Learning everything about the Scala and apache spark

Spark is a popular framework that is used for implementing the concepts of cluster computing. But due to its high level functionalities, it is often used on the YARN framework of Hadoop. The main aim of spark is to give the needed speed to the process of computation. For anyone who is planning to work on the concepts of big data analytics, the apache spark and Scala online certification will come in handy.

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If you sign up for apache spark and Scala online training in Boston, you are expected to know the basics concepts of Scala, a programming language used for the development of web applications. When it comes to data computation and analysis, the problems were faced due to slow speed of the queries and algorithms. With the help of spark, this process helps in improving the way memory storage is done and how the process of fault recovery is made efficient.

If you want to understand the basics, you should begin from the concepts of the RDD. RDD is the resilient distributed dataset which is used for the data abstraction following the core concepts. RDD is basically a collection of objects that are defined in the process of development. The concept here is simple; the RDD is divided into a variety of partitions. The computation of these partitions is done with the help of different nodes that are defined in the data cluster. Since we are talking in context to Scala, the RDD will contain objects that are based on the top of Scala.

Since the RDD play an important role, you either needs to create new RDDs as and when required or modifications is done as per the requirements. The process of data distribution is very sorted and done with uniformity across all the clusters to ensure that the operations take place in the desired manner.

The areas of applications of apache spark with Scala are quite a man and since there are a very low amount of compatibility issues, one doesn’t need to worry about it in any possible situations. It is composed of various components like Spark Streaming, GraphX, Spark SQL, etc. these are nothing but the libraries that cover wide range of topics and thus most of your requirements related to development could be taken care of with the help of them instead of doing it on your own.

There are a lot of added advantages when you use the apache spark with Scala. One of such aspects is the compatibility. The development of apache spark is done in Scala itself and therefore when you use it for your development related needs, the task would not just get simplified but the concepts of compatibility would also come in handy. If you have any knowledge about the Scala, you will know that it is one of the easiest languages to write and implement and due to this, the problems related to complexities have widely reduced leading to high level of security and reliability.

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