Database Insights

Use Neo4j and graph databases for AI knowledge graphs — entity extraction, relationship storage, and RAG augmentation

Monitor pgvector query performance — index hit rates, slow similarity searches, HNSW vs IVFFlat trade-offs

Evaluate AI-native databases for ML workloads — vector search, hybrid queries, and integration with LLM frameworks

Use Apache Pinot for sub-second OLAP — StarTree indexes, Kafka ingestion, tenant isolation, and query latency at scale

Choose where to store embeddings at scale — pgvector vs Redis Stack vs dedicated vector databases for production AI apps

Use LLMs for natural language to SQL — schema context injection, prompt design, safety guards, and accuracy benchmarks

Build production RAG pipelines using PostgreSQL + pgvector — chunking, embedding storage, similarity search, and retrieval

Compare pgvector, Pinecone, Weaviate, and Qdrant for AI workloads — cost, performance, and operational trade-offs

Compare Snowflake, ClickHouse, and BigQuery on performance, cost, ecosystem, and real-world use cases in 2026

PostgreSQL WAL retention is a balancing act — too little and standbys fall behind; too much and you risk disk exhaustion. Here's how to configure wal_keep_size and manage replication slots safely.

TiDB performs schema changes online without blocking reads or writes — using a distributed DDL queue across TiDB nodes. Here's how it works and how to monitor DDL jobs in production.

Five counterintuitive database performance truths every engineer gets wrong — over-indexing costs, N+1 queries, connection pool sizing, replica reads, and EXPLAIN limitations.