What you'll walk away with
Six outcomes this course is actually built around — not a syllabus dump.
Extract from anything
APIs, databases, files and event streams, handled reliably.
Transform with confidence
Clean, dedupe and reshape data with testable transformation logic.
Load & model warehouses
Star schemas, incremental loads, and slowly changing dimensions.
Orchestration
Schedule and monitor pipelines with Airflow, not cron and hope.
Streaming basics
Kafka fundamentals for near-real-time pipelines.
Data quality & alerts
Catch broken pipelines before your stakeholders do.
Your learning path
Five stages, one flowing track — click a stage in the curriculum below and watch it light up here.
Curriculum
5 modules. Click any module to expand it — and to highlight its stage on the roadmap above.
- Pulling data from REST APIs and databases
- Handling pagination, rate limits and retries
- File-based ingestion: CSV, JSON, Parquet
- Lab: build a multi-source extraction script
- Cleaning, deduping and validating incoming data
- Writing testable transformation functions
- Handling schema drift gracefully
- Lab: transform a messy raw dataset
- Star schemas and dimensional modeling
- Incremental loads and slowly changing dimensions
- Partitioning and query performance basics
- Lab: model a sales data warehouse
- DAGs, scheduling and dependency management
- Retry logic and failure notifications
- Monitoring pipeline health in production
- Lab: orchestrate a full daily pipeline
- Kafka basics for streaming ingestion
- Data quality checks and alerting
- Deploying pipelines to the cloud
- Capstone: ship an end-to-end ETL pipeline
Weekly schedule
Live sessions run evenings IST. Filter by day, or just watch — today's slot highlights itself.
Your instructor
Arjun Sethi
Data Engineer, 8 yrs building pipelines at scaleArjun has built ETL systems processing billions of rows daily and has taught data engineering to over 300 learners.