Big Data Analysis With RHadoop

An introduction to using Hadoop Streaming to write map/reduce functions without knowing Java. Includes examples written in Python.

Hadoop Streaming allows any executable or script to be used as a MapReduce job. It works by launching the executable or script as a separate process and communicating with it via stdin and stdout. The executable or script receives key-value pairs in a predefined format and outputs new key-value pairs that are collected. Hadoop Streaming uses PipeMapper and PipeReducer to adapt the external processes to the MapReduce framework. It provides a simple way to run MapReduce jobs without writing Java code.

I originally gave this presentation as an internal briefing at SDSC based on my experiences in working with Spark to solve scientific problems.

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.

Provide a system level and pseudo-code level anatomy of Hive, a data warehousing system based on Hadoop.

Big Data with Hadoop & Spark Training: http://bit.ly/2sh5b3E This CloudxLab Hadoop Streaming tutorial helps you to understand Hadoop Streaming in detail. Below are the topics covered in this tutorial: 1) Hadoop Streaming and Why Do We Need it? 2) Writing Streaming Jobs 3) Testing Streaming jobs and Hands-on on CloudxLab

The document provides information about Hive and Pig, two frameworks for analyzing large datasets using Hadoop. It compares Hive and Pig, noting that Hive uses a SQL-like language called HiveQL to manipulate data, while Pig uses Pig Latin scripts and operates on data flows. The document also includes code examples demonstrating how to use basic operations in Hive and Pig like loading data, performing word counts, joins, and outer joins on sample datasets.

- Profiling Hadoop jobs at Twitter revealed that compression/decompression of intermediate data and deserialization of complex object keys were very expensive. Optimizing these led to performance improvements of 1.5x or more. - Using columnar file formats like Apache Parquet allows reading only needed columns, avoiding deserialization of unused data. This led to gains of up to 3x. - Scala macros were developed to generate optimized implementations of Hadoop's RawComparator for common data types, avoiding deserialization for sorting.

Pig is a platform for analyzing large datasets that uses a high-level language to express data analysis programs. It compiles programs into MapReduce jobs that can run in parallel on a Hadoop cluster. Pig provides built-in functions for common tasks and allows users to define their own custom functions (UDFs). Programs can be run locally or on a Hadoop cluster by placing commands in a script or Grunt shell.

RHive aims to integrate R and Hive by allowing analysts to use R's familiar environment while leveraging Hive's capabilities for big data analysis. RHive allows R functions and objects to be used in Hive queries through RUDFs and RUDAFs. It also provides functions like napply to analyze big data in HDFS using R in a distributed manner. RHive provides a bridge between the two environments without requiring users to learn MapReduce programming.

Big Data with Hadoop & Spark Training: http://bit.ly/2LCTufA This CloudxLab Introduction to SparkR tutorial helps you to understand SparkR in detail. Below are the topics covered in this tutorial: 1) SparkR (R on Spark) 2) SparkR DataFrames 3) Launch SparkR 4) Creating DataFrames from Local DataFrames 5) DataFrame Operation 6) Creating DataFrames - From JSON 7) Running SQL Queries from SparkR

The document provides an overview of various Apache Pig features including: - The Grunt shell which allows interactive execution of Pig Latin scripts and access to HDFS. - Advanced relational operators like SPLIT, ASSERT, CUBE, SAMPLE, and RANK for transforming data. - Built-in functions and user defined functions (UDFs) for data processing. Macros can also be defined. - Running Pig in local or MapReduce mode and accessing HDFS from within Pig scripts.

Hive is a data warehouse system built on top of Hadoop that allows users to query large datasets using SQL. It is used at Facebook to manage over 15TB of new data added daily across a 300+ node Hadoop cluster. Key features include using SQL for queries, extensibility through custom functions and file formats, and optimizations for performance like predicate pushdown and partition pruning.

1. The document discusses multi-resource packing of tasks with dependencies to improve cluster scheduler performance. It describes problems with current schedulers related to resource fragmentation and over-allocation. 2. A packing heuristic is proposed that assigns tasks to machines based on an alignment score to reduce fragmentation and spread load. A job completion time heuristic is also described. 3. The paper presents results showing improvements in makespan and job completion times from approaches that consider dependent tasks and multiple resource demands compared to current schedulers. It also discusses achieving trade-offs between performance and fairness.

This was the first session about Hadoop and MapReduce. It introduces what Hadoop is and its main components. It also covers the how to program your first MapReduce task and how to run it on pseudo distributed Hadoop installation. This session was given in Arabic and i may provide a video for the session soon.

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.

Big Data with Hadoop & Spark Training: http://bit.ly/2kyXPo0 This CloudxLab Writing MapReduce Programs tutorial helps you to understand how to write MapReduce Programs using Java in detail. Below are the topics covered in this tutorial: 1) Why MapReduce? 2) Write a MapReduce Job to Count Unique Words in a Text File 3) Create Mapper and Reducer in Java 4) Create Driver 5) MapReduce Input Splits, Secondary Sorting, and Partitioner 6) Combiner Functions in MapReduce 7) Job Chaining and Pipes in MapReduce

Hadoop, being a disruptive data processing framework, has made a large impact in the data ecosystems of today. Enabling business users to translate existing skills to Hadoop is necessary to encourage the adoption and allow businesses to get value out of their Hadoop investment quickly. R, being a prolific and rapidly growing data analysis language, now has a place in the Hadoop ecosystem. With the advent of technologies such as RHadoop, optimizing R workloads for use on Hadoop has become much easier. This session will help you understand how RHadoop projects such as RMR, and RHDFS work with Hadoop, and will show you examples of using these technologies on the Hortonworks Data Platform.

財經新聞版面上充斥者許多看似可以賺錢的訊息，但看著新聞做股票，就像用了會誤導人的GPS，不但沒有帶領你找尋到交易的聖杯,處於資訊弱勢的散戶，只能跟著新聞訊息追高殺低，反而落入大戶養、套、殺的圈套中。 而我們這次將透由Ｒ語言的文字探勘與金融資料進行交叉分析，幫助你解讀每一次的新聞訊息或輿情資料，是大戶出貨的訊息，或是保障獲利的名燈，讓你趨吉避凶，正確從新聞中找到穩健投資的康莊大道。 在本簡報中，將介紹如何使用 rvest 獲取金融資料，並透過jiebaR 的斷詞，以及tmcn.word2vec 的分析，找出金融產業的相關聯性，讓進行投資時，可以參考關聯網路圖，發現先機！

RHive tutorial 1: RHive 튜토리얼 1 - 설치 및 설정 (한글판)

RHive 튜토리얼 2 - 한글판

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.

This document discusses using Python for social network analysis on Facebook data. It provides examples of: - Connecting to the Facebook API and obtaining an access token - Retrieving user and friend data via API calls - Analyzing likes on posts to determine who likes a user's posts the most - Performing text mining on post messages using NLTK and Jieba to determine popular topics - Modeling the friendship network as a graph and using NetworkX and community detection to identify groups within the social network.

Hadoop is rapidly being adopted as a major platform for storing and managing massive amounts of data, and for computing descriptive and query types of analytics on that data. However, it has a reputation for not being a suitable environment for high performance complex iterative algorithms such as logistic regression, generalized linear models, and decision trees. At Revolution Analytics we think that reputation is unjustified, and in this talk I discuss the approach we have taken to porting our suite of High Performance Analytics algorithms to run natively and efficiently in Hadoop. Our algorithms are written in C++ and R, and are based on a platform that automatically and efficiently parallelizes a broad class of algorithms called Parallel External Memory Algorithms (PEMA’s). This platform abstracts both the inter-process communication layer and the data source layer, so that the algorithms can work in almost any environment in which messages can be passed among processes and with almost any data source. MPI and RPC are two traditional ways to send messages, but messages can also be passed using files, as in Hadoop. I describe how we use the file-based communication choreographed by MapReduce and how we efficiently access data stored in HDFS.

R and Hadoop go together. In fact, they go together so well, that the number of options available can be confusing to IT and data science teams seeking solutions under varying performance and operational requirements. Which configuration is faster for big files? Which is faster for sharing data and servers among groups? Which eliminates data movement? Which is easiest to manage? Which works best with iterative and multistep algorithms? What are the hardware requirements of each alternative? This webinar is intended to help new users of R with Hadoop select their best architecture for integrating Hadoop and R, by explaining the benefits of several popular configurations, their performance potential, workload handling and programming model and administrative characteristics. Presenters from Revolution Analytics will describe the options for using Revolution R Open and Revolution R Enterprise with Hadoop including servers, edge nodes, rHadoop and ScaleR. We’ll then compare the characteristics of each configuration as regards performance but also programming model, administration, data movement, ease of scaling, mixed workload handling, and performance for large individual analyses vs. mixed workloads.

The document discusses integrating R and Hadoop for big data analytics. It notes that existing statistical applications like R are incapable of handling big data, while data management tools lack analytical capabilities. Integrating R with Hadoop bridges this gap by leveraging R's analytics and statistics functionality with Hadoop's ability to process and store distributed data. RHadoop is introduced as an open source project that allows R programmers to directly use MapReduce functionality in R code. Specific RHadoop packages like rhdfs and rmr2 are described that enable interacting with HDFS and performing statistical analysis via MapReduce on Hadoop clusters. Text analytics use cases with R and Hadoop like sentiment analysis are also briefly outlined.

This document outlines an agenda for analyzing social networks with R. It discusses connecting to social networks like Facebook via APIs, extracting friend data, creating a friendship matrix, and visualizing the resulting friend graph in Gephi. It also provides examples of analyzing Facebook data like extracting post likes counts and generating statistics on popular posts. The document encourages exploring one's own social network data to find insights like common interests between friends or the gender distribution of one's network.

This document discusses big data analysis and data science. It introduces common data analysis techniques like predictive modeling, machine learning, and recommendation systems. It also discusses tools for working with big data, including Hadoop, HDFS, Pig, HBase, Mahout and languages like R and Python. The document provides an example of using these techniques and tools to build a recommendation system using streaming data from Flume stored in HDFS and analyzed with Pig and HBase.

This document summarizes algorithms for large-scale data mining using MapReduce, including: 1) Information retrieval algorithms like distributed grep, calculating URL access frequency, and constructing the reverse web link graph. 2) Graph algorithms like PageRank, which is computed through an iterative process of message passing between nodes. 3) Clustering algorithms like canopy clustering, which uses two distance thresholds to create overlapping clusters in a single pass over the data.

This document discusses using machine learning with R for data analysis. It covers topics like preparing data, running models, and interpreting results. It explains techniques like regression, classification, dimensionality reduction, and clustering. Regression is used to predict numbers given other numbers, while classification identifies categories. Dimensionality reduction finds combinations of variables with maximum variance. Clustering groups similar data points. R is recommended for its statistical analysis, functions, and because it is free and open source. Examples are provided for techniques like linear regression, support vector machines, principal component analysis, and k-means clustering.

The document discusses the next generation design of Hadoop MapReduce. It aims to address scalability, availability, and utilization limitations in the current MapReduce framework. The key aspects of the new design include splitting the JobTracker into independent resource and application managers, distributing the application lifecycle management, enabling wire compatibility between versions, and allowing multiple programming paradigms like MPI and machine learning to run alongside MapReduce on the same Hadoop cluster. This architecture improves scalability, availability, utilization, and agility compared to the current MapReduce implementation.

Market Basket Analysis Algorithm with Map/Reduce of Cloud Computing presented at PDPTA 2011 (http://www.world-academy-of-science.org/worldcomp11/ws/conferences/pdpta11)

The document provides an overview of MapReduce, including: 1) MapReduce is a programming model and implementation that allows for large-scale data processing across clusters of computers. It handles parallelization, distribution, and reliability. 2) The programming model involves mapping input data to intermediate key-value pairs and then reducing by key to output results. 3) Example uses of MapReduce include word counting and distributed searching of text.

Microsoft R server for distributed computing โดย กฤษฏิ์ คำตื้อ Technical Evangelist Microsoft (Thailand) Limited ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND

The document introduces Microsoft R Server and Microsoft R Open. It discusses that R is a popular open source programming language and platform for statistics, analytics, and data science. Microsoft R Server allows for distributed computing on big data using R and brings enterprise-grade support and capabilities to the open source R platform. It can perform analytics both in-database using SQL Server and in Hadoop environments without moving data.

The document describes a market basket analysis algorithm using MapReduce and HBase to analyze transaction data from stores. The algorithm breaks transaction data into key-value pairs of item pairs, aggregates the counts using MapReduce, and stores the results in HBase. An experiment loaded transaction data of various sizes into Hadoop and analyzed the data, finding execution times increased with more data and nodes but HBase provided faster retrieval compared to HDFS alone.