hadoop vs RDBMS What is the difference between Hadoop & Database

Apache Hadoop is the future of the database because it is designed to keep a large amount of data which will not be possible with traditional databases.

There are some differences between Hadoop and RDBMS which are mentioned below.

1) Architecture
Hadoop is a distributed computing framework having three main component, that is HDFS, MapReduce, and YARN. whereas RDBMS is a traditional database having ACID properties

2) Scalability
RDBMS follow vertical scalability. It means if the data increases for storing then we have to increase the particular system configuration.(like RAM and memory space)
While Hadoop follows horizontal scalability. It means we just have to add one more node to the cluster if there is any requirement for the increase in data volumes.

3) 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 data store and there will be no data loss.

4) OLTP (OnLine Transactional Processing) and OLAP (OnLine Analytical Processing)
RDBMS support OLTP whereas Hadoop does not support OLTP. Apache Hadoop is designed to support large-scale Batch Processing workloads for OLAP

The differences between Hadoop vs RDBMS :

1) Hadoop is a distributed computing framework, it has two core components HDFS and MapReduce
Hadoop Core doesn’t support Real time Data processing (OLTP),it is designed to support large scale Batch Processing work loads (OLAP),where as RDBMS are designed for OLTP (Real time data processing) not Batch Processing.

2) RDBMS is good if data is low upto GB’s .If data exceeds GB’s and is in TB or PB then hadoop is best to use.

3) Hadoop offers massive scale in processing power and storage at a very low comparable cost to an RDBMS.

4) In RDBMS if data increases machine configuration is increased so it follows vertical scalability ,if same happens for hadoop just need to add more machines to its cluster for better performance so it follows horizontal scalbility.

5) Hadoop is designed for Write once read many type of data.It doesn’t work for random reading and writing of a few records like RDBMS.

6) Hadoop can work with structured ,semi structured or unstructured data where RDBMS is best for strutured data.

7) Traditional databases/RDBMS have ACID properties . You get none of these with Hadoop.

8) RDBMS requires a schema before data ingestion. Since hadoop can handle all types of data so schema is not mandatory.

Differences between Hadoop and RDBMS :

Query-Response- In RDBMS, query response time is immediate. In Hadoop, there is latency due to Batch processing.

Data Size: RDBMS is useful when we have data upto GB’s but if we have data which exceeds GB’s or TB’s then Hadoop is very useful in processing such data.

Structure Of Data: RDBMS is best suited for Structured-data and hadoop and handle Structured, Semi-Structured or unstructured data.

Scaling: It is Non-linear in RDBMS and Linear in Hadoop which means with increase in resources Hadoop/MapReduce will Scale.

Updates: In RDBMS, we need Read/Write Many Times, in Hadoop we can Write once and read many times.