Become a Data Engineer
A hands-on, industry-aligned Data Engineering syllabus covering Python, SQL, PySpark, Cloud storage, streaming, warehouses (Snowflake / Redshift), orchestration with Airflow, DevOps basics, and more. Build real-world batch and streaming pipelines, work with data lakes & warehouses, and prepare confidently for interviews.
Quick Course Snapshot
Curriculum
Python for Data Engineering (files, APIs, Pandas, PySpark)
SQL for Data Engineers — joins, window functions, performance
Big Data: PySpark, Hadoop concepts, optimizations
Cloud Storage & Services: AWS S3, GCS, Azure Blob (choose one cloud)
Streaming & Real-time: Kafka, Kinesis, Spark Streaming
Data Warehouses: Snowflake, Redshift; working with lakehouses
Orchestration: Airflow / Dagster / Prefect
DevOps basics: Git, Docker, CI/CD, monitoring
Capstone Project
Build a production-ready pipeline: ingest (API/S3), process (Spark), store (Data Warehouse), and visualize. Includes CI/CD, tests, and deployment notes — great for portfolios and interviews.
What you will be able to do
✅ Build scalable batch & streaming pipelines
✅ Work with data lakes, warehouses, and real-time streams
✅ Automate workflows with Airflow / Prefect
✅ Deploy pipelines on cloud platforms (AWS/GCP/Azure)
✅ Confidently attend Data Engineer interviews
Tools & Platforms Covered
✅ Python, Bash, Linux, Git, Docker, CI/CD
✅ MySQL, PostgreSQL, Snowflake, Redshift
✅ Spark, Databricks, Airflow, Pandas, Hadoop
✅ AWS, GCP, Azure (choose one cloud)
✅ Parquet, Avro, JSON, CSV