Businesses are all about processing big data. In order to make this process easier for you, we have introduced Spark. Let’s take a deep dive into Spark and learn how it helps your business.

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What is Spark computing engine?

Spark is a “Cluster Computing Engine” originally introduced by Apache. This engine is specifically designed to compute big data in a small fraction of time. This fast computing engine is based on Hadoop.

Spark offers an array of amazing features that include stream processing as well as interactive queries.

That’s not all. The memory cluster of spark improves the speed of the applications to make the process of data computing easier for you.

Components of Spark Ecosystem:

Two major features of Spark include quick computation as well as easy development. However, these two are impossible without the proper components. These components of the Spark Ecosystem include:

  • Spark Core: Spark core supports all the functionalities of Spark. It is basically the fundamental processing and execution engine. Spark Core is also referred to as the external storage system’s datasets. It offers an array of in-memory computation features.
  • Spark SQL: The Spark core component of Spark offers data abstraction. This abstraction is also known as Schema RDD. Spark SQL is capable of supporting both structured and unstructured data.
  • Spark Streaming: Spark streaming enables real-time data processing. This component of spark performs streaming analytics. The process of data computing is done by dividing the data into small batches. Spark streaming also performs Dstream (a series of RDDs). Real-time streaming is performed via Dstream.
  • MLib: MLib is also known as Machine Learning Library. It is basically Spark’s machine learning framework. There are two major components of this model namely, learning utilities as well as algorithms. There are several functions performed by this library. These functions include classification, regression, clustering and many more. MLib also improves In-memory data processing. This, in turn, increases the performance of the iterative algorithm.
  • GraphX: GraphX is the component of Spark that operates on the top of the Spark framework. It is the distributed graph computing model that enhances the rate of large-scale data processing.
  • SparkR: Spark and R combine together to form SparkR. This component explores a variety of techniques. The functionality of Spark is improved by combining the R operations with the scalability features of Spark.

Focus on the major aspects of your business. Offshore Software Solutions offer Spark to take care of your big data & help you grow.

How Spark Operates?

Spark offers RDD also known as Resilient Distributed Datasets. RDDs are the basic unit of data. These are a group of data sets distributed over a range of cluster nodes. They support parallel operations which are incontrovertible otherwise. There are three ways by which RDDs can be created in Spark. These include:

  • By external datasets
  • Through parallel collections
  • By existing RDDs

Some of the major operations performed by RDD include:

  • Transformation
  • Action


There are no changes that can be made to RDDs. However, they can be transformed. The transformed RDDs result in the formation of new RDDs. Some of the transformations of RDD include:

  • Map
  • FlatMap
  • Filters

Action: Spark reduces the action operations. This offers new value which is added to the external datasets.

How Spark Helps Your Business To Grow?

  • High-Speed data computing: Businesses with big data require fast speed data execution. This is what Spark offers. The computing speed of Spark is 100 times better than Hadoop MapReduce. This is what makes it the ideal option for businesses that deal with large-scale data. It achieves this speed using controlled partitioning. When parallel distributed data is partitioned, it can easily be processed even within minimal traffic.
  • Multiple Formats: Spark database supports a range of formats including RDBMS tables and CSV from multiple data sources like JSON, Hive, and Cassandra. Moreover, the Data Source API of Spark SQL offers a plugable mechanism to make access to structured data easier.
  • Developer friendly: Spark is capable of supporting a variety of languages to build applications. These languages include Java, Python, Scala, R and etc. The APIs mask the complexity of computing with easy to use yet high-level operators. In this way, it reduces the number of codes needed.

  • Real-Time processing: Spark is the ideal computing engine for the businesses that require massive scalability. It can easily support businesses with large data cluster having various nodes and processing models.
  • Hadoop compatible: Offshore Software Solutions Spark is highly compatible with Hadoop. For anyone who has started his career with Hadoop can easily operate Spark. This is because Spark is the MapReduce replacement of Hadoop. It can easily be operated on Hadoop cluster to perform resource scheduling with the help of YARN.

Offshore Software Solutions will take your business to the new height. Contact us today on for the best in class business solutions.