Curriculum
- 15 Sections
- 71 Lessons
- 10 Weeks
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- Module 1: Data Engineering Foundations6
- 1.1What a Data Engineer does (daily tasks, typical project flow)
- 1.2Data pipeline basics: ingest → store → process → serve
- 1.3Batch vs streaming (where each fits, examples)
- 1.4ETL vs ELT (and what modern teams prefer)
- 1.5Data quality basics: duplicates, nulls, schema drift
- 1.6Intro to lake/lakehouse/warehouse (simple comparison)
- Module 2: AWS Core for Data Engineers (Must-Know)5
- Module 3: Amazon S3 Deep Dive (Data Lake Storage)5
- Module 4: Data Formats + Performance Basics5
- Module 5: AWS Glue Basics (Catalog + Crawlers)5
- Module 6: Glue ETL with PySpark (Real Transformations)5
- Module 7: Lake Formation (Governance + Permissions)5
- Module 8: Amazon Athena (Querying S3 Like a Pro)5
- Module 9: Amazon Redshift (Warehouse Layer)5
- Module 10: Data Ingestion Patterns (DMS + Common Sources)5
- Module 11: Streaming with Kinesis (Real-Time Data)5
- Module 12: Orchestration with Step Functions + EventBridge5
- Module 13: Apache Airflow on AWS (MWAA)5
- Module 14: Monitoring, Logging, Reliability5
- Module 15: Capstone Project (Interview-Ready)0
Data pipeline basics: ingest → store → process → serve
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