What you'll walk away with
Six outcomes this course is actually built around — not a syllabus dump.
Data platform design
Architect data systems that scale with the business, not against it.
Distributed processing
Spark fundamentals for datasets that don't fit on one machine.
Cloud data infrastructure
Provision and manage data infra on AWS, not just click around a console.
Lakehouse architecture
Understand when a data lake, warehouse, or lakehouse actually fits.
Reliability & governance
Access control, lineage and cost management that survives an audit.
Ship a capstone
A production-grade data platform, deployed and documented.
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.
- Architecture patterns for modern data platforms
- Batch vs streaming: choosing the right approach
- Cost and scale tradeoffs in system design
- Lab: design a platform for a growing startup
- Spark fundamentals: RDDs, DataFrames, and jobs
- Partitioning and performance tuning
- Handling large joins and shuffles efficiently
- Lab: process a multi-terabyte dataset
- Provisioning data infra with infrastructure-as-code
- Managing storage, compute and networking on AWS
- Cost monitoring and optimization
- Lab: deploy a cloud-native data pipeline
- Data lakes vs warehouses vs lakehouses
- Table formats: Delta Lake and Iceberg basics
- Access control, lineage and governance
- Lab: build a governed lakehouse layer
- Bringing it together: ingestion to serving
- Monitoring, alerting and on-call basics
- Documentation that a new hire can follow
- Capstone: ship and present a full platform
Weekly schedule
Live sessions run evenings IST. Filter by day, or just watch — today's slot highlights itself.
Your instructor
Vikram Shah
Staff Data Engineer, large-scale platformsVikram has built and operated data platforms handling petabyte-scale workloads and has led infra teams at two unicorns.