Data Engineering on AWS

Overview

Build a complete AWS data engineering stack—design and ingest data into S3-based lakes, transform and query it with Glue and Athena, model a Redshift analytics warehouse, and automate end-to-end pipelines with Lambda and EventBridge.

Syllabus

Course 1: Designing & Ingesting Data into AWS Data Lakes
Course 2: Transforming and Analyzing Data with AWS Glue & Athena
Course 3: Data Warehousing on AWS with Amazon Redshift
Course 4: Data Pipeline Automation on AWS

Courses

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Build a scalable data lake on AWS. Learn to structure S3 storage with data zones, ingest streaming events with Amazon Kinesis and Firehose, and catalog data using AWS Glue Crawlers to prepare for downstream transformation and querying.
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Build ETL pipelines with AWS Glue and PySpark, convert raw JSON to Parquet, and run fast analytics with Amazon Athena. Learn to manage data across raw, processed, and curated zones and automate workflows to deliver business-ready insights from your data lake.
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Build a fast, organized analytics warehouse on AWS by connecting Redshift Serverless to your S3 data lake. Model a star schema, load and export data efficiently, tune for speed, and publish business-ready analytics—all with hands-on, real-world demos.
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Automate data workflows on AWS using Lambda and EventBridge. Build serverless functions that trigger on S3 uploads, run on schedules, and launch Glue ETL jobs—creating an end-to-end pipeline that processes files automatically without manual intervention.