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.

Navigate the open source database landscape. Compare MySQL, PostgreSQL, MongoDB, Redis, and Cassandra with detailed feature analysis and selection criteria.

Complete MongoDB guide covering document modeling, aggregation pipelines, sharding, replication, and Atlas deployment. Learn when MongoDB is the right choice for your application.

Compare ScyllaDB and Apache Cassandra — throughput, latency, operational complexity, and when to switch

Apply DynamoDB advanced patterns — GSI overloading, sparse indexes, single-table design, and access pattern modeling

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

Model data in Cassandra around query patterns — partition key design, clustering columns, and anti-patterns to avoid

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

Configure Redis Cluster for horizontal scaling — hash slots, replication, failover, and client-side routing

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

Design MongoDB sharded clusters — choose shard keys, manage chunk balancing, and avoid hot shards in production

Design Cassandra tables for time-series data that scale to billions of rows. Learn partition key design, clustering columns, TTL tombstone management, and bucket strategies.