Apache Hadoop is the future of the database because it stores and processes a large amount of data. Which will not be possible with the traditional database. There is some difference between Hadoop and RDBMS which are as follows:
1) Architecture – Traditional RDBMS have ACID properties. Whereas Hadoop is a distributed computing framework having two main components: Distributed file system (HDFS) and MapReduce.
2) Data acceptance – RDBMS accepts only structured data. While Hadoop can accept both structured as well as unstructured data. It is a great feature of Hadoop, as we can store everything in our database and there will be no data loss.
3) Scalability – RDBMS is a traditional database which provides vertical scalability. So if the data increases for storing then we have to increase particular system configuration. While Hadoop provides horizontal scalability. So we just have to add one or more node to the cluster if there is any requirement for an increase in data.
4) OLTP (Real-time data processing) and OLAP – Traditional RDMS support OLTP (Real-time data processing). OLTP is not supported in Apache Hadoop. Apache Hadoop supports large scale Batch Processing workloads (OLAP).
5) Cost – Licensed software, therefore we have to pay for the software. Whereas Hadoop is open source framework, so we don’t need to pay for software.
Read: 50+ Hadoop MapReduce Interview Questions and Answers
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