19 · Product Technology Landscape
Data Technologies
Different data technologies are designed for different kinds of information, access patterns, and application needs.
Different jobs, different tools
These technologies are not simply competing with one another. Each is optimized for a different way of storing, finding, or using information.
PostgreSQL
Rich relationships, SQL, and reliable application data.
MySQL
Widely used relational data for web applications.
SQLite
A small, self-contained database inside an application.
MongoDB
Documents with a flexible data structure.
Redis
Very fast in-memory data access.
Vector Databases
Search vector representations for semantic similarity.
Tables, rows, relationships, SQL
Relational databases organize information into tables made of rows and columns. They are a strong fit when data has clear relationships and the application needs reliable, structured queries using SQL.
Flexible, powerful relational database for many kinds of products.
Popular relational database, especially across web applications.
Lightweight relational database stored in a local file.
MongoDB
DOCUMENTS
Stores record-shaped data that can feel natural for application objects.
FLEXIBLE STRUCTURE
Useful when records do not all have exactly the same fields or shape.
NoSQL is not automatically better than SQL. The useful question is: what shape does the data have, and how will the product retrieve it?
Redis
Redis provides fast in-memory data access. It is commonly used for a cache, sessions, temporary data, queues, or other fast-changing state depending on the architecture.
Vector databases
Vector databases store and search vector representations: numerical patterns that capture meaning or similarity. They are useful when a product needs to find things that are semantically similar, not only things with the same exact words.
Cross-reference: Module 08 · Vector Database / RAG
Which kind of job?
| Technology | Category | Typical role | Simple example |
|---|---|---|---|
| PostgreSQL | Relational | Structured application data | Users, orders, permissions |
| MySQL | Relational | Web application data | Accounts and product catalog |
| SQLite | Relational | Local, embedded data | Data inside a desktop or mobile app |
| MongoDB | Document / NoSQL | Flexible document data | A profile with changing fields |
| Redis | Cache / key-value | Fast temporary access | Cached page result or session |
| Vector Databases | Vector search | Semantic similarity search | Find related documents for RAG |
Choose for the product’s access pattern
Different databases are optimized for different jobs, so the right choice depends on how the product needs to store and retrieve data.