13 AWS Glue ETL

🚀 *Step-by-Step: Building an ETL Pipeline Using AWS Glue and Python* 🚀
Excited to share how I built an efficient ETL pipeline using *AWS Glue**, **PySpark**, and **DynamicFrames* for data processing from **Amazon S3**.
Key Steps:
1️⃣ *Set up Glue job* to process S3 data.
2️⃣ *Initialize Spark & Glue Context* to handle transformations.
3️⃣ *Pull data* from S3 into DynamicFrame.
4️⃣ *Remove duplicates* and unwanted fields.
5️⃣ *Handle nulls* with custom logic.
6️⃣ *Run SQL queries* for aggregation.
7️⃣ *Write back* processed data to S3 in `parquet` format with `snappy` compression.
8️⃣ *Commit job* to finalize.
Proud of this automated workflow that simplifies data transformation!
#AWS #Python #DataEngineering #ETL #CloudComputing #BigData #PySpark #Automation