Database Insights

Use pgbench to benchmark PostgreSQL before and after configuration changes. Covers scale factor selection, TPC-B and read-only workloads, custom SQL scripts, and latency percentiles.

Monitor ETL pipelines with data freshness checks, row count reconciliation, dbt data quality tests, and Slack alerting. Detect silent failures before users do.

MySQL 8 introduced comprehensive password management — password validation policies, reuse history, expiration, and dual passwords for zero-downtime credential rotation.

Implement MySQL audit logging for SOC2, PCI-DSS, and HIPAA compliance. Covers MariaDB Audit Plugin, general query log, trigger-based auditing, and SIEM integration.

Configure PostgreSQL TLS with self-signed or CA-signed certificates. Force TLS via pg_hba.conf, set minimum TLS version, and verify server certs from clients.

pg_stat_statements tracks every query's total time, call count, and cache hit ratio. Learn to enable it, find your highest-cost queries, hunt for cache misses, and measure the impact of every optimization.

mysqld_exporter exposes 300+ MySQL metrics for Prometheus. Learn to deploy it, configure essential alerting rules for connection saturation and replication lag, and build production dashboards in Grafana.

Your MySQL instance is at 80% CPU and nobody knows which query is responsible. Learn to enable the slow query log, analyze it with pt-query-digest, and fix the top patterns causing CPU and I/O saturation.

PostgreSQL's pg_stat_activity view exposes every connection's state — but understanding what 'idle in transaction' means and why it's dangerous requires knowing the full lifecycle of a PostgreSQL backend process.

MySQL transmits data in plaintext by default, exposing passwords and query results to network interception. Here's how to configure SSL/TLS for MySQL — from certificate generation to enforcing encrypted connections.

Hatchet transforms raw MongoDB JSON logs into actionable insights — surfacing slow operations, connection storms, and namespace hotspots that are impossible to spot in raw log files.

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