I think the best way is:
a) Copy data into HDFS (if it is not already there)
b) Create external table over your CSV like this
CREATE EXTERNAL TABLE TableName (id int, name string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
STORED AS TEXTFILE
LOCATION 'place in HDFS';
c) You can start using TableName already by issuing queries to it.
d) if you want to insert data into other Hive table:
insert overwrite table finalTable select * from table name;
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
Best Answer
Here's what I found out: Using HiveColumnarLoader makes sense if you store data as a RCFile. To load table using this you need to register some jars first:
To load data from Sequence file you have to use PiggyBank (as in previous example). SequenceFile loader from Piggybank should handle compressed files:
This doesn't work with Pig 0.7 because it's unable to read BytesWritable type and cast it to Pig type and you get this exception:
How to compile piggybank is described here: Unable to build piggybank -> /home/build/ivy/lib does not exist