In the Big Data environment, Hadoop is a major role for large data storage, and data processing with different types of services like Hive, Pig, and etc.
In this article, we will explain what is the major difference between Hadoop HDFS and Apache HBase with examples for Big Data engineers in the present market.
HBase vs Hadoop HDFS:
- Basically, Hadoop is a solution for Big Data for large data storage and data processing. For data storage using Hadoop Distribute Files system and data processing using MapReduce. HDFS is sequential data access, not applicable for random reads/writes for large data.
- Coming to HBase, it is Not OnlySQL(NoSQL) database that runs on top of the Hadoop cluster. It provides random read/write access for large data sets.
- In Hadoop HDFS we can store unstructured, semi-structured, and structured data.
- HBase is not only for key-value stores it is also stored unstructured, semi-structured, and structured data.
- Basically, Hadoop follows the write-once and ready many
- In HBase, it follows the key-value pairs in the columnar level.
- In the Hadoop eco-system mostly followed the batch processing, HBase is used only for real-time needs.
Summary: Hadoop HDFS is used for unstructured, semi-structured, and structured large data sets but doesn’t have to provide fast search in the record files. Coming to NoSQL databases such as HBase, Cassandra, and MongoDB, etc. Basically, HBase is built on top of HDFS, and then it provides a fast search in the record files. As per HDFS architecture that does not allow changes. Then come to HBase architecture allows for dynamic changes for standalone applications. In Big Data system Hadoop and HBase are mandatory framework/services as per business requirements and how we will receive data daily. Nowadays these services are also provided by Big Data distributions are like Cloudera, Hortonworks, and MapR distributions for users.