Hadoop Vs. Hbase

Hadoop is an open-source framework of programs that is used to store and process big data. Hadoop uses multiple clusters of computers to analyze big data sets in parallel. The distributed processing of data sets can be scaled from single servers to multiple servers. The Hadoop library is designed in a manner to detect and negate failure at the application layer level and doesn’t depend upon any hardware for delivering high availability. 

Hadoop is important for  

  • Storing and processing huge amounts of datasets in parallel quickly 
  • It allows to scale infinitely just by adding nodes 
  • It allows complete fault tolerance as the tasks are immediately routed to other nodes in case of failure of some nodes. It is more so possible because multiple copies of data are stored. 
  • It allows you the flexibility to store data in any formats be it text, videos and images 

The four main components of Hadoop are 

  • Hadoop Distributed File System (HDFS) 
  • Yarn 
  • MapReduce, 
  • and libraries 

Hbase 

Hbase is a distributed, column oriented, horizontally scalable big data store built on Hadoop distributed file system. It is modelled after Google’s Bigtable and written in Java. 

Features of Hbase 

  • It has strong consistency for read/write which implies that you will get real time data in a read operation. 
  • It allows for horizontal scaling. As the table size increases and can’t accommodate data, it is auto shraded and distributed to multiple machines in cluster. 
  • It can be coupled with MapReduce. The Hbase table can act as the source or the sink of the MapReduce job 
  • It helps to store sparse data in fault tolerant manner 
  • It serves the need to read/write data in real-time 

Hbase has features like 

  • Compression 
  • Bloom filters and  
  • In-memory operations 

Differences between Hadoop Distributed File System and Hbase 

HDFS  Hbase 
Java based file system  Hadoop database based. Java based, No SQL database 
Has a static architecture  Allows dynamic changes. Can be even used for standalone applications 
Preferred for batch processing offline  Preferred for real time processing 
High latency for operations  Low latency to small amounts of data 
Ideally suited for write once and read many times sequentially  Suited for random write and read of data 
Complete fault tolerant  Partially fault tolerant 
Accessed through MapReduce Jobs  Accessed through Java API, Rest, Avro and Thrift APIs 
Data stored in chunks  Data stored in key value pairs 
Inexpensive when massive amounts of data are being processed  Specifically used in random data access 
Hive performance with HDFS is excellent  Hive performance with Hbase is four to five times slower 
Maximum data size is 30+ petabytes  Maximum data size is nearly 1 petabyte 

 

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