Difference Between Hadoop and Spark

The Hadoop framework is open-source that has the ability to expand computation and storage. A spread environment across a host of computers lets you store and process big data. As an alternative, Spark is an open-source clustering technology. It was designed to speed up computing. This product enables whole program clusters that are fault tolerant and implicitly parallel. The prime characteristic of Spark is in-memory cluster computing, which improves an application's speed. These technologies have some similarities and differences, so let's briefly discuss them.

What is Hadoop?

In the year of 2006, Hadoop began as a Yahoo project. A later version of the project was turned into a top-tier Apache project. To coordinate operations among clusters, it employs a simple programming model. Every module in Hadoop is created with a fundamental theory that hardware failure is a usual incident and that it needs to be dealt with by the framework.

The MapReduce algorithm processes the data in parallel. The Hadoop framework can sufficiently develop applications that run on clusters of systems.

Hadoop's core has a storage section called the Hadoop Distributed File system and a processing section known as the MapReduce programming model. Hadoop separates files into big blocks and spreads them across the clusters.

Hadoop Yarn is another module used for scheduling and coordinating application runtimes. Hadoop is created in Java, so it is available through many programming languages to write MapReduce code. It is accessible either open-source from the Apache distribution or sellers like–MapR, Hortonworks, or Cloudera.

What is Spark?

Spark was first developed in 2012, making it a recent project. The developers made it on top of the Hadoop MapReduce module. The MapReduce model is extended to accommodate interactive queries and stream processing using other computations. Apache introduced Spark, an open-source project for processing data in parallel across clusters in memory.

Spark comprises its cluster version of Hadoop. In storing and processing, it uses Hadoop. It has many features, by upgrading specific modules and integrating new modules. This enables an application to run in memory much faster in a Hadoop cluster.

This happens because it lowers the read/write operation to disk. Keeping the intermediate processing data in memory saves the read/write operations. An end-user can write applications in various methods as Spark gives built-in APIs in Python, Scala, and Java. Various libraries that work on top of Spark core, including machine learning, SQL queries, Streaming data, and Graph Algorithms.

Difference between Hadoop and Spark

Following are the major differences between spark and Hadoop

Hadoop	Spark
An open-source framework that uses the MapReduce algorithm.	A lightning-fast cluster computing technology that effectively extends the MapReduce model to use various computation methods.
The MapReduce model uses read/write operation, resulting in a slower processing speed.	It provides faster-processing speed by lowering the number of reading/writing operations to disk.
Created to operate batch processing effectively.	Created to operate real-time data effectively.
Hadoop offers high latency computing.	Spark offers low latency computing.
Comprises an interactive environment.	Does not have an interactive environment.
Data can only be processed in batch mode.	Capable of processing real-time data.
Hadoop is cheaper in terms of cost.	Spark is expensive.

Conclusion

Hadoop permits the parallel processing of large amounts of data. It splits large data into smaller ones to be processed individually on various data nodes. Then it automatically collects the results from all over the multiple nodes to give back a single result. Hadoop may surpass Spark if the data result is larger compared to the available RAM.

Talking about Spark, it is user-friendly as it comprises user-friendly APIs. It is simple for customers to clarify their framework for data processing, as it gives a way to carry out streaming, machine learning, and batch processing in the same cluster.