Introduction to MapReduce and Hadoop

Map Reduce is a parallel and distributed approach developed by Google for processing large data sets. It has two key components - the Map function which processes input data into key-value pairs, and the Reduce function which aggregates the intermediate output of the Map into a final result. Input data is split across multiple machines which apply the Map function in parallel, and the Reduce function is applied to aggregate the outputs.

The document discusses fault tolerance in Apache Hadoop. It describes how Hadoop handles failures at different layers through replication and rapid recovery mechanisms. In HDFS, data nodes regularly heartbeat to the name node, and blocks are replicated across racks. The name node tracks block locations and initiates replication if a data node fails. HDFS also supports name node high availability. In MapReduce v1, task and task tracker failures cause re-execution of tasks. YARN improved fault tolerance by removing the job tracker single point of failure.

GraphX is Apache Spark's API for graph distributed computing based on the Pregel programming model. In this talk we'll see a brief introduction to Pregel and then we'll focus on transforming standard graph algorithms in their distributed counterpart using GraphX to speedup performance in a distributed environment.

Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.

The document provides an introduction to Hadoop, including an overview of its core components HDFS and MapReduce, and motivates their use by explaining the need to process large amounts of data in parallel across clusters of computers in a fault-tolerant and scalable manner. It also presents sample code walkthroughs and discusses the Hadoop ecosystem of related projects like Pig, HBase, Hive and Zookeeper.

This document provides a summary of MapReduce algorithms. It begins with background on the author's experience blogging about MapReduce algorithms in academic papers. It then provides an overview of MapReduce concepts including the mapper and reducer functions. Several examples of recently published MapReduce algorithms are described for tasks like machine learning, finance, and software engineering. One algorithm is examined in depth for building a low-latency key-value store. Finally, recommendations are provided for designing MapReduce algorithms including patterns, performance, and cost/maintainability considerations. An appendix lists additional MapReduce algorithms from academic papers in areas such as AI, biology, machine learning, and mathematics.

This document provides an overview of topics to be covered in a Big Data training. It will discuss uses of Big Data, Hadoop, HDFS architecture, MapReduce algorithm, WordCount example, tips for MapReduce, and distributing Twitter data for testing. Key concepts that will be covered include what Big Data is, how HDFS is architected, the MapReduce phases of map, sort, shuffle, and reduce, and how WordCount works as a simple MapReduce example. The goal is to introduce foundational Big Data and Hadoop concepts.

Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It was developed to support distributed processing of large datasets. The document provides an overview of Hadoop architecture including HDFS, MapReduce and key components like NameNode, DataNode, JobTracker and TaskTracker. It also discusses Hadoop history, features, use cases and configuration.

The document discusses linking the statistical programming language R with the Hadoop platform for big data analysis. It introduces Hadoop and its components like HDFS and MapReduce. It describes three ways to link R and Hadoop: RHIPE which performs distributed and parallel analysis, RHadoop which provides HDFS and MapReduce interfaces, and Hadoop streaming which allows R scripts to be used as Mappers and Reducers. The goal is to use these methods to analyze large datasets with R functions on Hadoop clusters.

The document describes how to use Gawk to perform data aggregation from log files on Hadoop by having Gawk act as both the mapper and reducer to incrementally count user actions and output the results. Specific user actions are matched and counted using operations like incrby and hincrby and the results are grouped by user ID and output to be consumed by another system. Gawk is able to perform the entire MapReduce job internally without requiring Hadoop.

This document discusses using Python for Hadoop and data mining. It introduces Dumbo, which allows writing Hadoop programs in Python. K-means clustering in MapReduce is also covered. Dumbo provides a Pythonic API for MapReduce and allows extending Hadoop functionality. Examples demonstrate implementing K-means in Dumbo and optimizing it by computing partial centroids locally in mappers. The document also lists Python books and tools for data mining and scientific computing.

This document provides an overview of MapReduce in Hadoop. It defines MapReduce as a distributed data processing paradigm designed for batch processing large datasets in parallel. The anatomy of MapReduce is explained, including the roles of mappers, shufflers, reducers, and how a MapReduce job runs from submission to completion. Potential purposes are batch processing and long running applications, while weaknesses include iterative algorithms, ad-hoc queries, and algorithms that depend on previously computed values or shared global state.

Spark is a fast and general engine for large-scale data processing. It provides an interface called resilient distributed datasets (RDDs) that allow data to be distributed in memory across clusters and manipulated using parallel operations. Shark is a system built on Spark that allows running SQL queries over large datasets using Spark's speed and generality. The document discusses Spark and Shark's performance advantages over Hadoop for iterative and interactive applications.

This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.

In this talk, we present two emerging, popular open source projects: Spark and Shark. Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write. It outperform Hadoop by up to 100x in many real-world applications. Spark programs are often much shorter than their MapReduce counterparts thanks to its high-level APIs and language integration in Java, Scala, and Python. Shark is an analytic query engine built on top of Spark that is compatible with Hive. It can run Hive queries much faster in existing Hive warehouses without modifications. These systems have been adopted by many organizations large and small (e.g. Yahoo, Intel, Adobe, Alibaba, Tencent) to implement data intensive applications such as ETL, interactive SQL, and machine learning.

The document discusses MapReduce, a framework for processing large datasets in parallel. It provides an overview of MapReduce's basic principles, surveys research to improve the conventional MapReduce framework, and describes research projects ongoing at KAIST. The key points are that MapReduce provides automatic parallelization, fault tolerance, and distributed processing of large datasets across commodity computer clusters. It also introduces the map and reduce functions that define MapReduce jobs.

Shark is a new data analysis system that marries SQL queries with complex analytics like machine learning on large clusters. It uses Spark as an execution engine and provides in-memory columnar storage with extensions like partial DAG execution and co-partitioning tables to optimize query performance. Shark also supports expressing machine learning algorithms in SQL to avoid moving data out of the database. It aims to efficiently support both SQL and complex analytics while retaining fault tolerance and allowing users to choose loading frequently used data into memory for fast queries.

Very detail description if MapReduce in this slides. I hope you will learn very much about MapReduce after reading these slides

The document presents an introduction to MapReduce. It discusses how MapReduce provides an easy framework for distributed computing by allowing programmers to write simple map and reduce functions without worrying about complex distributed systems issues. It outlines Google's implementation of MapReduce and how it uses the Google File System for fault tolerance. Alternative open-source implementations like Apache Hadoop are also covered. The document discusses how MapReduce has been widely adopted by companies to process massive amounts of data and analyzes some criticism of MapReduce from database experts. It concludes by noting trends in using MapReduce as a parallel database and for multi-core processing.

Presentation by Brandwatch Developer James Grant at the second Big Data Brighton meetup, hosted by Brandwatch: www.brandwatch.com

Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.

This document provides an introduction to MapReduce and Disco, an open source implementation of MapReduce in Erlang and Python. It explains the motivation for MapReduce frameworks like Google's in addressing the need to process massive amounts of data across large clusters reliably. The core concepts of MapReduce are described, including how the input is split and mapped in parallel, intermediate key-value pairs are grouped and reduced, and the final output is produced. An example word counting algorithm demonstrates how a problem can be solved using MapReduce.

This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.

This presentation is about apache hadoop technology. This may be helpful for the beginners. The beginners will know about some terminologies of hadoop technology. There is also some diagrams which will show the working of this technology. Thank you.

This document provides an overview of Big Data and Hadoop. It defines Big Data as large volumes of structured, semi-structured, and unstructured data that is too large to process using traditional databases and software. It provides examples of the large amounts of data generated daily by organizations. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop including HDFS for distributed storage and fault tolerance, and MapReduce for distributed processing, are described at a high level. Common use cases for Hadoop by large companies are also mentioned.

This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker. At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics. PPT Agenda: ✓ Introduction to BIG Data & Hadoop ✓ What is MapReduce? ✓ MapReduce Data Flows ✓ MapReduce Programming ---------- What is MapReduce? MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java. ---------- What are MapReduce Components? It has the following components: 1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing. 2. Job Tracker: This allocates the data across multiple servers. 3. Task Tracker: This executes the program across various servers. 4. Reducer: It will isolate the desired output from across the multiple servers. ---------- Applications of MapReduce 1. Data Mining 2. Document Indexing 3. Business Intelligence 4. Predictive Modelling 5. Hypothesis Testing ---------- Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance. Email: sales@skillspeed.com Website: https://www.skillspeed.com

Twitter Data Analysis using various Hadoop tools and little description of Mapreduce concept and use case

Here is how you can solve this problem using MapReduce and Unix commands: Map step: grep -o 'Blue\|Green' input.txt | wc -l > output This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches). Reduce step: cat output This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green. So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map

This document provides an overview of MapReduce, a programming model developed by Google for processing and generating large datasets in a distributed computing environment. It describes how MapReduce abstracts away the complexities of parallelization, fault tolerance, and load balancing to allow developers to focus on the problem logic. Examples are given showing how MapReduce can be used for tasks like word counting in documents and joining datasets. Implementation details and usage statistics from Google demonstrate how MapReduce has scaled to process exabytes of data across thousands of machines.

As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop. At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.

This document proposes an app idea to solve conflicts that arise in Indian families when parents and children want to watch different things on TV. The app would display updates like cricket scores, news, stock prices, weather and social media feeds along the bottom of the TV screen as ticker updates, allowing the whole family to watch together while still getting the information they want. Key features outlined include customizing the number and type of information bars displayed, minimizing bars individually, and changing themes. Revenue would come from advertisements on the app interface. Initial sketches and wireframes are included to illustrate the proposed user interface and flows.

This document provides a guide for OpenStack developers to contribute code. It outlines the prerequisites like creating a Launchpad account and signing a CLA. It describes finding work by attending meetings, tracking bugs, or writing blueprints. Developers are instructed to write git commit messages linking their code patches to specific bugs or blueprints. The guide also covers submitting code for review using git-review and responding professionally to review comments.

The document provides information and instructions for students preparing for their orientation at the University of Kansas (KU). It discusses: 1) Completing the pre-orientation survey and selecting 10 interesting classes to share with an advisor in order to help plan their first year schedule. 2) Logging into the orientation portal to confirm their session date and research any other questions about the day. 3) Checking that they have the required documents like ID, transcripts, placement scores before attending orientation. 4) The orientation day will include introductions, meeting with student assistants and advisors, and enrolling in classes. Snacks and drinks will be provided.