Database Insights

A top-down playbook for high-performance MongoDB: measure with the profiler and explain(), model for access patterns, index by the ESR rule, keep the working set in the WiredTiger cache, pool connections, and scale reads with secondaries and sharding — with flow diagrams for each layer.

A repeatable, top-down method for tuning PostgreSQL: measure with pg_stat_statements, read plans with EXPLAIN (ANALYZE, BUFFERS), fix queries and indexes before parameters, then tune memory, I/O, WAL, connection pooling, and autovacuum — with a ready-to-adapt postgresql.conf baseline.

Use TimescaleDB for time-series data in PostgreSQL. Covers hypertable creation, continuous aggregates with refresh policies, retention policies, and 10-20x compression.

Use dbt to build modular SQL transformations — models, tests, documentation, and CI/CD for your data warehouse

Choose the right Cassandra compaction strategy for your workload — STCS for write-heavy, LCS for read-heavy, TWCS for time-series

Compare Redis RDB snapshots and AOF logging — durability trade-offs, performance impact, and recovery scenarios

Master MongoDB aggregation pipeline stages — $lookup, $group, $facet, explain plans, and index optimization

Define meaningful SLOs and SLAs for your database tier — latency, availability, durability, and error budgets

Size and configure the InnoDB buffer pool correctly to maximize cache hit ratios and minimize disk I/O

Measure PostgreSQL table and index bloat, then use pg_repack to reclaim space with zero downtime

Fine-tune PostgreSQL autovacuum to eliminate dead tuples, prevent table bloat, and maintain query performance

Use MySQL Performance Schema to profile queries, monitor memory, and diagnose bottlenecks without guesswork