Wonder if you should switch from your RDBMS to Apache Hadoop: Don't!
Last weekend I spend a lot of fun time at FrOSCon* in Sankt Augustin - always great to catch up with friends in the open source space. As always there were quite a few talks on NoSQL, Hadoop, but also really solid advise on tuning your system for stuff like MySQL (including a side note on PostgreSQL and Oracle) from Kristian Köhntopp. When following some of the discussions in the audience before and after the talk I could not help but shake my head on some of the advise given about HDFS and friends.
This is to give a really short rule of thumb on what project to use for which occasion. Maybe it helps clear some false assumptions. Note: All of the below are most likely gross oversimplifications. Don't use it as hard and fast advise but as a first step towards finding more information with your preferred search engine.
I have relational data but my database doesn't perform. Tune your system, go to step 1.
I have relational data but way more reads than one machine can accomodate. Have master-slave replication turned on, configure enough slaves to accommodate your traffic.
I have relational data, but way too much data for a single machine. Start sharding your database.
I have a lot of reads (or writes) and too much data for a single machine. If the sharding+replication pain gets unbearable but you still need strong consistency guarantees start playing with HBase. You might loose the option of SQL but win being able to scale beyond traditional solutions. Hint: Unless your online product is hugely successful switching to HBase usually means you've missed some tuning option.
I want to store and process a crawl of the internet. Store it as flat files, if you like encode metadata together with the data in protocol buffers, thrift or Avro.
My internet crawl no longer fits on a single disk. Put multiple disks in your machine, RAID them if you like.
Processing my crawl takes too long. Optimise your pipeline. Make sure you utilise all processors in your machine.
Processing the crawl still takes too long. If your data doesn't fit on a single machine, takes way too long to process but there is no bigger machine that you can reasonably pay for you are probably willing to take some pain. Get yourself more than one machine, hook them together, install Hadoop and use either plain map reduce , Pig, Hive or Cascading to process the data. Distribution-wise Apache, Cloudera, MapR, Hortonworks are all good choices.
I have structured data and want my business analysts to find great new insights. Use a data warehouse your analysts are most familiar with.
I want to draw conclusions from one year worth of traffic on a busy web site (hint: resulting log files no longer fit on the hard disk of my biggest machine). Stream your logs into HDFS. From here it depends: If it's your developers that want to get their hands dirty, Cascading and depending packages might be a decent idea. There's plenty of UDFs in Pig that will help you as well. If the work is to be done by data analysts that only speak SQL use Hive.
I want to correlate user transactions with social media activity around my brand. See above.
Use HBase to scale the backend your end users interact with. If you want to trade strong consistency for being able to span multiple datacenters on multiple continents take a look at Cassandra.
Use plain HDFS with Hive/Pig/Cascading for batch analysis. This could be business intelligence queries against user transactions, log file analysis for statistics, data extraction steps for internet crawls, social media data or other sensor data.
Use Drill or Impala for low latency business intelligence queries.
For as long as you wonder why you should be switching to Hadoop, don't.
A parting note: I've left CouchDB, MongoDB, JackRabbit and friends out of the equation. The reason for this is my own lack of first-hand experience with those projects. Maybe someone else can add to the list here.