Why Is Apache Spark Gaining Traction For Big Data Analytics?

Inception:

Big Data is moving forward at lightning speed. The emergence of the new technology promises to manage and analyze the large volume of data in more robust and scalable manner and this is only possible with cheaper implementation and maintenance cost. Among the list of recent tools, Apache spark- Which is an open source computing platforms adds more value as compared to other tools.

Being one of the most powerful processing engines for Hadoop – It’s a cluster computing framework built by AMPLab in 2009 later on which became open sourced in 2010. At a later date, Apache Spark was renowned as one of the largest open source community in big data that cherishes 200+ contributors from over 50 communities. It holds a wide number of features and functionalities that make processing and data management easy and hassle free. But, before diving deep inside this scalable and open source technology, let’s find out what exactly Apache Spark is and how it works.

What is Apache Spark?

Apache Spark is basically a parallel data processing framework that works well with Apache Hadoop to foster easy and fast mobile app development. It also allows easy data streaming, interactive analysis and batch processing on all data of big data applications.

Apache Spark with Enterprise Adoption:

The release of Apache Spark observed a huge enterprise adoption by a wide range of business and enterprise. These adoption has not only benefitted multiple organizations but also assisted big brands like Yahoo, Netflix, and eBay to collect an enormous range of data from over 8000 nodes. Now the question arises is:

Why Is Apache Spark Highly Adopted For Big Data Analysis And Processing?

There are numerous advantages of big data which makes it attractive for big data framework. They are as below:

1. Fast Processing:

In the realm of big data, speed is of paramount importance. Spark, with the assistance of a Microsoft Gold partner, empowers applications to operate at speeds up to 100 times faster within Hadoop clusters and 10 times faster when utilizing disk storage. This is achieved through the ingenious concept of Resilient Distributed Data Sets (RDDs), enabling seamless data storage in memory and efficient disk persistence as required. This transformative approach significantly minimizes disk read and write times, thereby optimizing the data processing workflow.

2. Unified Platform For Data Management:

Apache Spark is considered as the unified platform that manages all the operations. Let’s see how Apache spark can manage data efficiently.

1. Spark SQL:

By using SQL language and API, it enables app developers to query structured data which can be used with Java, Scala or Python. It also allows the developers to build and run applications in Spark.

2. Spark Streaming:

Unlike Map reduces that processes data in batches, Spark manages a larger volume of data in real time scenario. This process streamlines the entire process of data analyzation for management.

3. Machine Learning:

This tool contains a list of algorithms that offers many utilities to Apache Spark. These list of utilities includes vector machines, latent Dirichlet allocation, Bayesian regression tree and much more.

3. Real Time Stream Processing:

Spark helps in analyzation of real-time data when collected. When the data is collected, Spark helps in analyzation of data. Spark excellently handles real-time data streaming when it modifies the data. Certain high-ended applications like fraud detection, log processing in live data streams and electronic trading are gaining lot of benefits from Spark. Spark consist of robust and lightweight API that allows easy and swift development of the streaming application.

Apart from this, Spark also supports machine learning algorithm for future predictions along with supporting languages like Java, Python and Scala. Let’s watch how modifications and upgradation of technology in Spark – One of the best business intelligence solutions for big data improves data processing and analysis in future.