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When your business decides to develop a software application or a software product, its IT team or IT consulting partner must choose an appropriate development environment to support the needs of the project and plan for scalability, performance and upgrades. Many developers like the Apache Spark tools and libraries and supporting environment and there is good reason to choose Spark for your project.
When you are contemplating a new software product development project or a software application development initiative, your team may benefit from expert IT consulting. There are many considerations and decisions to be made to successfully execute this type of project and one of them involves the type of technology, architecture, development frameworks and tools you will use to best meet your requirements.
No one development environment is right for every software development project. The Apache Development environment provides numerous benefits for many types of projects. In our previous article, entitled, ‘Four Important Advantages of Apache Spark’, we discuss some important advantages of the Apache Spark development option.
Spark is a distributed open-source cluster-computing framework and includes an interface for programming a full suite of clusters with comprehensive fault tolerance and support for data parallelism. Spark can be used effectively to provide support for Java, Scala, Python and R programming and is suitable for SQL, streaming data, processing graphs and for machine learning.
If you are a developer, contemplating a software development project that must support Big Data, a large user base and/or multiple locations, Apache Spark should definitely be on your short list of considerations for a computing framework. In this article, we look at three reasons you should use Apache Spark in your Big Data projects.
