Features and Benefits of Apache Spark

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Features and Benefits of Apache Spark

Apache Spark Framework is latest buzzword in the field of Big Data processing and this tutorial we are going to learn the features offered by this framework.

Apache Spark Framework is latest buzzword in the field of Big Data processing and this tutorial we are going to learn the features offered by this framework.

Features and Benefits of Apache Spark

Suddenly Apache Spark became the buzzword for people in Big Data space. Why it is so? Why there is so much buzz on this new data computing technology? Being a person in Big Data analytics business how much should you care about it and to what extent? Is Apache Spark is really steps ahead of other Big Data technologies like Hadoop? By explaining the features and benefits of data processing engine we would find replies for these questions.

Apache Spark is an open source high speed data processing engine for Hadoop which is built to deliver unparalleled speed, easy to use, and sophisticated data analytics. Originally conceptualized and developed in University of California Berkeley's AMPLab and later licensed under Apache, it added immense value to global data processing and app development. Apache Spark can work as a parallel data processing framework with Apache Hadoop offering extreme ease in developing fast, performance driven Big Data applications.

Apache Spark vs. Hadoop MapReduce

Spark utilizes more random memory instead of network and disk memory and consequently offers faster processing than Hadoop. The RAM based in-memory data storage of Spark is different from the Hadoop storage of data on disk. While Hadoop for achieving fault tolerance depends on replication, Spark uses a variety of data storage model including resilient distributed datasets or RDD. It offers a more fault-tolerant processing equipped with in-memory cluster computing.

Features and Benefits of Apache Spark

Lighting Fast Processing

In Big Data processing speed takes the central place of importance. Fast processing of huge data sets is the priority here. Spark makes applications in Hadoop clusters capable to run with 100 times faster speed in memory and 10 times faster while running on machine disk. This is achieved by Spark simply by reducing the number of read and write actions to disc. By storing intermediate processing data in-memory Spark ensures highest speed. The Resilient Distributed Dataset (RDD) model in Spark allows transparent storage of data in memory that only requires using disk space when absolutely needed. Naturally, by minimizing the disc read and write actions to a significant level the principal time consuming factor in data processing can be addressed.

When Spark is deployed on the MapReduce converged data platform it ensures optimum performance for an array of data applications. By the creation of object collections termed as Resilient Distributed Datasets (RDD) which take the space across the cluster, Spark facilitates rapid transformation and sharing of data across various applications.

Rapid and dependable application development

Simple and easy-to-use programming APIs provided by Spark makes it easy to build applications at a rapid speed in languages such as Java, Python or Scala. Moreover, for developing rapid prototypes and workflows for the purpose of rescuing code across batch, interactive and streaming applications, Spark can be deployed advantageously by data scientists and developers.

Spark applications and frameworks such as Spark SQL, Spark Streaming, GraphX and MLLib offers enterprise grade features and performance. These apps and frameworks can run on a continuous basis across diverse production environments and can leverage the benefits of faster data driven processing and accessibility.

More flexibility with support for multiple languages

Using Spark developers can quickly write applications in most developer friendly programming languages like Java, Scala or Python. This allows creating and running apps in familiar programming languages with the advantage of rapid development. Moreover, for querying data interactively inside the shell it comes loaded with an in-built set of more than 80 high level operators.

Support for sophisticated analytics

Spark rose to popularity among data scientists and developers primarily for its capability of sophisticated analytics. Besides providing support for so called map and reduce operations, Spark supports an array of advanced data analytic operations including SQL queries, data streaming and procedures pertaining to complex analytics like machine learning, graph algorithms etc. Furthermore, within a single workflow the Spark users are capable to combine all these attributes.

Real Time Stream Processing

Real time streaming is a great advantage of Spark. While MapReduce is responsible for handling and processing already stored data Spark Streaming allows manipulation of real time data. While for streaming data in Hadoop you need to integrate other frameworks, Spark can do this besides offering other all-round advantages.

  • Using Spark streaming is easy as it is built on lightweight but power packed APIs. The ease of use facilitates rapid development of streaming applications.
  • Spark streaming is exceptionally tolerant to faults in comparison to other streaming frameworks. From recovering lost work to delivering the identical semantics, it offers unparalleled fault tolerance capability.
  • Spark offers better integration by allowing batch and stream processing and by joining streaming data to the historical data sets.

Easy integration with Hadoop and pre-existing Hadoop data

Besides the capacity to run independently, Spark also can run on existing Hadoop cluster manager and already existing Hadoop data. It is capable to read from any Hadoop data source like HBase, HDFS etc. Naturally as far as migration of existing Hadoop applications is concerned Spark will not be an obstacle but will prove to be an aiding factor.

Active and Expanding Community

Apache Spark is developed and backed by a global developer community. Apache Spark including its core and the frameworks were conceptualized and built over the years by developers representing more than 50 developer companies. Such robust backing of developers worked as a big impetus for it to rise in popularity.

Use cases: How Spark can be utilized for diverse applications and operational benefits?

Let us now have a look at some of the common user cases where deployment of Spark has been very successful and fruit bearing.

High speed batch applications

High speed in-memory computing capability of Spark allows deploying fast paced batch applications in various production environments. Furthermore, the ease of maintaining codes adds to the advantage even more.

Complex ETL Data Pipelines

Complex ETL pipelines now can be built by utilizing Spark. With the utilization of Spark stack streaming, machine learning and sql operations can be merged and pipelined within just one program.

Real-time operational analytics

Real time operational analytics is an area that has gained unprecedented focus and is now crucial part of deploying Big Data technologies. From building real time operational dashboards to developing time sequenced analytics on high speed data sets, Spark can be successfully utilized for all these data analytics by leveraging MapReduce DB or HBase or Spark Streaming functionality.

What is Apache Spark Framework?

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