Apache Spark software consists of two main components on the basic level: a driver which transforms the user’s code into several tasks which can be spread across worker nodes and executors that run on and carry out the assigned tasks at those node levels. To mediate between the two, some sort of cluster manager is needed.

1. What is Apache Spark?
Apache Spark is a large-scale data processing consolidated analytical engine. Apache Spark is a data processing system that can
perform very large data sets computing tasks easily and can also spread data processing tasks on several machines, on their own,
or in combination with other distributed programming resources. They are important to big data and machine learning.
Apache Spark software consists of two main components on the basic level: a driver which transforms the user’s code into
several tasks which can be spread across worker nodes and executors that run on and carry out the assigned tasks at those node
levels. To mediate between the two, some sort of cluster manager is needed.
Features which make Spark one of the big data platforms most widely used are:
It provides advanced analytical support:
Spark not only supports basic ‘maps,’ but supports even advanced analysis, ML and graph algorithms, and supports SQL queries,
streaming of datasets, and advanced analytics. It features a versatile stack of libraries such as Machine Learning, graphs, and
MLlib SQL & DataFrames. What is interesting is that Spark allows all these libraries to be integrated into a single workflow.
Easy Usage:
Spark can be used in Java, Scala, Python, and R for scalable applications. Developers, therefore, have the ability to build and run
Spark apps in their favorite programming languages. In addition, Spark has an interconnected range of more than 80 operating
companies. You can use Spark for Scala, Python, R, and SQL shells to query knowledge interactively.
Fast processing speed:
The processing of vast quantities of structured data requires all big data analysis. Therefore, as far as large data processing is
concerned, companies and organizations want such a framework that massive data can be processed at high speed. Spark apps
can operate on disk in Hadoop clusters up to 100 times faster and 10 times faster. It uses the Resilient Distributed Dataset to
make it possible for Spark to store memory data transparently and to only record if required. This can reduce the reading and
writing time of most disks during data processing.
Flexibility:
Spark can run separately or on Hadoop YARN, Apache Mesos, Kubernetes, and even on the cloud. In addition, various data
sources can be accessed. For example, Spark will run on the YARN cluster manager and read any Hadoop data already available.
The data sources like Hbase, HDFS, Hive, and Cassandra can be read from any Hadoop info. Spark is an excellent method for
migrating pure Hadoop software, whether the code is spark-friendly.
Processing of real-time sources:

2. Spark is designed to process data streaming in real-time. Although MapReduce is constructed to manage and process data
already stored in Hadoop clusters, Spark can handle and manipulate data on Spark Streaming in real-time. In comparison to
other streaming approaches, Spark Streaming restores lost work and provides the exact semantics of the out-of-the-box without
needing additional code or configuration. It also helps you to reuse the same batch and stream code and even to add stream data
to historical data.
Conclusion: Spark is an incredibly multifaceted big data platform with an amazing functionality. Since it’s an open-source
platform, it actively enhances and expands with additional functionality and functionalities. For the diversification and extension
of applications in big data, Apache Spark will use cases.
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krishna-kumar-learnbay
August 25, 2020
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