System Design Interview
Real-world architecture case studies: Instagram, Netflix, Uber and more
9 lessons
E-Commerce Microservices
Full e-commerce: API Gateway, Auth + Redis, Products, Orders, Kafka, Payment, Notifications
Modern Data Stack
Real-world data lakehouse: PostgreSQL → CDC → S3 → Iceberg → Trino → BI
Big Data Architecture
Complete big data pipeline with Kafka, HDFS, Spark, and BI tools
Microservices Architecture
Modern microservices with API Gateway, services, and databases
[SYSTEM DESIGN] Instagram
Full Instagram architecture: post upload, feed generation (fan-out on write), social graph, engagement. Covers pre-signed URLs, cache-aside, CQRS, event-driven patterns.
Cloud Data Platform (v1 → v2 → v3)
Cloud Data Platform evolution: toggle v1 Lakehouse Core → v2 Full Platform (CDC, Notebooks, BI) → v3 Enterprise (Flink, Metadata Hub, Advanced Governance). Uses layer pills to progressively reveal platform components.
[LAKEHOUSE] Cloud Data Platform v1
Lakehouse Core MVP: S3 + Iceberg REST Catalog + Managed Spark on K8s + Trino SQL engine + Airflow orchestration + ClickHouse serving layer. Batch ETL, interactive SQL, BI dashboards.
[FULL] Cloud Data Platform v2
Full Platform: adds CDC (Debezium), JupyterHub/Notebooks (Spark Connect), Managed BI (Metabase), Transfer Service, and Data Governance (lineage in Iceberg Catalog) on top of the v1 Lakehouse core.
[ENTERPRISE] Cloud Data Platform v3
Full Enterprise Data Platform: Managed Flink (CEP, windowed joins), Metadata Hub (OpenMetadata lineage + discovery), Advanced Governance (PII detection, column-level masking, DQ rules), 50+ connector Transfer, Console v3 unified workspace. Source to dashboard in one platform.