No I meant 100MB. It really depends on how strained your databases are. For example, we have about 6 people constantly running jobs on MySQL and running it in

No I meant 100MB. It really depends on how strained your databases are. For example, we have about 6 people constantly running jobs on MySQL and running it into the ground. So if I ever wanted to do some data manipulation I have to pretty much wait until 11pm. But yes, if your situation isn't as bad, you can do a lot with MySQL. HiveQL is less forgiving in the sense that sometimes it doesn't return what you'd expect. Sometimes when I run DISTINCTS and GROUPBY's, the output isn't what SQL on MySQL would give. For example, DISTINCTs on strings don't work very well. GROUPBY's lose a little functionality, and you have to read the documentation to figure out what you can do. Other than that, queries in Hive are not as cheap as they are in a traditional DB. Hive offers great flexibility, but you still have to plan your query carefully because its going to take a while to execute on your large dataset. Even if you specify LIMIT, it doesn't enforce that until after all the MapReduce jobs have run.