How are HDFS and HBase different from each other

Question

I am new to Big Data and Hadoop, I am confused with the functionalities that HDFS is capable to offer, these are similar to RDBMS. As far as I have searched, I found HDFS is a software which manages data by dividing them into chunks, replicate and store it.

Please help me out to learn the difference.

Answer 1

Apache Hadoop project includes four key modules

1. Hadoop Common: The common utilities that support the other Hadoop modules.

2. Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.

3. Hadoop YARN: A framework for job scheduling and cluster resource management.

4. Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

HBase is A scalable, distributed database that supports structured data storage for large tables. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.

When to use HBase:

1. If your application has a variable schema where each row is slightly different

2. If you find that your data is stored in collections, that is all keyed on the same value

3. If you need random, real-time read/write access to your Big Data.

4. If you need key-based access to data when storing or retrieving.

5. If you have a huge amount of data with existing Hadoop cluster

But HBase has some limitations

1. It can't be used for classic transactional applications or even relational analytics.

2. It is also not a complete substitute for HDFS when doing large batch MapReduce.

3. It doesn’t talk SQL, have an optimizer, support cross record transactions or joins.

4. It can't be used with complicated access patterns (such as joins)

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