When it comes to choosing a top Spark company, it is important to look for the level of comfort, expertise and prior experience of the developer. There are some key considerations to keep in mind before deciding on your choice. Spark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.
Spark is a general purpose computing platform and is an open source, wide range data processing engine. It can perform batch processing and stream processing. Batch processing refers to the processing of the previously collected job in a single batch Whereas stream processing means to deal with Spark streaming data. It integrates with all Bigdata tools and can run on Hadoop clusters.
Given below are the listing of spark components
It supports a wide array of applications, Spark Provides a generalized platform. Spark core provides an execution platform for all the Spark applications.
Spark SQL enables users to run SQL/HQL queries. We can process structured as well as semi-structured data, by using Spark SQL.
Spark Streaming enables a powerful interactive and data analytics application. The live streams are converted into micro-batches those are executed on top of spark core.
Machine learning library delivers both efficiencies as well as the high-quality algorithms. It is capable of in-memory data processing, that improves the performance of the iterative algorithm.
Spark GraphX is the graph computation engine built on top of Apache Spark that enables to process graph data at scale.
The R package gives light-weight frontend. Moreover, it allows data scientists to analyze large datasets. It allows running jobs interactively on them from the R shell.
Here are some of the features of Apache Spark
Dynamic in Nature
In-Memory Computation in Spark
Fault Tolerance in Spark
Real-Time Stream Processing
Support Multiple Languages
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