This post outlines some of the most expensive parts of deploying, operating, and maintaining an application that operates on "NoSQL" databases like Mongo, and in my experience, DynamoDB:
1) Implicit Schemas. We avoid this completely by doing production migrations and re-indexes on every new field we add. Its expensive, and we have to write/test/run scripts in production. At times I wish I could just write a Rails migration on an RDS instance and call it a day. N-1 compatibility isn't hard to accomplish with good code reviews.
2) Search. Want to search and join like in the old days? Good luck. Using a NoSQL DB as the primary authoritative store of record is great, but you'll need a secondary indexes for any searches you want to do. If you need to look up an object on a new field (or even one that already exists in all of your data) if you haven't built an index for it you will have to.
3) Serving Clients. Because of the schemaless-blobby nature of writing clients, things get real messy the minute you have multiple services or applications reading or writing to a DB. To get around this, you have to put a service in front of it and serve the data from some RPC technology, which is an extra step and requires more development and maintenance.
4) Administration. There aren't a long history yet of robust toolsets and "science" behind different schemaless NoSQL databases. Meaning which one you choose has a huge impact on your ability to fine tune it, debug consistency or other expectation issues, and do things like proper failover, backups, restores, etc. Knowledge between similar NoSQL DBs doesn't transfer as well so your mileage will vary more so than on SQL databases (MySQL vs PostgresSQL, for instance).
NoSQL has its place and purpose, but it is rarely as the "one database that rules them all" that many businesses end up with. I'd be interested in counter stories.
1) Implicit Schemas. We avoid this completely by doing production migrations and re-indexes on every new field we add. Its expensive, and we have to write/test/run scripts in production. At times I wish I could just write a Rails migration on an RDS instance and call it a day. N-1 compatibility isn't hard to accomplish with good code reviews.
2) Search. Want to search and join like in the old days? Good luck. Using a NoSQL DB as the primary authoritative store of record is great, but you'll need a secondary indexes for any searches you want to do. If you need to look up an object on a new field (or even one that already exists in all of your data) if you haven't built an index for it you will have to.
3) Serving Clients. Because of the schemaless-blobby nature of writing clients, things get real messy the minute you have multiple services or applications reading or writing to a DB. To get around this, you have to put a service in front of it and serve the data from some RPC technology, which is an extra step and requires more development and maintenance.
4) Administration. There aren't a long history yet of robust toolsets and "science" behind different schemaless NoSQL databases. Meaning which one you choose has a huge impact on your ability to fine tune it, debug consistency or other expectation issues, and do things like proper failover, backups, restores, etc. Knowledge between similar NoSQL DBs doesn't transfer as well so your mileage will vary more so than on SQL databases (MySQL vs PostgresSQL, for instance).
NoSQL has its place and purpose, but it is rarely as the "one database that rules them all" that many businesses end up with. I'd be interested in counter stories.