Serverless Data Engineering on AWS Training Course

 Serverless Data Engineering on AWS Training Course 

Introduction 

Serverless Data Engineering on AWS Training Course is designed to equip participants with practical skills and advanced knowledge required to design, develop, deploy, and manage scalable serverless data engineering solutions using Amazon Web Services (AWS). The course explores modern cloud-native data architecture, serverless computing, data ingestion, data transformation, real-time data processing, data orchestration, and automated analytics pipelines. Participants will gain hands-on experience with Amazon S3, AWS Lambda, AWS Glue, Amazon Kinesis, Amazon DynamoDB, Amazon Athena, Amazon EventBridge, and AWS Step Functions to build highly available, cost-efficient, and event-driven data platforms. Emphasis is placed on cloud data integration, Infrastructure as Code, data lake architecture, distributed processing, and serverless application development. 

The training also examines enterprise data engineering practices, including data governance, security automation, data quality management, performance optimization, and continuous integration and continuous delivery (CI/CD) for serverless data pipelines. Through practical exercises and global industry case studies, participants will explore how organizations leverage AWS serverless technologies to modernize data infrastructure, accelerate business intelligence, and support real-time decision-making. By the end of the course, participants will be able to implement production-ready serverless data solutions that improve operational efficiency, reduce infrastructure management overhead, and support scalable digital transformation initiatives. 

Course Objectives 

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

1. Design scalable serverless data engineering architectures on AWS.
2. Implement cloud-native data ingestion and processing pipelines.
3. Develop event-driven applications using AWS Lambda.
4. Configure automated ETL workflows using AWS Glue.
5. Build secure and scalable data lakes using Amazon S3.
6. Implement real-time data streaming using Amazon Kinesis.
7. Orchestrate serverless workflows using AWS Step Functions.
8. Apply data governance, metadata management, and data cataloguing.
9. Implement data security using AWS IAM and AWS KMS.
10. Optimize serverless workloads for cost and performance.
11. Integrate Amazon Athena for serverless data analytics.
12. Deploy automated data pipelines using Infrastructure as Code.
13. Monitor, troubleshoot, and maintain enterprise serverless data platforms.

Organizational Benefits
 

1. Reduced infrastructure provisioning and maintenance costs.
2. Improved scalability of enterprise data engineering platforms.
3. Faster deployment of cloud-native data solutions.
4. Enhanced real-time business intelligence capabilities.
5. Improved data security and regulatory compliance.
6. Increased automation of enterprise data workflows.
7. Better data quality and accessibility across departments.
8. Reduced operational complexity through managed services.
9. Improved organizational agility and digital transformation.
10. More efficient utilization of cloud computing resources.

Target Audiences
 

1. Data engineers and cloud data architects.
2. AWS cloud solutions architects.
3. Big data and analytics professionals.
4. Database administrators and developers.
5. Business intelligence specialists.
6. DevOps and cloud automation engineers.
7. IT infrastructure and enterprise architects.
8. Technical managers and digital transformation professionals.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Serverless Data Engineering on AWS
 

• Serverless computing concepts and cloud-native data architecture.
• AWS serverless ecosystem and core service integration.
• Event-driven architecture and distributed data processing.
• Serverless data pipeline design principles.
• Scalability, availability, and infrastructure cost optimization.
• Case study: Netflix's use of AWS cloud technologies for scalable digital content data processing.

Module 2: Serverless Data Ingestion and Storage
 

• Data ingestion using Amazon S3 and AWS Lambda.
• Structured, semi-structured, and unstructured data integration.
• Data lake architecture and storage lifecycle management.
• Amazon DynamoDB for serverless data storage.
• Data partitioning, compression, and storage optimization.
• Case study: Airbnb's data infrastructure and cloud-based data ingestion practices.

Module 3: Serverless ETL and Data Transformation
 

• AWS Glue architecture and serverless ETL development.
• Data transformation using PySpark and Python.
• Data cleansing, validation, and enrichment workflows.
• AWS Glue Data Catalog and schema management.
• Automated batch processing and pipeline scheduling.
• Case study: Capital One's cloud data modernization and automated data processing.

Module 4: Real-Time Data Streaming and Event Processing
 

• Amazon Kinesis Data Streams architecture.
• Real-time data ingestion and event processing.
• AWS Lambda integration with streaming services.
• Event routing using Amazon EventBridge.
• Stream monitoring, fault tolerance, and processing optimization.
• Case study: Uber's real-time event processing and large-scale data streaming applications.

Module 5: Serverless Data Analytics and Query Processing
 

• Amazon Athena for serverless SQL analytics.
• Data querying and analysis using Amazon S3.
• Integration with Amazon QuickSight.
• Query performance optimization and partition projection.
• Data visualization and business intelligence reporting.
• Case study: FINRA's cloud-based data analytics and large-scale market surveillance.

Module 6: Workflow Orchestration and Automation
 

• AWS Step Functions for workflow orchestration.
• Automated pipeline execution and dependency management.
• Event-driven scheduling using Amazon EventBridge.
• Error handling, retries, and workflow monitoring.
• Infrastructure automation using AWS CloudFormation and AWS CDK.
• Case study: NASA's cloud-based scientific data processing and workflow automation.

Module 7: Security, Governance, and Data Quality
 

• AWS IAM policies, roles, and access management.
• Data encryption using AWS Key Management Service.
• Data governance and metadata management.
• Data quality validation and monitoring.
• Audit logging, compliance, and secure data sharing.
• Case study: Financial services organizations implementing secure AWS data lake governance.

Module 8: Deployment, Monitoring, and Optimization
 

• Serverless application deployment and CI/CD pipelines.
• Monitoring with Amazon CloudWatch and AWS CloudTrail.
• Performance troubleshooting and operational reliability.
• Serverless cost optimization and resource management.
• Production deployment and disaster recovery strategies.
• Case study: Coca-Cola's cloud modernization and enterprise data platform transformation.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical AWS laboratory sessions and guided demonstrations.
• Real-world serverless data engineering projects.
• Group assignments and collaborative problem-solving exercises.
• International industry case studies and technical simulations.
• Practical assessments and knowledge 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.