• Data engineers and analysts exploring AWS for data solutions.
• IT professionals seeking to implement data pipelines and analytics workflows.
• Business managers interested in AWS-based analytics solutions.

• Basic knowledge of cloud computing and data concepts is recommended. Familiarity with SQL is beneficial but not mandatory.

Course Outline:

Module 1: Introduction to AWS Data Engineering and Analytics

• Overview of Data Engineering and Analytics Concepts
• AWS Data Ecosystem: S3, Glue, Redshift, Athena, Kinesis

Module 2: Data Storage and ETL Pipelines with AWS Glue

• Introduction to AWS Glue for Data Integration
• Building ETL Pipelines and Cataloging Data
• Hands-On: Creating an ETL Workflow

Module 3: Analytics with Redshift and Athena

• Overview of Amazon Redshift for Data Warehousing
• Serverless Analytics with Amazon Athena
• Hands-On: Querying and Analyzing Data

Module 4: Real-Time Data Processing with Amazon Kinesis

• Introduction to Streaming Data Processing
• Designing Real-Time Workflows with Kinesis Data Streams

Module 5: Real-World Use Cases and Best Practices

• Applications of Data Engineering on AWS
• Best Practices for Scalability and Cost Optimization
• Q&A and Additional Resources

Training material provided: Yes (Digital format)