Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, c

Databases Skill Unified guide for working with MongoDB (document oriented) and PostgreSQL (relational) databases. Choose the right database for your use case and master both systems. When to Use This Skill Use when: Designing database schemas and data models Writing queries (SQL or MongoDB query language) Building aggregation pipelines or complex joins Optimizing indexes and query performance Implementing database migrations Setting up replication, sharding, or clustering Configuring backups and disaster recovery Managing database users and permissions Analyzing slow queries and performance issues Administering production database deployments Database Selection Guide Choose MongoDB When: Schema flexibility: frequent structure changes, heterogeneous data Document centric: natural JSON/BSON data model Horizontal scaling: need to shard across multiple servers High write throughput: IoT, logging, real time analytics Nested/hierarchical data: embedded documents preferred Rapid prototyping: schema evolution without migrations Best for: Content management, catalogs, IoT time series, real time analytics, mobile apps, user profiles Choose PostgreSQL When: Strong consistency: ACID transactions critical Complex relationships: many to many joins, referential integrity SQL requirement: team expertise, reporting tools, BI systems Data integrity: strict schema validation, constraints Mature ecosystem: extensive tooling, extensions Complex queries: window functions, CTEs, analytical workloads Best for: Financial systems, e commerce transactions, ERP, CRM, data warehousing, analytics Both Support: JSON/JSONB storage and querying Full text search capabilities Geospatial queries and indexing Replication and high availability ACID transactions (MongoDB 4.0+) Strong security features Quick Start MongoDB Setup PostgreSQL Setup Common Operations Create/Insert Read/Query Update Delete Indexing Reference Navigation MongoDB References [mongodb crud.md](references/mongodb crud.md) CRUD operations, query operators, atomic updates [mongodb aggregation.md](references/mongodb aggregation.md) Aggregation pipeline, stages, operators, patterns [mongodb indexing.md](references/mongodb indexing.md) Index types, compound indexes, performance optimization [mongodb atlas.md](references/mongodb atlas.md) Atlas cloud setup, clusters, monitoring, search PostgreSQL References [postgresql queries.md](references/postgresql queries.md) SELECT, JOINs, subqueries, CTEs, window functions [postgresql psql cli.md](references/postgresql psql cli.md) psql commands, meta commands, scripting [postgresql performance.md](references/postgresql performance.md) EXPLAIN, query optimization, vacuum, indexes [postgresql administration.md](references/postgresql administration.md) User management, backups, replication, maintenance Python Utilities Database utility scripts in scripts/ : db migrate.py Generate and apply migrations for both databases db backup.py Backup and restore MongoDB and PostgreSQL db performance check.py Analyze slow queries and recommend indexes Key Differences Summary Feature MongoDB PostgreSQL Data Model Document (JSON/BSON) Relational (Tables/Rows) Schema Flexible, dynamic Strict, predefined Query Language MongoDB Query Language SQL Joins $lookup (limited) Native, optimized Transactions Multi document (4.0+) Native ACID Scaling Horizontal (sharding) Vertical (primary), Horizontal (extensions) Indexes Single, compound, text, geo, etc B tree, hash, GiST, GIN, etc Best Practices MongoDB: Use embedded documents for 1 to few relationships Reference documents for 1 to many or many to many Index frequently queried fields Use aggregation pipeline for complex transformations Enable authentication and TLS in production Use Atlas for managed hosting PostgreSQL: Normalize schema to 3NF, denormalize for performance Use foreign keys for referential integrity Index foreign keys and frequently filtered columns Use EXPLAIN ANALYZE to optimize queries Regular VACUUM and ANALYZE maintenance Connection pooling (pgBouncer) for web apps Resources MongoDB: https://www.mongodb.com/docs/ PostgreSQL: https://www.postgresql.org/docs/ MongoDB University: https://learn.mongodb.com/ PostgreSQL Tutorial: https://www.postgresqltutorial.com/