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☁️ AWS Data Engineering • S3 • Glue • Lambda • Redshift • Airflow

AWS Data Engineering

End-to-end AWS lessons from the provided AWS lessons.txt — S3 features & classes, security, Glue DynamicFrames & DPU, Lambda triggers, SCD, Redshift distribution & sort keys, Airflow DAGs, all without overwriting SQL/PySpark.

Start with S3 → Explore Glue

Learning Path

10 chapters derived directly from AWS lessons.txt

🪣 01

S3

Features, Classes, Security, Versioning, Folder Structure, Cross-Account

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🔧 02

Glue

Catalog, DynamicFrames, DPU Workers, Crawlers, Jobs, Workflows, Bookmarks

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λ 03

Lambda

Serverless, Layers, Limitations, Boto3, Glue Triggers

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🕰️ 04

SCD Types

SCD 0-6 with PySpark example for SCD2

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🔄 05

ETL & Incremental

ETL vs ELT, Medallion, Incremental Loads, CDC/DMS

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06

Redshift

MPP, Columnar, Spectrum, Distribution, Sort Keys, Optimization

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07

EMR

Elastic MapReduce, Architecture, Nodes, Storage, EMR vs Glue

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08

Snowflake

Cloud DW, Architecture, Snowpipe, Streams, Time Travel, Failsafe

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09

Airflow

Operators, Sensors, DAG, Catchup/Depends, vs Step Functions

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10

DevOps

Git/GitHub, Secrets Manager, Status Codes

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