The JusDB Database Engineering Blog

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

Design multi-tenant databases — compare schema-per-tenant, row-level-security, and database-per-tenant approaches

Build a real-time analytics pipeline from PostgreSQL to ClickHouse using CDC — architecture, latency, and schema mapping

Understand the differences between OLAP and OLTP systems — query patterns, storage engines, and when to use a hybrid approach

Compare Liquibase and Flyway for schema migration management — features, abstraction level, and team fit