About Course
The Data Engineering with AWS – Certified Course equips learners with the essential skills to design, build, and manage modern data pipelines using Amazon Web Services. This course covers cloud-based data ingestion, storage, transformation, orchestration, and analytics using services like S3, Redshift, Glue, Lambda, EMR, and Kinesis. Students gain hands-on experience in building end-to-end data workflows, implementing ETL/ELT processes, managing big data applications, and optimizing cloud data architectures. Ideal for aspiring data engineers, cloud professionals, and anyone preparing for AWS data-related certifications.
Skills You Will Gain:
- Cloud data architecture design
- ETL & ELT pipeline development
- Data ingestion & streaming workflows
- Big data processing on AWS
- Data warehouse design (Redshift)
- Data lake implementation (S3 + Glue)
- Serverless data engineering with Lambda
- Real-time analytics using Kinesis
- Data cataloging, governance & security
- Monitoring & optimizing cloud data workflows
The Course Enables Students To:
- Build scalable data pipelines on AWS
- Use AWS Glue for automated ETL workflows
- Create data lakes using AWS S3
- Design data warehouses with Amazon Redshift
- Process big data using EMR (Hadoop/Spark)
- Analyze real-time streams with Kinesis
- Implement serverless data transformations with Lambda
- Create and manage AWS data catalogs
SYLLABUS:
Module 1: Introduction to AWS for Data Engineering
- Overview of cloud data ecosystems
- AWS architecture fundamentals
- IAM, VPC, security basics
Module 2: AWS Storage Fundamentals
- Amazon S3
- Data lake architecture
- Storage classes & lifecycle policies
Module 3: Data Ingestion Services
- AWS Kinesis Stream & Firehose
- AWS IoT Core
- Data migration tools
Module 4: Database & Data Warehousing
- Amazon Redshift
- DynamoDB
- Aurora & RDS
Module 5: Big Data Processing on AWS
- AWS EMR (Hadoop, Spark, Hive)
- Cluster setup & tuning
- Distributed data processing
Module 6: AWS Glue for ETL/ELT
- Crawlers, Catalogs & Jobs
- PySpark in Glue
- Workflow orchestration
Module 7: Serverless Data Engineering
- Lambda functions
- Step Functions for orchestration
- Serverless ETL pipelines
Module 8: Data Transformation & Analytics
- Athena (SQL over S3)
- Quicksight dashboards
- Data pipeline optimization
Module 9: Data Security & Governance
- IAM policies
- KMS encryption
- Data lineage & monitoring tools
Module 10: End-to-End Capstone Project
- Build a complete AWS data pipeline
- Ingest → Transform → Store → Analyze
- Final documentation & presentation
Skills You Will Develop:
- Data pipeline automation
- Distributed data processing knowledge
- Practical PySpark experience
- Cloud data monitoring & cost optimization
- Production-grade ETL deployments
Tools Covered:
- AWS S3
- AWS Glue
- Amazon Redshift
- Amazon DynamoDB
- Amazon EMR
- AWS Lambda
Live Projects:
- Build a cloud data lake on S3
- Real-time data streaming with Kinesis
- ETL pipeline with Glue + Lambda
- Redshift data warehouse creation
- Big data processing using EMR (Spark)
- Full end-to-end automated data pipeline
Who Is This Program For?
- Aspiring data engineers
- Cloud engineers & architects
- Software developers transitioning to data roles
- Analytics engineers & BI developers
- Students and professionals preparing for AWS certifications
How To Apply:
- Mobile: 9100348679
- Email: coursedivine@gmail.com
