributed computing.
• Case study: Netflix cloud-based data processing architecture.

Module 2: AWS Data Lakes and Cloud Storage
 

• Amazon S3 architecture, storage classes, and lifecycle policies.
• Data lake architecture and implementation.
• Data partitioning, cataloguing, and metadata management.
• AWS Lake Formation and centralized data governance.
• Data encryption, access control, and security.
• Case study: Airbnb-style enterprise data lake implementation.

Module 3: Cloud-Native ETL and Data Pipeline Development
 

• AWS Glue architecture and serverless ETL development.
• Data transformation using PySpark and Python.
• Data integration and automated ingestion.
• AWS Glue Data Catalog and schema management.
• Pipeline scheduling and workflow automation.
• Case study: Global retail data integration using AWS Glue.

Module 4: Real-Time Data Streaming and Event-Driven Architecture
 

• Amazon Kinesis Data Streams and Firehose.
• Event-driven data processing architectures.
• AWS Lambda for serverless event processing.
• Stream processing, filtering, and transformation.
• Real-time monitoring and data delivery.
• Case study: Uber-style real-time transportation analytics.

Module 5: Cloud Data Warehousing and Analytics
 

• Amazon Redshift architecture and configuration.
• Data warehouse modelling and dimensional design.
• Query optimization and workload management.
• Data integration between S3 and Redshift.
• Business intelligence and analytical reporting.
• Case study: Global financial services data warehousing.

Module 6: Distributed Processing and Workflow Orchestration
 

• Amazon EMR and distributed computing fundamentals.
• Apache Spark and Hadoop processing frameworks.
• AWS Step Functions and workflow orchestration.
• Dependency management and pipeline scheduling.
• Fault tolerance, retries, and error handling.
• Case study: Large-scale e-commerce data processing.

Module 7: Cloud Security, DevOps and Infrastructure Automation
 

• AWS Identity and Access Management and security policies.
• Infrastructure as Code using AWS CloudFormation.
• CI/CD pipelines and automated deployments.
• Data encryption, monitoring, and compliance.
• AWS CloudWatch and operational observability.
• Case study: Enterprise cloud security implementation.

Module 8: Advanced Cloud-Native Data Engineering
 

• Data mesh and modern distributed data architecture.
• Machine learning integration with AWS data pipelines.
• Data quality validation and automated testing.
• Cloud performance optimization and cost management.
• Capstone: Design and deploy an integrated AWS data engineering pipeline.
• Case study: Global healthcare analytics and scalable cloud data processing.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical AWS cloud laboratory exercises.
• Hands-on data pipeline development projects.
• Group discussions and international case study analysis.
• Real-world cloud architecture simulations.
• Capstone projects and competency 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.