Database Insights

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.

Use Apache Flink CDC to capture MySQL and PostgreSQL changes — stateful processing, exactly-once semantics, and Kubernetes deployment

pgvector brings vector similarity search to PostgreSQL — enabling semantic search, recommendation engines, and RAG pipelines without a separate vector database.

Apply GitOps principles to database changes — PR-based schema reviews, automated migrations, and rollback strategies

A production guide to zero-downtime database migrations — why ALTER TABLE causes downtime, the Expand-Contract pattern, safe migration patterns for PostgreSQL and MySQL, and tooling with Flyway, Liquibase, pt-osc, and gh-ost.

Add data quality checks to your pipeline — row count validation, schema drift detection, and anomaly alerting with Great Expectations

UUIDs as primary keys can destroy database performance. Learn why 50M rows went from 0.5ms to 2.3ms inserts, and how to fix it with hybrid approaches.

Compare streaming and batch data processing — latency, throughput, tooling, and when each approach makes sense

A production guide to PostgreSQL Row Level Security — policy creation, USING vs WITH CHECK clauses, performance implications with indexes, and multi-tenant SaaS patterns with real schema examples.

Go beyond basics with Debezium — handle schema evolution, manage connector offsets, and build resilient CDC pipelines

Debezium reads MySQL binary logs and PostgreSQL WAL to stream every row-level change as a Kafka event in near real-time — replacing nightly batch ETL with sub-second data propagation.

Stream PostgreSQL changes to Kafka using pgoutput and Debezium — setup, monitoring, and lag management