MongoDB, NoSQL & Data Modeling

Document databases shine for some workloads and hurt in others.

Two great databases with different strengths.

Start in minutes with Docker or Atlas.

The building blocks of MongoDB.

Insert, find, update and delete.

Filter documents precisely.

Return only the fields you need.

Change documents atomically.

The most important modeling decision.

Proven patterns for common problems.

Flexible does not mean unvalidated.

Indexes decide query speed.

One index serving many queries.

Indexes for specific needs.

Find and fix slow queries.

Understand what MongoDB actually did.

Transform and analyze data inside MongoDB.

Pipelines you will reuse.

Combine collections when you need to.

Work with arrays in pipelines.

Results and filter counts in one query.

Skip is easy and slow. Ranges are fast.

Store and query time correctly.

Multi-document ACID when you need it.

Single-document writes are atomic.

Avoid lost updates.

High availability for MongoDB.

Choose durability and consistency.

Scale beyond one server.

Get it right the first time.

React to data changes in real time.

Delete old documents automatically.

Fixed-size collections for logs.

Store metrics and events efficiently.

Find nearby places.

From text indexes to Atlas Search.

Semantic search in your database.

Use the official driver well.

Schemas and models for Node.js.

Make Mongoose fast.

Two ways to join.

Structured Node backends with MongoDB.

PyMongo and Motor.

The C# driver in practice.

A practical schema for a store.

Content with tags, authors and comments.

One cluster, many customers.

When to use GridFS — and when not to.

Protect your data.

Yes, MongoDB has injection too.