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

1. PostgreSQL deep dive — EXPLAIN · indexes — seq scan vs index · statistics
2. Orchestrating multiple pools — domain split · query helpers · transactions
3. pgvector + HNSW — vector type · operators · indexes · reproducibility
4. Five roles of Redis — cache · session · rate limit · pub/sub · distributed lock
5. 3-layer cache strategy — stale-while-revalidate · invalidation · TTL
6. Kafka — when · when not — event signatures · partitions · backpressure
7. Data pipelines — retries · idempotency — the myth of exactly-once · outbox
8. Backup · restore drills — pg_dump · PITR · monthly rehearsal

Prerequisites — complete backend-with-spring or python-data-pipeline · basic PostgreSQL CRUD.