Data Engineering and Analytics on AWS (Amazon Web Services)

Description:

This training provides a hands-on introduction to data engineering and analytics capabilities on AWS. Participants will learn how to build scalable data pipelines, process and analyze data, and use key AWS services such as AWS Glue, Redshift, and Athena. The training emphasizes practical applications of AWS tools to manage and analyze large datasets efficiently. By the end of the session, attendees will have the foundational skills to design and implement data workflows and analytics solutions on AWS.

Duration: 1 Day

Course Code: BDT33

Learning Objectives:

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

• Identify the key data engineering and analytics services on AWS.
• Build data pipelines using AWS Glue and S3.
• Analyze large datasets using Redshift and Athena.
• Integrate real-time and batch processing workflows.
• Evaluate AWS-based solutions for analytics in business scenarios.

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

• 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.

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)