To make it easier to think about databases, I group them into categories

- SQL Databases (Mysql, Postgres, SQL Server, SQLite)

- JSON document stores (MongoDB, etc)

- In-memory key-value stores (redis, memcache)

- key-value stores (cassandra, etc)

- Graph DBs (neo4j, dgraph)

- Search DBs (elasticsearch, Lucene)

- Timeseries DBs (timescale)

I consider the SQL databases and the JSON document stores to be the general default databases that my applications will use 90%+ of the time. SQL databases require defining the schema ahead of time, while with document stores I can just dump data into the database and then figure out what's needed afterwards. At this point, I usually use postgres as my go-to database since it's open-source, well-supported and has some additional features like the ability to use json, but if you're more familiar with a different SQL db use that instead. Most of the other database categories are specialized for specific use-cases (in-memory key-values are great for queues and caching, time-series dbs are great for metrics)

EDIT: https://db-engines.com/en/ranking currently shows 381 unique databases, which is probably a bit of an undercount but not by much.

I realized I didn't answer the `Why there are so many databases` part of your question. Essentially, many companies outgrow the general-purpose databases and need databases optimized to their read-write level and data structures. Analytics tend to use columnar-databases once their data sets are in the TB+ range so that their queries return quickly.