Hadoop - Simple. Scalable.
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This document provides an overview of Hadoop, including: 1) Hadoop solves the problems of analyzing massively large datasets by distributing data storage and analysis across multiple machines to tolerate node failure. 2) Hadoop uses HDFS for distributed data storage, which shards massive files across data nodes with replication for fault tolerance, and MapReduce for distributed data analysis by sending code to the data. 3) The document demonstrates MapReduce concepts like map, reduce, and their composition with an example job.
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Lecture 2 part 1Jazan University The document discusses Hadoop, its components, and how they work together. It covers HDFS, which stores and manages large files across commodity servers; MapReduce, which processes large datasets in parallel; and other tools like Pig and Hive that provide interfaces for Hadoop. Key points are that Hadoop is designed for large datasets and hardware failures, HDFS replicates data for reliability, and MapReduce moves computation instead of data for efficiency.
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Apache Hadoop & Friends at Utah Java User's GroupCloudera, Inc. Talk about Apache Hadoop at the February 18, 2010 meeting of the Utah Java User's Group.
http://www.ujug.org/web/
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Introduction to Hadoopjoelcrabb An introduction to Hadoop presentation geared towards educating potential clients on Hadoop\'s capabilities.
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Polyglot and Poly-paradigm Programming for Better Agility
Polyglot and Poly-paradigm Programming for Better Agilityelliando dias This document discusses the benefits of polyglot and poly-paradigm programming approaches for building more agile applications. It describes how using multiple languages and programming paradigms can optimize both performance and developer productivity. Specifically, it suggests that statically-typed compiled languages be used for core application components while dynamically-typed scripting languages connect and customize these components. This approach allows optimizing areas that require speed/efficiency separately from those requiring flexibility. The document also advocates aspects and functional programming to address cross-cutting concerns and concurrency challenges that arise in modern applications.
Javascript Libraries
Javascript Librarieselliando dias This document discusses JavaScript libraries and frameworks. It provides an overview of some popular options like jQuery, Prototype, Dojo, MooTools, and YUI. It explains why developers use libraries, such as for faster development, cross-browser compatibility, and animation capabilities. The document also discusses how libraries resemble CSS and use selector syntax. Basic examples are provided to demonstrate common tasks like hover effects and row striping. Factors for choosing a library are outlined like maturity, documentation, community, and licensing. The document concludes by explaining how to obtain library code from project websites or Google's AJAX Libraries API.
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How to Make an Eight Bit Computer and Save the World!elliando dias This document summarizes a talk given to introduce an open source 8-bit computer project called the Humane Reader. The talk outlines the goals of providing a cheap e-book reader and computing platform using open source tools. It describes the hardware design which uses an AVR microcontroller and interfaces like video output, SD card, and USB. The talk also covers using open source tools for development and sourcing low-cost fabrication and assembly. The overall goals are to create an inexpensive device that can provide educational resources in developing areas.
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Ragel talkelliando dias Ragel is a parser generator that compiles to various host languages including Ruby. It is useful for parsing protocols and data formats and provides faster parsing than regular expressions or full LALR parsers. Several Ruby projects like Mongrel and Hpricot use Ragel for tasks like HTTP request parsing and HTML parsing. When using Ragel with Ruby, it can be compiled to Ruby code directly, which is slow, or a C extension can be written for better performance. The C extension extracts the parsed data from Ragel and makes it available to Ruby.
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A Practical Guide to Connecting Hardware to the Webelliando dias This document provides an overview of connecting hardware devices to the web using the Arduino platform. It discusses trends in electronics and computing that make this easier, describes the Arduino hardware and software, and covers various connection methods including directly to a computer, via wireless modems, Ethernet shields, and services like Pachube that allow sharing sensor data over the internet. The document aims to demonstrate how Arduinos can communicate with other devices and be used to build interactive systems.
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Minicurso arduinoelliando dias O documento apresenta um mini-curso introdutório sobre Arduino, abordando o que é a plataforma Arduino, como é estruturado seu hardware, como programá-lo, exemplos básicos de código e aplicações possíveis como controle residencial e robótica.
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Incanter Data Sorceryelliando dias The document discusses various functions for working with datasets in the Incanter library for Clojure. It describes how to create, read, save, select rows and columns from, and sort datasets. Functions are presented for building datasets from sequences, reading datasets from files and URLs, saving datasets to files and databases, selecting single or multiple columns, and filtering rows based on conditions. The document also provides an overview of the Incanter library and its various namespaces for statistics, charts, and other functionality.
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Rangoelliando dias Rango is a lightweight Ruby web framework built on Rack that aims to be more robust than Sinatra but smaller than Rails or Merb. It is inspired by Django and Merb, uses Ruby 1.9, and supports features like code reloading, Bundler, routing, rendering, and HTTP error handling. The documentation provides examples and details on using Rango.
Fab.in.a.box - Fab Academy: Machine Design
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Hadoop + Clojureelliando dias The document discusses using Clojure for Hadoop programming. Clojure is a dynamic functional programming language that runs on the Java Virtual Machine. The document provides an overview of Clojure and how its features like immutability and concurrency make it well-suited for Hadoop. It then shows examples of implementing Hadoop MapReduce jobs using Clojure by defining mapper and reducer functions.
Hadoop and Hive Development at Facebook
Hadoop and Hive Development at Facebookelliando dias Facebook generates large amounts of user data daily from activities like status updates, photo uploads, and shared content. This data is stored in Hadoop using Hive for analytics. Some key facts:
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From Lisp to Clojure/Incanter and RAn Introductionelliando dias This document provides a comparison between the statistical computing languages R and Clojure/Incanter. It discusses the histories and philosophies behind Lisp, Fortran, R and Clojure. Key differences noted are that Clojure runs on the Java Virtual Machine, allowing it to leverage Java libraries, while R is primarily written in C and Fortran. Incanter is presented as a Clojure-based platform for statistical computing and graphics that is more immature than R but allows easier access to Java capabilities. Basic syntax comparisons are provided.
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Hadoop - Simple. Scalable.
- 3. Java. Clojure. Ruby. Cloudera Certified
- 4. posscon.org April 15, 16, and 17
- 5. Agenda Overview Massively Large Data Sets and the problems therein Distributed File System MapReduce Pig
- 6. Overview
- 7. Doug Cutting Genius
- 8. Favorite Hadoop Story New York Times
- 9. 4 Terabytes of Source Articles.
- 10. 24 Hours.
- 11. 5.5 Terabytes of PDFs.
- 12. Did it again.
- 13. $240.
- 14. Infoporn from Yahoo 73 hours 490 TB Shuffling 280 TB Output 4000 Nodes 16 PB Disk Space 32K Cores 64 TB RAM
- 15. Hadoop solves...
- 16. Analyzing Massively Large Datasets
- 17. Two Problems You have to distribute.
- 18. Data Storage Capacity has increased rapidly beyond read speeds. Datasets won't fit on one disk. Tolerate node failure.
- 19. Data Analysis Combine data from many machines. Tolerate node failure.
- 20. How Hadoop solves these problems.
- 21. Send Code to Data. Not Data to Code.
- 22. Data Storage HDFS
- 23. Name Node. Data Nodes. Master - Slave Relationship
- 24. Shard massive files across multiple machines. MB, GB, and TB
- 25. Tolerant of Node Failure Files replicated across at least 3 nodes.
- 26. HDFS behaves like a normal file system. No true appends yet.
- 27. Demonstration.
- 28. Data Analysis MapReduce
- 29. Job Tracker. Task Nodes. Master - Slave Relationship.
- 30. map
- 31. Demonstration
- 32. pmap
- 33. Demonstration
- 34. reduce
- 35. Demonstration
- 36. (reduce (pmap))
- 37. Demonstration.
- 38. MapReduce Java
- 39. Nobody likes it. :-)
- 40. MapReduce Ruby. Python. Unix Utilities.
- 42. Hadoop Ecosystem Pigkeeper. Hive. Cascading.
- 43. Pig
- 44. HBase