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Apache Spark REVIEW

Data Analytics Software

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USER SATISFACTION 97%
OUR SCORE 9.8

What is Apache Spark?

Apache Spark is an easy-to-use, blazing-fast, and unified analytics engine which is capable of processing high volumes of data. It is an open source project that was developed by a group of developers from more than 300 companies, and it is  still being enhanced by a lot of developers who have been investing time and effort for the project. As a lightning-fast analytics engine, Apache Spark is the preferred data processing solution of many organizations that need to deal with large datasets because it can quickly perform batch and real-time data processing through the aid of its stage-oriented  DAG or Directed Acyclic Graph scheduler, query optimization tool, and physical execution engine.

Apache Spark, moreover, is equipped with libraries that can be easily integrated all together in a single application. These libraries include an SQL module which can be used for querying structured data within programs that are running Apache Spark, a library designed to create applications that can execute stream data processing, a machine learning library that utilizes high-quality and fast algorithms, and an API for processing graph data and performing graph-parallel computations. Apache Spark is also a highly-interoperable analytics solution, as it can seamlessly run on multiple systems and process data from multiple sources. It can be deployed to a single cluster of servers or machines using the standalone cluster mode as well as implemented on cloud environments.

Overview of Apache Spark Benefits

Generality: Perform SQL, Streaming, And Complex Analytics In The Same Application

Generality is among the powerful features offered by Apache Spark. It is built with a broad range of features and capabilities that allow users to perform different types of data analytics which they can even combine in a single tool. Whether they are doing SQL-based analytics, stream data analysis, or complex analytics; the open source and unified analytics engine covers all of them.

Easily Work On Structured Data Using The SQL Module

Being a general-purpose analytics solution, Apache Spark delivers a stack of libraries that can be all incorporated into a single application. One of these libraries is a module called Spark SQL. With this module, users will be able to write and execute SQL queries so they can process and work on structured data within Apache Spark-related programs.

Take Advantage Of The DataFrame API

Aside from providing the ability to run SQL queries, Spark SQL uses a DataFrame API which is used for collecting data from various data sources such as Hive, Avro, Parquet, ORC, JSON, and JDBC; and organizing them in a distributed manner. This distributed collection of data is called a DataFrame. A DataFrame is a data set which  is arranged and structured into labelled or named columns. For users who are familiar with the relational database management system, DataFrame is similar to the table being used in such system. It is also equivalent to a data frame in R/Python.

Uniform And Standard Way To Access Data From Multiple Sources

So what’s the importance of using SQL queries and the DataFrame API? Basically, this enables users to establish a uniform and standard way of accessing data from multiple data sources. In other words, no matter how diverse the data sources they are collecting data from, Apache Spark ensures that they are able to apply a common method to connect to such sources and access all the data they need for analysis.

Supports Both Batch Data And Real-Time Data Processing

Apache Spark is an analytics engine which can handle both batch data processing and real-time data processing. Batch data processing is a big data processing technique wherein a group of transactions are gathered throughout a period of time. Then, the input data from this set of transactions are processed and batch results are generated. This technique normally requires a longer time. Thus, insights are not produced immediately, as users need to wait first until such time that all the transactions in the batch are processed.

On the other hand, real-time data processing, which is also referred to as stream data processing or real-time analytics, maintains a continuous flow of input, process, and output data, thereby allowing users to gain insights into their data within a small period of time. This data processing technique enables organizations and teams to spot issues and problems immediately and address and solve them as quickly as possible.

Stream Data Processing

Luckily, Apache Spark has component exclusively built to accelerate stream data processing This component is called Spark Streaming, and it is among the libraries available in Apache Spark. Spark Streaming lets users connect to various data sources and access live data streams. Then, the analytics engine processes the live input data streams through the aid of complex algorithms and generates live output data streams. The output or processed data can be extracted and exported to file systems, databases, and live dashboards.

Built Interactive, Scalable, And Fault-Tolerant Streaming Applications

With Spark Streaming, users will be able to create streaming applications and programs that are scalable, fault-tolerant, and interactive. As they build such applications, they can write and activate streaming jobs and tasks within the applications using high-level operators. In addition, this component of the analytics engine permits them to write and run the same codes which they can reuse for batch data processing, enabling them to run ad-hoc batch data queries against live data streams and apply real-time analytics to historical data.

High-Quality Machine-Learning Algorithms

Another great feature of Apache Spark is its utilization of powerful and high-performance algorithms which are contained in a machine learning library known as MLlib. With these algorithms, users can implement and execute computational jobs and tasks which are 100 times faster than Map/Reduce, a computing framework and paradigm which was also developed by The Apache Software Foundation for distributed processing of large data sets. These high-quality algorithms can seamlessly work on Java, Scala, Python, and R libraries; and offer high-level iteration capabilities. As a result, users will be able to process and analyze data more accurately and quickly.

Graph Analytics And Computation Made Easy

Apache Spark provides a graph processing system that makes it easy for users to perform graph analytics tasks. But what is graph analytics all about? Graph analytics is a type of data analysis method that allows users to explore and analyze the dependencies and relationships between their data by leveraging the models, structures, graphs, and other visualizations that represent those data. In other words, it enables them to analyze graph data.

Apache Spark’s graph processing system called GraphX permits users to efficiently and intelligently perform graph analytics and computation tasks within a single tool. Here, they can visualize their data as graphs, convert a collection of vertices and edges into a graph, restructure graphs and transform them into new graphs, and combine graphs together. This system is also built with graph operators which provides users with the capability to manipulate and control graph data in multiple ways. Furthermore, GraphX is equipped with graph algorithms that simplify how they apply analytics to graph data sets and identify patterns and trends in their graphs.

Overview of Apache Spark Features

  • Generality
  • Combine SQL, Streaming, and Complex Analytics
  • Run Workloads 100 Times Faster
  • Ease of Use
  • Runs Everywhere
  • Standalone Cluster Mode
  • Deploy to Cloud Environments
  • Stack of Libraries Which Can be Combined in The Same Application
  • Spark SQL
  • Mix SQL Queries with Spark Programs
  • Uniform Data Acess
  • DataFrame API
  • Spark Streaming
  • Build Scalable and Fault-Tolerant Streaming Applications
  • High-Level Streaming Operators
  • Combine Streaming with Batch and Interactive Queries
  • Machine Learning
  • High-Quality Algorithms
  • Usable in Java, Scala, Python, and R
  • Seamlessly Work with Both Graphs and Collections
  • Fast Graph Processing System
  • Graph Operators and Algorithms

Apache Spark Position In Our Categories

Position of Apache Spark in our main categories:

5

Apache Spark is one of the top 5 Data Analytics Softwareproducts

5

Apache Spark is one of the 5
Data Analytics Software products


If you are interested in Apache Spark it might also be sensible to analyze other subcategories of Best Data Analytics Software gathered in our base of SaaS software reviews.

Every enterprise is different, and may require a specific Data Analytics Software solution that will be adjusted to their company size, type of customers and employees and even specific industry they deal with. We advise you don't count on finding a perfect app that will work for each business regardless of their history is. It may be a good idea to read a few Apache Spark reviews first and even then you should keep in mind what the service is intended to do for your company and your workers. Do you need a simple and straightforward solution with only elementary features? Will you actually use the complex tools required by pros and big enterprises? Are there any particular features that are especially useful for the industry you operate in? If you ask yourself these questions it is going to be much easier to find a solid service that will fit your budget.

How Much Does Apache Spark Cost?

Apache Spark Pricing Plans:
Free trial
Apache Spark
Free

Apache Spark is delivered based on the Apache License, a free and liberal software license that allows you to use, modify, and share any Apache software product for personal, research, commercial, or open source development purposes for free. Thus, you can use Apache Spark with no enterprise pricing plan to worry about.

User Satisfaction

We realize that when you make a decision to buy Data Analytics Software it’s important not only to see how experts evaluate it in their reviews, but also to find out if the real people and companies that buy it are actually satisfied with the product. That’s why we’ve created our behavior-based Customer Satisfaction Algorithm™ that gathers customer reviews, comments and Apache Spark reviews across a wide range of social media sites. The data is then presented in an easy to digest form showing how many people had positive and negative experience with Apache Spark. With that information at hand you should be equipped to make an informed buying decision that you won’t regret.

POSITIVE SOCIAL MENTIONS

114

NEGATIVE SOCIAL MENTIONS

2

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Technical details

Devices Supported
  • Windows
  • Linux
  • Mac
  • Web-based
Language Support
  • English
  • Chinese
  • German
  • Hindi
  • Japanese
  • Spanish
  • French
  • Russian
  • Italian
  • Dutch
  • Portugese
  • Polish
  • Turkish
  • Swedish
Pricing Model
  • Free
Customer Types
  • Small Business
  • Large Enterprises
  • Medium Business
Deployment
  • Cloud Hosted
  • On Premise
  • Open API

What Support Does This Vendor Offer?

  • EMAIL
  • PHONE
  • LIVE SUPPORT
  • TRAINING
  • TICKETS

What are Apache Spark pricing details?

Apache Spark Pricing Plans:
Free trial
Apache Spark
Free

Apache Spark is delivered based on the Apache License, a free and liberal software license that allows you to use, modify, and share any Apache software product for personal, research, commercial, or open source development purposes for free. Thus, you can use Apache Spark with no enterprise pricing plan to worry about.

What integrations are available for Apache Spark?

Apache Spark integrates with some open source projects developed by The Apache Software Foundation as well as with third-party systems such as the following:

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

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