PostgreSQL in depth + Redis · Kafka
Eight steps to a real data platform — indexes, caching, event streams.
- Difficulty
- advanced
- Lessons
- 8
- Total time
- 210 min
PostgreSQL in depth + Redis · Kafka
SELECT and INSERT are easy. At production traffic, most bottlenecks live in indexes · caches · asynchronous queues. This course frames the roles and boundaries of PostgreSQL, Redis, and Kafka.
Who it's for
- Devs who want to answer "why is this query slow?"
- People unsure whether to use Redis for caching, sessions, or locking
- Teams debating whether Kafka is warranted
What you can do afterwards
- Read EXPLAIN and tune indexes
- Run multiple PostgreSQL pools from one app
- Use Redis in its five roles (cache · session · rate limit · pub/sub · distributed lock)
- Vector search with pgvector + HNSW
- Design Kafka topics · consumers · backpressure
- Choose the right tier in a 3-layer cache (edge · Redis · PG)
Steps
- PostgreSQL deep dive — EXPLAIN · indexes — seq scan vs index · statistics
- Orchestrating multiple pools — domain split · query helpers · transactions
- pgvector + HNSW — vector type · operators · indexes · reproducibility
- Five roles of Redis — cache · session · rate limit · pub/sub · distributed lock
- 3-layer cache strategy — stale-while-revalidate · invalidation · TTL
- Kafka — when · when not — event signatures · partitions · backpressure
- Data pipelines — retries · idempotency — the myth of exactly-once · outbox
- Backup · restore drills — pg_dump · PITR · monthly rehearsal
Prerequisites — complete backend-with-spring or python-data-pipeline · basic PostgreSQL CRUD.