SQL Knowledge Bank - Azure

Introduction In my previous blog post, we are discussing about Hadoop Map Reduce. Now in this blog post we are going to discuss about Spark Fundamentals concept like RDD. RDD stands for  Resilient Distributed  Dateset , these are the elements that run and operate on multiple nodes to do parallel processing on a cluster.  Hope it will be interesting. What is RDD According to Apache Spark documentation -  " Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat ". RDDs are immutable elements, which means once we create an RDD we cannot change it. RDDs are fault tolerant as well, hence in case of any failure, they recover a

Introduction Before moving to Spark RDD concept, which is the base line of Spark, we need to understand the concept of Hadoop Mapredue. RDD is the improvement of Hadoop Mapreduce to get 100 outputs in memory. In this blog post we are going to discuss about Hadoop Mapredue to understand the concept only. Hope it will be interesting.   What is MapReduce MapReduce is a software framework and programming module to handle huge data. Hadoop is capable to run MapReduce programs which are written in different language like Java, Ruby, Python and C++. How MapReduce Works As the name specified, MapReduce is a combination of Mapping and Reducing.   We can divide the MapReduc into following Sections. ·          Input Splits ·          Mapping ·          Shuffling ·          Reducing   Before examining those, let’s take an example.   Please consider the following input data as Map Reduce program.   Mayuree how are you We welcome Mayuree in India Mayaure is