[SOLAR_FIELDS]

Absolutely this.

Also, I’ve had to explain this to so many other engineers, both junior and senior to me: most data is inherently relational. This next statement is a bit opinionated: 9 times out of 10 you probably want an RDBMS. I’ve seen so many attempts to shoehorn some ElasticSearch/Mongo/Neo4j/whatever database into a design because the developer wanted to work on CoolDatabaseTech. Then you’re stuck dealing with joins in CoolDatabase that it wasn’t really designed to do and frustrated at CoolDatabase’s lack of drivers in X language. Later on you’re dealing with stability and scalability issues you would never see with BattleTestedRDBMS.

The amount of capability a well designed Postgres instance can output is insane. I’ve seen a single vertically scaled Postgres instance compete with 100+ node Spark clusters on computations.

[danpalmer]

Exactly, but it goes a lot further than RDBMSs. For example does the application expect that all items on a queue will be processed? If so then you need a durable queue and Redis probably isn't a good idea, and this will likely reduce the throughput of the queue which might change how it needs to be used.

One I've been bitten by several times is expecting APIs to allow me to read-my-writes, only to find that their underlying data store is eventually consistent. The integration point/API client on our end may end up being twice as complex or more just to handle that.

[mamcx]

> 9 times out of 10 you probably want an RDBMS.

And the last 1 can be done (modeled) in a RDBMS when the scale/pressure/volume is low. In other words, wait until you feel the heat.

[SOLAR_FIELDS]

Yeah, Postgres off the top of my head does NoSQL (JSONB), graph and time series stuff either natively or through some cheap or freely available add ons. It really can do anything. It’s not gonna be the best at that non-relational stuff, but it will do a “good enough” job for most use cases until you introduce heavy scaling.