Hadoop Vs Cassandra and HBase

The Vanilla hadoop consists of a Distributed File System (DFS) at the core and libraries to support Map Reduce model to write programs to do analysis. DFS is what enables Hadoop to be scalable. It takes care of chunking data into multiple nodes in a multi node cluster so that Map Reduce can work on individual chunks of data available nodes thus enabling parallelism.

The paper for Google File System which was the basis for Hadoop Distributed File System (HDFS) can be found here

The paper for Map Reduce model can be found here

For a detailed explanation on Map Reduce read this post

Cassandra is a highly scalable, eventually consistent, distributed, structured key-value store. It is not a conventional database but is more like Hashtable or HashMap which stores a key/value pair. Both Cassandra and HBase are implementations of Google's BigTable. Paper for Google BigTable can be found here.

BigTable makes use of a String Sorted Table (SSTable) to store key/value pairs. SSTable is just a File in HDFS which stores key followed by value. Furthermore BigTable maintains a index which has key and offset in the File for that key which enables reading of value for that key using only a seek to the offset location. SSTable is effectively immutable which means after creating the File there is no modifications can be done to existing key/value pairs. New key/value pairs are appended to the file. Update and Delete of records are appended to the file, update with a newer key/value and deletion with a key and tombstone value. Duplicate keys are allowed in this file for SSTable.  The index is also modified with whenever update or delete take place so that offset for that key points to the latest value or tombstone value.

Thus you can see Cassandra's/HBase's internals allow fast read/write which is crucial for real time data handling. Whereas Vanilla Hadoop with Map Reduce can be used to process batch oriented passive data.