Apache Hadoop 1.1

This document provides an overview and introduction to Hadoop, HDFS, and MapReduce. It covers the basic concepts of HDFS, including how files are stored in blocks across data nodes, and the role of the name node and data nodes. It also explains the MapReduce programming model, including the mapper, reducer, and how jobs are split into parallel tasks. The document discusses using Hadoop from the command line and writing MapReduce jobs in Java. It also mentions some other projects in the Hadoop ecosystem like Pig, Hive, HBase and Zookeeper.

Here's the talk that we presented at the Hadoop Summit 2015, in San Jose. This was an inside look at how we at Yahoo scaled Hive to work at Yahoo's data/metadata scale.

This document summarizes Hortonworks' Hadoop distribution called Hortonworks Data Platform (HDP). It discusses how HDP provides a comprehensive data management platform built around Apache Hadoop and YARN. HDP includes tools for storage, processing, security, operations and accessing data through batch, interactive and real-time methods. The document also outlines new capabilities in HDP 2.2 like improved engines for SQL, Spark and streaming and expanded deployment options.

Introduction to Hadoop Ecosystem was presented to Lansing Java User Group on 2/17/2015 by Vijay Mandava and Lan Jiang. The demo was built on top of HDP 2.2 and AWS cloud.

The document discusses a presentation about practical problem solving with Hadoop and Pig. It provides an agenda that covers introductions to Hadoop and Pig, including the Hadoop distributed file system, MapReduce, performance tuning, and examples. It discusses how Hadoop is used at Yahoo, including statistics on usage. It also provides examples of how Hadoop has been used for applications like log processing, search indexing, and machine learning.

This document provides guidance on sizing and configuring Apache Hadoop clusters. It recommends separating master nodes, which run processes like the NameNode and JobTracker, from slave nodes, which run DataNodes, TaskTrackers and RegionServers. For medium to large clusters it suggests 4 master nodes and the remaining nodes as slaves. The document outlines factors to consider for optimizing performance and cost like selecting balanced CPU, memory and disk configurations and using a "shared nothing" architecture with 1GbE or 10GbE networking. Redundancy is more important for master than slave nodes.

- The document discusses migrating structured data between Hadoop and relational databases using a tool called Bouquet. - Bouquet allows users to select data from a relational database, which is then sent to Spark via Kafka and stored in HDFS/Tachyon for processing. - The enriched data in Spark can then be re-injected back into the original database.

The most well known technology used for Big Data is Hadoop. It is actually a large scale batch data processing system

Big data and Hadoop are introduced as ways to handle the increasing volume, variety, and velocity of data. Hadoop evolved as a solution to process large amounts of unstructured and semi-structured data across distributed systems in a cost-effective way using commodity hardware. It provides scalable and parallel processing via MapReduce and HDFS distributed file system that stores data across clusters and provides redundancy and failover. Key Hadoop projects include HDFS, MapReduce, HBase, Hive, Pig and Zookeeper.

Spark and Ignite are two of the most popular open source projects in the area of high-performance Big Data and Fast Data. But did you know that one of the best ways to boost performance for your next generation real-time applications is to use them together? In this session, Dmitriy Setrakyan, Apache Ignite Project Management Committee Chairman and co-founder and CPO at GridGain will explain in detail how IgniteRDD — an implementation of native Spark RDD and DataFrame APIs — shares the state of the RDD across other Spark jobs, applications and workers. Dmitriy will also demonstrate how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames. Don't miss this opportunity to learn from one of the experts how to use Spark and Ignite better together in your projects. Speakers: Dmitriy Setrakyan, is a founder and CPO at GridGain Systems. Dmitriy has been working with distributed architectures for over 15 years and has expertise in the development of various middleware platforms, financial trading systems, CRM applications and similar systems. Prior to GridGain, Dmitriy worked at eBay where he was responsible for the architecture of an add-serving system processing several billion hits a day. Currently Dmitriy also acts as PMC chair of Apache Ignite project.

Introduction to Hive and HCatalog presentation by Mark Grover at NYC HUG. A video of this presentation is available at https://www.youtube.com/watch?v=JGwhfr4qw5s

http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now. That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis. This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop. Keys Botzum - Senior Principal Technologist with MapR Technologies Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.

Andrew Ryan describes how Facebook operates Hadoop to provide access as a shared resource between groups. More information and video at: http://developer.yahoo.com/blogs/hadoop/posts/2011/02/hug-feb-2011-recap/

The document provides an overview of Hadoop, an open-source software framework for distributed storage and processing of large datasets. It describes how Hadoop uses HDFS for distributed file storage across clusters and MapReduce for parallel processing of data. Key components of Hadoop include HDFS for storage, YARN for resource management, and MapReduce for distributed computing. The document also discusses some popular Hadoop distributions and real-world uses of Hadoop by companies.

Apache Drill is a scalable SQL query engine for analysis of large-scale datasets across various data sources like HDFS, HBase, Hive and others. It allows for ad-hoc analysis of datasets without requiring knowledge of the schema beforehand. Drill uses a distributed architecture with query coordinators and workers to process queries in parallel. It supports various interfaces like JDBC, ODBC and a web console for running SQL queries on different data sources.

Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.

kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderaba www.kellytechno.com

This document describes MoSQL, an elastic storage engine for MySQL that allows adding and removing storage nodes with little performance impact. It has three main components: MySQL servers that interface with clients, storage nodes that store encrypted data using a multi-version key-value store, and a certifier that ensures transactions commit on up-to-date data. Evaluation shows MoSQL outperforms MySQL on TPC-C benchmarks and can dynamically add nodes with minimal throughput reduction. Future work includes supporting different consensus protocols and improving usability.

This document discusses React and Flux. It introduces React as a JavaScript library created by Facebook for building user interfaces. Flux is described as an application architecture pattern for avoiding complex event chains. Key aspects of React covered include using JSX, the virtual DOM for efficient updates, and integrating with other libraries. The document emphasizes thinking about data flow and putting it in good order using Flux. It concludes by recommending enjoying life on a sunny day.

Lukas Vlcek built a search app for public mailing lists in 15 minutes using ElasticSearch. The app allows users to search mailing lists, filter results by facets like date and author, and view document previews with highlighted search terms. Key challenges included parsing email structure and content, normalizing complex email subjects, identifying conversation threads, and determining how to handle quoted content and author disambiguation. The search application and a monitoring tool for ElasticSearch called BigDesk will be made available on GitHub.

Presented on 10/11/12 at the Boston Elasticsearch meetup held at the Microsoft New England Research & Development Center. This talk gave a very high-level overview of Elasticsearch to newcomers and explained why ES is a good fit for Traackr's use case.

The document discusses the OseeGenius discovery platform and its features. It provides an overview of OseeGenius' services, search capabilities, and technical details. Key features include facets, explorers, classification, keyword indexing, metadata extraction, stemming, auto-completion, geospatial search, and integration with library systems. Screenshots demonstrate the user interface and capabilities like highlighting, user workspaces, reviews, and MARC import.

This document provides an overview of Elasticsearch, including its uses cases at companies like GitHub, Stack Overflow, and Netflix. It discusses Elasticsearch's data indexing and querying capabilities. Key topics covered include document mapping and types, shards and replicas, analyzers, term queries, match queries, sorting, aggregations, and cluster configuration. The document concludes with lessons learned and a reference to Elasticsearch's documentation.

Apresentação utilizada no nosso webinar de People Marketing realizado por E-commerce Brasil, Social Miner e iMasters

Atelier organisé par Oxalide (Ludovic Piot) et Kernel 42 (Edouard Fajnzilberg) à destination des niveaux débutants et intermédiaire. Le point de vue du Syadmin et du Dev en un seul atelier et avoir une vision globale du fonctionnement et de l'usage d'Elastic Search.

This document discusses using Elasticsearch for social media analytics and provides examples of common tasks. It introduces Elasticsearch basics like installation, indexing documents, and searching. It also covers more advanced topics like mapping types, facets for aggregations, analyzers, nested and parent/child relations between documents. The document concludes with recommendations on data design, suggesting indexing strategies for different use cases like per user, single index, or partitioning by time range.

The document provides an overview of Elasticsearch including that it is easy to install, horizontally scalable, and highly available. It discusses Elasticsearch's core search capabilities using Lucene and how data can be stored and retrieved. The document also covers Elasticsearch's distributed nature, plugins, scripts, custom analyzers, and other features like aggregations, filtering and sorting.

A quick tour of available integration hooks in Apache Jackrabbit Oak to plug in Apache Solr in order to provide scalable search (& more) functionalities to the repository

This document provides steps to set up Elastic Search on an Ubuntu server including installing Apache, PHP, Java, Elastic Search server, the Elastic Search PHP API, and testing PHP scripts connecting to Elastic Search. It outlines downloading required files, running commands to install packages and configure services, and testing the basic functionality.