The JusDB Database Engineering Blog

Explore the trade-offs between database normalization and denormalization for optimal performance. Learn when to normalize for data integrity and when to denormalize for query speed.

Twelve schema design patterns that survive production scale: primary key strategy, normalization trade-offs, partitioning, sharding boundaries, and indexing rules.

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

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

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

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

Compare Liquibase and Flyway for production database schema management. Learn changelog formats, rollback strategies, team workflows, and CI/CD integration patterns.

Understand when MySQL JSON columns help and hurt performance. Learn functional indexes, JSON_TABLE, and when to migrate JSON to normalized columns.

MySQL's foreign key implementation has evolved significantly from 5.7 through 8.4 — with changes to metadata locking, instant DDL interactions, and Group Replication behavior that every DBA needs to understand.

Use MySQL JSON columns effectively with path operators, functional indexes via generated columns, JSON_CONTAINS filtering, and partial updates with JSON_SET.

Design Cassandra tables around query patterns, not entities. Covers partition key cardinality, hot partition prevention, time bucketing, and secondary indexes vs materialized views.

Compare three PostgreSQL multi-tenant patterns: shared schema with RLS, schema-per-tenant, and database-per-tenant. Includes hash partitioning for shared schema at scale.