Relationship between MapReduce, Spark, YARN, and HDFS ! In Big Data era Hadoop is the de facto standard for developing of big data applications by using MapReduce framework. And Hadoop is composed of one or more master nodes and any number of slave nodes depends up on the data needed. Hadoop simplifies distributed applications by […]
The 8th Habit of Highly Effective Big Data Programmers ! Last week I read a book called “The Seven Habits of Highly Effective Big Data Programmers” by Rekha Joshi which is interesting. Happy to share with the community which I have encouraged from the book. Let’s understand first what Big Data is. Just by listening the […]
Self-Learn Yourself Apache Spark in 21 Blogs – #8 In this blog let us discuss on How to loading data, what is Lambdas, How to do Transforming Data and more on Transformations. And want to have quick read on the other blogs in this learning series. Apache Spark can load from any input sources like […]
Self-Learn Yourself Scala in 21 Blogs – #5 Blog 5 – Does functional programming matters and what are monads? Missed the previous blogs have a quick look with Self-Learn Yourself Scala in 21 Blogs (#1, #2, #3, #4). In this blog let’s understand for Scala developers does the functional programming matters and also what is […]
11 Key Tuning Checklists for Apache Hadoop! Apache Hadoop is a well know and de-facto framework for processing large big data sets through distributed & parallel computing. YARN(Yet Another Resources Negotiator) allowed Hadoop to evolve from a simple MapReduce engine to a big data ecosystem that can run heterogeneous (MapReduce and non-MapReduce) apps concurrently. This results […]
Today emerging big data technology firm focused on helping enterprises build breakthrough software solutions powered by disruptive enterprise software trends like Machine learning and data science, Cyber-security, Enterprise IOT, and Cloud. So Hadoop is one of the proven software in big data space, but is it only Hadoop. Nope we have many more technologies which […]
Blog 4 – OOP to Functional Programming We are already using functional programming using Scala with the previous blog series of Self-Learn Yourself Scala in 21 Blogs (#1, #2, #3). Let’s start with defining functional programming which is a programming paradigm that models computation as the evaluation of expressions and expressions are built using functions […]
Blog 3 – Functional Programming & Data Structures In Scala Functional programming and functional data structures are very interesting and powerful. Actually it supports both types data structures called immutable & mutable. And let us discuss on two more vital concepts called type parameterization and higher-order functions. The type is similar to Java generics; it […]
8 Breaking Changes in Apache Flink 1.0.0 ! Apache Flink is an open source platform for distributed stream and batch data processing. Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. Flink also builds batch processing on top of the streaming engine, overlaying […]
Blog 2 – Lets’ get started with Scala Just type Scala in your environment to get the Scala interpreter and if everything is fine we will prompt with scala>. If you have problem with installation please follow the link, which has step by step explanations. So we are good to explore the Scala commands. Now […]
Self-Biearn Yourself Scala in 21 Blogs – #1 Blog 1 – Scala the basics Thanks to the communities like LinkedIn, hadoop, Spark, Apache Software, Yahoo and more…from dataottam. As a new learning and sharing initiative we the dataottam team launched “Self-Learn Yourself Scala in 21 Blogs”. Scala is something Object-Oriented meets functional to have best […]
Self-Learn Yourself Apache Spark in 21 Blogs – #7 Key Concepts of Resilient Distributed Datasets (RDDs) and more… In this blog how do we create the RDDs and what operations can we perform with RDDs. Have quick read on the other blogs in this learning series. In simple RDD (Resilient Distributed Dataset); if data in […]
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