I sort of agree with threeseed and and the GP comment, so upvotes to both of you. 1) Altering schema is vague. It was used vaguely in the article (although,

I sort of agree with threeseed and and the GP comment, so upvotes to both of you. 1) Altering schema is vague. It was used vaguely in the article (although, given the clarity of the article, I suspect the authors knew exactly what they meant). Some alterations on relational database tables are fine, even when hot and have billions of rows. Others are not. Add a new column: fine. Index the new column: fine. Create a new index on a column with billions of rows: definitely not fine. But the index plan described in the article was very specific about what they wanted. It doesn't sound like they had to add any new indices. 2) Mostly agree here. Linear scalability is a big deal here, and it's fucking hard to do well for most RDBMS systems. I slightly disagree, however, because the article explicitly states that the requirements are willing to trade C for A in CAP theorem. This is important. The hardest parts of linear scaling in RDBMS are enforcing C. Think transactional systems that absolutely must be consistent. Like your bank account. This isn't that, and the blog post clearly states it. Takes a lot of pressure off the relational database when it comes to scaling. 3) Strongly disagree. Most companies don't have the resources or manpower to do that. It takes a lot of time and a lot of effort. Hell, most companies don't even have an EDW. Let alone a pipeline from the OLTP server to Spark/Hadoop to the non-existent EDW. 4) We seem to run in different circles. Almost everyone I know dismisses relational databases without question. Mongo is the way to go. And I get called out as the resident old fart/luddite who insists on using postgres. Speaking of which, if the first things you think of with relational DBs are Teradata and Oracle, we are definitely operating in different contexts. If your opinion is that relational databases are generally well understood by--and therefore often the first choice for--developers . . . I want to know where you work. Because that's not a different context from where I am. That's a different universe. The reality is that storing and retrieving data is a hard problem, and there's no set answer that works for everyone in every situation. If you're building a new product from scratch, you should go with what your team knows, provided that the team knows enough to not put yourself in the situation where you're just losing data in a partition scenario (well-made point in the original article. Mongo is fine on one node. Scale it out, and you might as well write your data to /dev/null) Almost any datastore will serve the needs of a new product until it needs to scale horizontally. Relational, NoSQL, Object store, whatever. When it comes to scaling linearly, you have to take factors into account. 1) Which part of CAP theorem are you willing to sacrifice? You always have to let go of one. 1a) If you want a CP system, you have no choice but to deal with scaling problems of relational databases. You must have transactional guarantees for this to work. 1b) If you need an AP system, you have choices, but the choices lean in favor of systems like cassandra. It's just easier than seting up multi-node postgres and doing sharding. It's also worth pointing out that people very often dismiss vertical scaling too soon. Take a look at Joel Spolsky's articles about infrastructure at StackOverflow. You can do quite a lot with the available firepower of modern technology by just buying bigger and better hardware. I'm not suggesting that going bigger would have been the right choice for Discord. But sometimes it can be the right choice. If there's something I fundamentally disagree with about the article, it's this: trying to do everything in a single data store. I think--much like what you suggested above--that it's better to have separate systems for reading and writing. Since the use case is definitively AP, I can't see a reason not to have a transactional system in an RDBMS and a streaming pipeline to a cassandra cluster for reading. Use the right tools for the right job, is basically my point.