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GeeteshSingh/README.md

💫 About Me:

I’m currently working on an end-to-end AWS data pipeline (S3 → Lambda → Glue → Kafka → Redshift) with near-real-time ingestion, monitoring, and retry logic.

I’m looking to collaborate on small data engineering utilities such as schema validation, ingestion connectors, pipeline observability tools, and CI/CD for ETL.

I’m looking for help with Spark job optimization and AWS cost governance for data workflows.

I’m currently learning Spark internals: lazy evaluation, narrow vs wide transformations, and the Catalyst optimizer.

Ask me about Kafka ingestion patterns, ETL reliability, AWS data workflows, and pipeline troubleshooting.

Fun fact: I break and rebuild my own pipelines to understand their failure points and improve design.

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💻 Tech Stack:

Netlify HTML5 JavaScript Markdown Python Bash Script AWS Vercel Apache Spark Apache Kafka Chakra Bootstrap React React Native Streamlit Yarn Apache Tomcat MariaDB Postgres MySQL Pandas GitHub Actions Git GitHub Ansible Docker Grafana Postman Splunk Bash Script Elasticsearch Expo CSS3 Apache Spark AWS

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