Do You Know difference between hadoop and spark

An Introduction of Hadoop:

Hadoop is an Open Source framework for Big data processing and storing using simple programming language. Hadoop allow distributed data processing of large data set across various cluster of computer

Read More- Hadoop Intoduction

Read More- What is Big data and Hadoop

Apache Spark:

Apache Spark is designed for cluster computing. Apache Spark used HDFS file system and use computer memory and disk for fast computation.

Similarities Hadoop and Spark :

Similarities	Hadoop	Spark
open source framework	YES	YES
Real Time BI & Big data analytics	YES	YES
fault tolerance & scalability	YES	YES
JVM based programming languages	YES	YES

Difference Hadoop and Spark :

Hadoop	Spark
Hadoop MapReduce is best suited for batch processing. For big data applications that require real time options,	Apache Spark used for both batch processing and real time processing.
Spark processes in-memory data	Hadoop MapReduce persists back to the disk
Hadoop MapReduce is written in Java. using Apache Pig makes it easier to develop in Hadoop, although some time needs to be spent on understand and learning the Syntax of Apache Pig. To add the SQL compatibility to Hadoop, developers can use Hive on top of Hadoop.	Spark uses Scala tuples and they can only be intensified by nesting the generic types because Scala tuples are difficult to be implemented in Java.

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