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Apache Spark Review

Apache Spark OUR SCORE 98%
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our score 98%
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  1. What is Apache Spark
  2. Product Quality Score
  3. Main Features
  4. List of Benefits
  5. Technical Specifications
  6. Available Integrations
  7. Customer Support
  8. Pricing Plans
  9. Other Popular Software Reviews

What is Apache Spark?

Apache Spark is an intuitive, fast, and centralized analytics engine capable of processing huge amounts of data. It’s an open-source project that was first created by a group of developers hailing from over 300 companies and up to now, many developers are still investing their time and effort for enhancing the project. It’s a highly rapid analytics engine making it the preferred option by many organizations when it comes to data processing solution that could deal with huge datasets. That is because even during high volume processing, it can still quickly perform real-time and batch data processing with the help of its stage oriented DAG (Directed Acyclic Graph) scheduler, physical execution engine, and query optimization tool. Furthermore, Apache Spark is lined with libraries that can be joined together easily in one application. Said libraries include SQL module that is used to query structured data inside the programs that run the system. There’s a library designed for creating applications that are capable of executing stream data processing, another one for machine learning that uses fast and high-quality algorithms, and then there’s an API for performing graph parallel computations and processing graph data.

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Apache Spark features

Main features of Apache Spark are:

  • Graph Processing System
  • Usable in Java, Scala, Python, and R
  • Spark Streaming
  • Mix SQL Queries
  • Stack of Libraries
  • High-Quality Algorithms
  • Standalone Cluster Mode
  • Machine Learning
  • Spark SQL
  • Uniform Data Acess
  • High-Level Streaming Operators
  • DataFrame API
  • Graph Operators and Algorithms

Apache Spark Benefits


The main benefits of Apache Spark are its generality feature, SQL module used to query structured data, uniform way of data access for multiple sources, faster stream data processing, high-quality machine learning algorithms, and easy graph analytics and computation. Here are more details:

Generality Feature

One of the most powerful features of Apache Spark is the generality. Built with a wide array of capabilities and features, it empowers users to implement various types of data analytics that they can aggregate in one tool. The unified and open-source analytics engine covers all the required processes, from performing SQL based analytics up to complex analytics.

SQL Module for Easy Querying of Structured Data

Apache Spark can be used as a general purpose analytics platform which is why it delivers a group of libraries that can be integrated into one application. One of which is the module named Spark SQL that users can utilize to write as well as execute SQL queries so that they will be able to work and process on structured data within the programs related to Apache Spark.

Standardized Way of Accessing Data from Different Sources

With the use of DataFrame API and SQL queries, users are able to establish a standard and uniform way of data access that comes from multiple data sources. This means that regardless of how diverse the sources where data are collected from, Apache Spark makes sure that users can apply a common method for connecting to the sources.

Accelerated Stream Data Processing

Apache Spark is equipped with a component that is built specifically for accelerating stream data processing called Spark Streaming and is one of the available libraries in the system. Spark Streaming enables users to connect different data sources as well as access real-time data streams. After which, the analytics engine processes the real-time input data streams with the help of complex algorithms and then generates a live output of data streams.

High-level Machine Learning Algorithms

Another highlight of Apache Spark is the usage of high performing algorithm contained within the machine learning library or MLlib. Using these algorithms, users are able to execute and implement computational tasks and jobs a hundred times faster than Map/Reduce. The high-quality algorithms can work seamlessly on R libraries, Python, Scala, and Java and offer high performing iteration capabilities as well.

Easy Graph Computation and Analytics

Apache Spark gives users a graph processing system, called GraphX, which empowers a more intelligent and efficient performance of graph computation and analytics works within one tool. Using GraphX, users are able to visualize the data they have as graphs, then convert the collection of edges and vertices into a graph. After that, graphs are restructured and transformed into new graphs, and finally, combine them together.

Technical Specifications

Devices Supported

  • Web-based
  • iOS
  • Android
  • Desktop

Customer types

  • Small business
  • Medium business
  • Enterprise

Support Types

  • Phone
  • Online

Apache Spark Integrations

The following Apache Spark integrations are currently offered by the vendor:

  • Apache Hadoop
  • Apache Mesos
  • Apache Cassandara
  • Kubernetes
  • Apache Hive
  • Apache HBase
  • EC2
  • Hadoop YARN
  • HDFS (Hadoop Distributed File System)


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Pricing Plans

Apache Spark pricing is available in the following plans:

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