HDFS vs. HBase : All you need to know

The sudden increase in the volume of data from the order of gigabytes to zettabytes has created the need for a more organized file system for storage and processing of data.

The demand stemming from the data market has brought Hadoop in the limelight making it one of biggest players in the industry.

Hadoop Distributed File System (HDFS), the commonly known file system of Hadoop and Hbase (Hadoop’s database) are the most topical and advanced data storage and management systems available in the market.

What are HDFS and HBase?

HDFS is fault-tolerant by design and supports rapid data transfer between nodes even during system failures.

HBase is a non-relational and open source Not-Only-SQL database that runs on top of Hadoop. HBase comes under CP type of CAP (Consistency, Availability, and Partition Tolerance) theorem.

HDFS is most suitable for performing batch analytics. However, one of its biggest drawbacks is its inability to perform real-time analysis, the trending requirement of the IT industry.

HBase, on the other hand, can handle large data sets and is not appropriate for batch analytics. Instead, it is used to write/read data from Hadoop in real-time.

Both HDFS and HBase are capable of processing structured, semi-structured as well as un-structured data.

HDFS lacks an in-memory processing engine slowing down the process of data analysis; as it is using plain old MapReduce to do it.

HBase, on the contrary, boasts of an in-memory processing engine that drastically increases the speed of read/write.

HDFS is very transparent in its execution of data analysis.  HBase, on the other hand, being a NoSQL database in tabular format, fetches values by sorting them under different key values.

Enhanced Understanding with Use Cases for HDFS & HBase

HBase is ideally suited for real-time environments and this can be best demonstrated by citing the example of our client, a renowned European bank.

To derive critical insights from the logs from application/web servers, we implemented solution in Apache Storm and Apache Hbase together.

Given the huge velocity of data, we opted for HBase over HDFS; as HDFS does not support real-time writes.

The results were overwhelming; it reduced the query time from 3 days to 3 minutes.

With our global beverage player client, the primary objective was to perform batch analytics to gain SKU level insights, and involved recursive/sequential calculations.

HDFS and MapReduce frameworks were better suited than complex Hive queries on top of Hbase.

MapReduce was used for data wrangling and to prepare data for subsequent analytics.  Hive was used for custom analytics on top of data processed by MapReduce.

The results were impressive; as there was a drastic reduction in the time taken to generate custom analytics – 3 days to 3 hours.