This team has decades of practical experience in working with Java and with billions of rows of data. This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel. Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other. Hands-on workout involving Hadoop, MapReduce: This course will get you hands-on with Hadoop very early on.
Learning a new development framework takes time, and, as is well known, the Hadoop platform is no exception. MapReduce developers face a steep learning curve when first deploying and configuring a Hadoop cluster and later when verifying program correctness. Compounded by long execution times (measured in minutes), this often frustrates data scientists, especially those who lack a systems administration background. Even experienced Hadoop users run into a well-known set of "gotchas" that hinder their progress. Recognizing that these obstacles easily can create frustration and impede productivity, ScaleOut Software has focused on making it as easy as possible for developers to build high performance, distributed applications for the enterprise.
Now that you're familiar with the basics of Hadoop and HDFS, it's time to explore Hadoop MapReduce. MapReduce is Hadoop's primary framework for processing big data on a shared cluster. It works by processing smaller amounts of data in parallel via map tasks. The outputs of these map tasks are then used as inputs for reduce tasks which produce a final result set. Every MapReduce application has an associated job configuration.
Learning Objectives - In this module, we will discuss about Meta Patterns & Graph Patterns. Meta Patterns are different from other Patterns discussed above i.e. these are not basic patterns, but Pattern about Patterns, Introduction to Graph Patterns. Topics - About Meta Patterns, Types of Meta Patterns: Job Chaining – Description, use cases, chaining with driver, basic & parallel job chaining, chaining with shell scripts, chaining with job control, Example code walk-through, Chain Folding – Description, What to fold, Chain mapper, Chain Reducer, Example code walk-through, Job Merging - Description, Steps for merging two jobs, Example code walk-through, Introduction to Graph design Pattern, Types of Graph Design Patterns: In-mapper Combining Pattern, Schimmy Pattern and Range Partitioning Pattern Pseudo-code for each pattern applied to Page-rank algorithm.
Apache Hadoop is an open-source software framework for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. MapReduce is the heart of Apache Hadoop. MapReduce is a framework which allows developers to develop hadoop jobs in different languages. So in this course we'll learn how to create MapReduce Jobs with Python.This course will provide you an in-depth knowledge of concepts and different approaches to analyse datasets using Python Programming. This course on MapReduce Jobs with Python will help you to understand MapReduce Jobs Programming in Python, how to set up an environment for the running MapReduce Jobs in Python, how to submit and execute MapReduce applications in Python environment.