What makes Spark the best Apache project? Advertised as “lightning fast cluster computing”, Spark is a processing framework for Big Data.
What sets it apart from other tech like Hadoop and Storm is its speed, analytical tools and ease of use. No wonder it has garnered one of the highest user bases today.
Apache Spark Support For Big Data Processing
This gives it an added advantage. It is an open source program and today Apache Spark support boasts of the biggest support community in the category.
This is an active and thriving community where every topic is discussed and innovative solutions found. For a user, this support community can be critical.
The Apache Spark Support
With one of the most complete and unified Big Data frameworks, Spark makes the data manageable, easy to understand study and analyse.
It allows one to work with different data sources and formats — from graphs to texts. It also supports a number of languages, including Scala, Java and Python.
Speed continues to be its USP as it is still faster than any other data processing platform. It allows you to run programs faster than any other platform, while using less machine power.
In fact, it has remained consistently ahead of Hadoop by making its processes lightening fast and maintaining its impressive efficiency.
In-memory data storage
The in-memory data storage with an almost real-time processing makes Spark’s performance remarkably fast. It will first attempt to store data in memory and only then move to disk.
Since it is designed to work both in-memory and on-disk, it will first store part of the data in memory and then store the remaining data on the disk. This gives quick results and a definite performance improvement.
It also make code writing impressively fast. Where other platforms may need reams of coding, Spark will often work with just a few lines. And let us not forget REPL. You no loner have to execute the entire job to test a single line of code. Coding is fast and analysis possible at any given time.
What adds to the Apache Spark support are its impressive libraries. These libraries give spark its thriving and supportive ecosystem. These include Spark Streaming, SparkSQL, GraphX, and MLlib.
Spark Streaming is used for processing streaming data. GraphX is concerned with graphs computations. MLlib is concerned with mining, while Spark SQL allows culling data from different formats and ready it for mining.
Written in the Scala Programming Language, Spark supports a number of languages including, Scala, Python, Java, R and Clojure. It is run on the Java Virtual Machine (JVM) environment.
This makes it easy to use, even for developers who may not be as proficient in its programming language, Scala.
Spark Streaming (the library mentioned above) can be really handy in manipulating data in real-time. Its own powerful APIs lets developers build up this ability quickly. It also allows a developer to recover from mistakes.
A thriving support community
As even a layman can tell you, software support communities can be crucial for many reasons. Yes, you can probably air your own problems and get someone to respond, but the chances are that a simple Google search will see both the question and a number of answers.
Chances are that someone has already faced your problem and posted it. You will also find a number of innovative answers, some of which would never occur to you!
Spark was built by over 250 of developers spread over 50 countries! Not only does it has an interactive and informative mailing list, it also has JIRA for tracking any issues. In practice this gives Spark the following advantages:
- A quick response from the many users and interaction boards.
- Readily available answers that can be accessed with a minutes’ Internet search.
- A connected and interactive community.
- An informative mailing list that helps you keeps updated, backed by an equally helpful community.
Spark has emerged as one of the most comprehensive and evolving framework to store and analyse data. It can work with a wide set of data, whether structured or instructed, stored, archived or real-time.
Apache Spark Support also boasts of a massive community of developers and users who have further made it accessible to everyone.
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