What is the Difference Between Hadoop and Spark? with Proper Definition and Brief Explanation

The main difference between Hadoop and Spark is that the Hadoop is an Apache open source framework that enables distributed processing of large datasets across groups of computers using simple programming models, while Spark is a framework in computing. cluster designed for fast Hadoop computing.

Big data refers to data collection that has high volume, speed, and variety. Therefore, it is not possible to use traditional data storage and processing methods to analyze big data. Hadoop is software for storing and handling large volumes of data effectively and efficiently. But on the other hand, Spark is an Apache framework to increase the computation speed of Hadoop. It can handle real-time and batch analytics and data processing workloads.

Key Areas Covered

1. What is Hadoop?
     – Definition, Functionality
2. What is Spark?
     – Definition, Functionality
3. What is the difference between Hadoop and Spark?
     – Comparison of key differences

Key terms

Big Data, Hadoop, Spark

what is Hadoop

Hadoop is an open source framework developed by the Apache Software Foundation. It is used to store big data in a distributed environment to process them simultaneously. In addition, it provides distributed computing and storage across groups of computers. Furthermore, there are four main components in the Hadoop architecture. Are; Hadoop Distributed File System (HDFS), Hadoop MapReduce, Hadoop Common, and Hadoop YARN. 

HDFS is the Hadoop storage system. It works according to the master-slave architecture. The master node manages the file system metadata. The other computers function as slave nodes or data nodes. Also, the data is split between these data nodes. Similarly, the Hadoop MapReduce contains the algorithm to process data. Here, the master node executes map reduce jobs on slave nodes. And, the slave node completes the tasks and sends the results to the master node. Additionally, Hadoop Common provides Java libraries and utilities to support the other components. On the other hand, Hadoop YARN performs cluster resource management and job scheduling.

what is the spark

Spark is an Apache framework to increase the computation speed of Hadoop. It helps Hadoop to reduce the waiting time between queries and to minimize the waiting time to execute the program.

Spark SQL, Spark Streaming, MLib, GraphX ​​and Apache Spark Core are the main components of Spark.

Spark Core – All functionalities are built on Spark Core. It is the general run engine for spark platform. Provides calculation and reference data sets in memory on external storage systems.

Spark SQL – Provides SchemaRDD that supports structured and semi-structured data.

Spark Streaming – Provides capabilities to perform streaming analytics.

MLib – A distributed machine learning framework. Spark MLib is faster than Hadoop’s disk-based version of Apache Mahout.

GraphX ​​- A distributed graph processing framework. Provides an API for expressing graph computation that can model user-defined graphs using the Pregel abstraction API.

Difference between Hadoop and Spark

Definition

Hadoop is an open source Apache framework that enables distributed processing of large data sets across groups of computers using simple programming models. Apache Spark is an open source distributed general-purpose cluster computing framework. Thus, this explains the main difference between Hadoop and Spark.

Velocity

Speed ​​is another difference between Hadoop and Spark. Spark performs faster than Hadoop.

Fault tolerance

Hadoop uses replication of data across multiple copies for fault tolerance. Spark uses a resilient distributed data set (RDD) for fault tolerance.

ape

Another difference between Hadoop and Spark is that Spark provides a variety of APIs that can be used with multiple data sources and languages. Also, they are more extensible than Hadoop APIs.

Use

Hadoop is used to manage the data storage and processing of big data applications running on clustered systems. Spark is used to power Hadoop’s computational process. Therefore, this is also an important difference between Hadoop and Spark.

Conclusion

In conclusion, the difference between Hadoop and Spark is that Hadoop is an Apache open source framework that enables distributed processing of large datasets across groups of computers using simple programming models, while Spark is an open source computing framework. cluster, designed for fast Hadoop computing. Both can be used for applications based on predictive analytics, data mining, machine learning, and many more.

Reference:

1. “Hadoop – Introduction to Hadoop.” Www.tutorialspoint.com, Tutorials Point, available here.
2. “Introduction to Apache Spark.” Www.tutorialspoint.com, Tutorials Point, available here.

Courtesy image:

1. “Apache Hadoop Elephant” By Intel Free Press (CC BY-SA 2.0) via Flickr
2. “Spark Java Logo” By David Åse – Own work (CC BY-SA 4.0) via Commons Wikimedia

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