Understanding Hadoop 2.x Architecture and it’s Daemons

Prior to learn the concepts of Hadoop 2.x Architecture, I strongly recommend you to refer the my post on Hadoop Core Components, internals of Hadoop 1.x Architecture and its limitations. It will give you the idea about Hadoop2 Architecture requirement. And we have already learnt about the basic Hadoop components like Name Node, Secondary Name Node, Data Node, Job Tracker and Task Tracker. Continue reading “Understanding Hadoop 2.x Architecture and it’s Daemons”

4 Steps to Configure Hive with MySQL Metastore on CentOS

Prior to the configuration of Hive with MySQL metastore, let’s know some important things about Apache Hive and it’s metastore. Apache Hive Metastore is normally configured with Derby Database. But that setting is recommended just for the testing or ad-hoc development purpose. When hive is used in production, its metastore should be configured in databases like MySQL or Postgres. Continue reading “4 Steps to Configure Hive with MySQL Metastore on CentOS”

Apache Spark RDD Operations: Transformation and Action

We have already discussed about Spark RDD in my post Apache Spark RDD : The Bazics. In this post we’ll learn about Spark RDD Operations in detail. As we know Spark RDD is distributed collection of data and it supports two kind of operations on it Transformations and Actions. Continue reading “Apache Spark RDD Operations: Transformation and Action”

Apache Spark RDD : The Bazics

RDD stands for Resilient Distributed Dataset. Apache Spark RDD is an abstract representation of the data which is divided into the partitions and distributed across the cluster. If you are aware about collection framework in Java than you can consider an RDD same as the Java collection object but here it is divided into various small pieces (referred as partitions) and is distributed across multiple nodes. Continue reading “Apache Spark RDD : The Bazics”

Introduction to Apache Spark

Prior to Introduction to Apache Spark, it is necessary that we understand the actual requirement of Apache Spark. So let’s rewind to the earlier architecture of distributed data processing for big data analytics. And the most famous algorithm for large scale data processing is Hadoop MapReduce. Hadoop MapRecuce solves certain problems for distributed computation but it has it’s own limitations when it comes to data scale and processing time. Continue reading “Introduction to Apache Spark”

7 Steps to Install Apache Hive with Hadoop on CentOS

Before we learn to install Apache Hive on CentOS let me give you the introduction of it. Hive is basically a data warehouse tool to store and process the structured data residing on HDFS. Hive was developed by Facebook and than after it is shifted to Apache Software Foundation and became an open source Apache Hive. Continue reading “7 Steps to Install Apache Hive with Hadoop on CentOS”

Building Spark Application JAR using Scala and SBT

Normally we create Spark Application JAR using Scala and SBT (Scala Build Tool). In my previous post on Creating Multi-node Spark Cluster we have executed a word count example using spark shell. As an extension to that, we’ll learn about How to create Spark Application JAR file with Scala and SBT? and How to execute it as a Spark Job on Spark Cluster? Continue reading “Building Spark Application JAR using Scala and SBT”