When to use Hadoop, HBase, Hive and Pig?

What are the benefits of using either Hadoop or HBase or Hive ?

From my understanding, HBase avoids using map-reduce and has a column oriented storage on top of HDFS. Hive is a sql-like interface for Hadoop and HBase.

I would also like to know how Hive compares with Pig.

MapReduce is just a computing framework. HBase has nothing to do with it. That said, you can efficiently put or fetch data to/from HBase by writing MapReduce jobs. Alternatively you can write sequential programs using other HBase APIs, such as Java, to put or fetch the data. But we use Hadoop, HBase etc to deal with gigantic amounts of data, so that doesn't make much sense. Using normal sequential programs would be highly inefficient when your data is too huge.

Coming back to the first part of your question, Hadoop is basically 2 things: a Distributed FileSystem (HDFS) + a Computation or Processing framework (MapReduce). Like all other FS, HDFS also provides us storage, but in a fault tolerant manner with high throughput and lower risk of data loss (because of the replication). But, being a FS, HDFS lacks random read and write access. This is where HBase comes into picture. It's a distributed, scalable, big data store, modelled after Google's BigTable. It stores data as key/value pairs.

Coming to Hive. It provides us data warehousing facilities on top of an existing Hadoop cluster. Along with that it provides an SQL like interface which makes your work easier, in case you are coming from an SQL background. You can create tables in Hive and store data there. Along with that you can even map your existing HBase tables to Hive and operate on them.

While Pig is basically a dataflow language that allows us to process enormous amounts of data very easily and quickly. Pig basically has 2 parts: the Pig Interpreter and the language, PigLatin. You write Pig script in PigLatin and using Pig interpreter process them. Pig makes our life a lot easier, otherwise writing MapReduce is always not easy. In fact in some cases it can really become a pain.

I had written an article on a short comparison of different tools of the Hadoop ecosystem some time ago. It's not an in depth comparison, but a short intro to each of these tools which can help you to get started. (Just to add on to my answer. No self promotion intended)

Both Hive and Pig queries get converted into MapReduce jobs under the hood.

HTH

I implemented a Hive Data platform recently in my firm and can speak to it in first person since I was a one man team.

Objective

1. To have the daily web log files collected from 350+ servers daily queryable thru some SQL like language
2. To replace daily aggregation data generated thru MySQL with Hive
3. Build Custom reports thru queries in Hive

Architecture Options

I benchmarked the following options:

1. Hive+HDFS
2. Hive+HBase - queries were too slow so I dumped this option

Design

1. Daily log Files were transported to HDFS
2. MR jobs parsed these log files and output files in HDFS
3. Create Hive tables with partitions and locations pointing to HDFS locations
4. Create Hive query scripts (call it HQL if you like as diff from SQL) that in turn ran MR jobs in the background and generated aggregation data
5. Put all these steps into an Oozie workflow - scheduled with Daily Oozie Coordinator

Summary

HBase is like a Map. If you know the key, you can instantly get the value. But if you want to know how many integer keys in Hbase are between 1000000 and 2000000 that is not suitable for Hbase alone.

If you have data that needs to be aggregated, rolled up, analyzed across rows then consider Hive.

Hopefully this helps.

Hive actually rocks ...I know, I have lived it for 12 months now... So does HBase...