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Postgres Extensions Cheat Sheet: Replace 7 Databases With SQL
This story was originally published on HackerNoon at: https://hackernoon.com/postgres-extensions-cheat-sheet-replace-7-databases-with-sql.
Learn how Postgres replaces Elasticsearch, Redis, Kafka, Pinecone, and MongoDB using extensions for AI, vectors, queues, caching, and search.
Check more stories related to programming at: https://hackernoon.com/c/programming.
You can also check exclusive content about #postgresql-architecture, #pgvector-diskann-tutorial, #timescaledb-hybrid-ai-stack, #bm25-full-text-search, #postgres-message-queue, #postgres-ai-embeddings, #postgres-extensions, #good-company, and more.
This story was written by: @tigerdata. Learn more about this writer by checking @tigerdata's about page,
and for more stories, please visit hackernoon.com.
This guide shows how modern Postgres extensions can replace large parts of today’s fragmented infrastructure stack. Using pgvector, TimescaleDB, pgmq, PostGIS, pg_cron, and BM25 search, developers can run vector AI search, time-series workloads, caching, queues, geospatial queries, and scheduled jobs directly inside Postgres with fewer services, lower complexity, and unified SQL workflows.
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By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/postgres-extensions-cheat-sheet-replace-7-databases-with-sql.
Learn how Postgres replaces Elasticsearch, Redis, Kafka, Pinecone, and MongoDB using extensions for AI, vectors, queues, caching, and search.
Check more stories related to programming at: https://hackernoon.com/c/programming.
You can also check exclusive content about #postgresql-architecture, #pgvector-diskann-tutorial, #timescaledb-hybrid-ai-stack, #bm25-full-text-search, #postgres-message-queue, #postgres-ai-embeddings, #postgres-extensions, #good-company, and more.
This story was written by: @tigerdata. Learn more about this writer by checking @tigerdata's about page,
and for more stories, please visit hackernoon.com.
This guide shows how modern Postgres extensions can replace large parts of today’s fragmented infrastructure stack. Using pgvector, TimescaleDB, pgmq, PostGIS, pg_cron, and BM25 search, developers can run vector AI search, time-series workloads, caching, queues, geospatial queries, and scheduled jobs directly inside Postgres with fewer services, lower complexity, and unified SQL workflows.