Data, Storage and Messaging

SQL, PostgreSQL, caching, Kafka, and durable data movement patterns.

Lessons

  1. Data Contracts and Ownership
  2. Kafka Architecture — Brokers, Partitions, ISR, and Consumers
  3. PostgreSQL MVCC, Isolation & Vacuum
  4. SQL Fundamentals — Querying Relational Data
  5. Backpressure — Slow Down When the System Is Full
  6. Cache Eviction Policies — LRU, LFU, TTL, and Friends
  7. ACID Transactions — All-or-Nothing Database Work
  8. Kafka Exactly-Once Semantics — What Is Actually Guaranteed
  9. PostgreSQL Partitioning & Performance
  10. SQL Indexing & Query Optimization
  11. CDC vs Dual Writes — Keeping Two Stores in Sync
  12. Banking Transaction Platform Design
  13. Cache Stampede — When Expiry Melts the Database
  14. WAL and Crash Recovery
  15. Kafka Partition Ordering and Delivery Guarantees
  16. PostgreSQL Replication & Failover
  17. SQL Window Functions — Analytics Without Collapsing Rows
  18. Change Data Capture (CDC) — Streaming Database Mutations
  19. Caching 101 — Memory Offloading and Latency Reduction
  20. Bloom Filters — Probabilistic Set Membership at Scale
  21. Kafka Streams Introduction
  22. Caching Strategies — Aside, Through, Behind, and Refresh-Ahead
  23. Connection Pooling — Reusing Expensive Database Sessions
  24. Dead-Letter Queues and Poison Messages — Quarantine Work That Keeps Failing
  25. Content Delivery Networks (CDN) — Edge Acceleration and Caching
  26. Consistent Hashing — Rings, Virtual Nodes, and Minimal Rebalancing
  27. Event-Driven Architecture — React to Facts, Don’t Chain Calls
  28. Publish/Subscribe Messaging — One Event, Many Interested Listeners
  29. Database Storage Architectures — B-Trees vs. LSM-Trees
  30. Distributed Cache Design
  31. Distributed Caching — Sharding and High-Availability Clusters
  32. Database Indexes — Find Rows Without Scanning Everything
  33. Database Sharding — Split Data Across Many Machines
  34. Read-Through vs. Write-Through Cache — Who Updates the Cache?
  35. Hot Partition / Hot Key — When One Shard Takes All the Heat
  36. Stale Cache After Write — When Your Own Update Disappears
  37. How to Scale a Database — The Progressive Scaling Ladder
  38. Rebalancing Shards Under Skewed Traffic
  39. Search Index Freshness vs Ranking Quality
  40. Search System Design
  41. SQL vs NoSQL — Choosing a Data Store
  42. Types of Databases — Matching Engines to Access Patterns

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Shubham Jain · Learning Lab