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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.
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.
If your business is like most, you have a lot of different data repositories and your data is buried in best-of-breed, legacy systems, data warehouses, the cloud and many other data storage areas. Sometimes it seems like all these data sources are inaccessible and that your data is hiding from even your most gifted analysts and IT staff.
Even if you do not work in IT, you have probably heard the term ‘Big Data’. These days, Big Data can cause big problems. If you do not have a plan to manage all that data and to make it easily accessible to your users, the data may as well not exist. If you cannot use that data for predictive analytics, you cannot accurately forecast your business results, plan for growth or compete in the market.
What is Apache Spark? The Apache Spark framework includes Spark Core to manage memory and interact with storage systems, Spark Streaming to process live data streams, Spark SQL supporting SQL with HiveQL, MLlib supporting machine-learning algorithms, regression, clustering and filtering, and GraphX supporting graph manipulation and computations. This framework makes it easier to stream data and to quickly process analytics and algorithms, so your applications will run faster and your enterprise can manage Big Data and high volume data.
You may think your business is unique (and it is) but in lots of ways, it is just like mine. Here’s what I mean. Every enterprise, including yours, desperately needs dependable, accurate data to support decisions and planning processes. So, in that way, all businesses are the same. Whether data analysis requirements are ongoing or specialized to support a focused initiative, the challenges of advanced data analytics and data science efforts can be daunting.
Apache Spark enables programmers with an application-programming interface that focuses on data structure. Apache Spark programming allows Spark consultants to expand the capabilities of development and programming, map functions across data, and simplify data results. The tool supports Hadoop YARN, Apache Mesos, Hadoop Distributed File System, Cassandra, OpenStack Swift, Amazon 53, Kudu, and MapR File System. It offers the Apache Spark developer a powerful tool to work in an integrated environment and simplify the programming environs.
