Spark companies in Argentina

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

Spark core

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

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

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.

Spark MLlib

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

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

Swift Processing

Dynamic in Nature

In-Memory Computation in Spark


Fault Tolerance in Spark

Real-Time Stream Processing

Support Multiple Languages

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