Hadoop at Yahoo! -- Hadoop World NY 2009
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Yahoo! is the largest contributor, tester, and user of Hadoop. They have over 4,000 node Hadoop clusters and use Hadoop to run many of their services and applications. Yahoo! contributes all of their Hadoop development work back to the Apache Foundation as open source software. They are also the largest employer of Hadoop contributors.
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The document discusses fuzzy matching and describes Fuzzy Table, a scalable solution for performing fuzzy matching on large multimedia databases using Hadoop. Fuzzy Table uses Hadoop for bulk processing tasks like clustering and indexing data. It then enables low-latency fuzzy searches by caching HDFS metadata and performing searches in parallel across data servers, with average query times scaling linearly as servers are added. Future work involves optimizations to reduce I/O latency and reliance on the HDFS Namenode.
Upgrading To The New Map Reduce API
The document summarizes the key changes between the old MapReduce API and the new MapReduce API in Hadoop. Some of the main changes include:
- Renaming all "mapred" packages to "mapreduce"
- Methods can now throw InterruptedException in addition to IOException
- Using Configuration instead of JobConf
- Changes to Mapper, Reducer, and RecordReader interfaces and classes
- Submitting jobs uses the Job class instead of JobConf and JobClient classes
- InputFormats and OutputFormats see changes like new methods and removed classes
HUG Nov 2010: HDFS Raid - Facebook
HDFS RAID is a contrib project in Hadoop that implements RAID techniques to reduce replication of data in HDFS and tolerate block failures. At Facebook, HDFS RAID is saving about 5PB of storage space by using XOR parity with gradual deployment across tables. The key components are the RaidNode daemon that generates parity files, the BlockFixer that reconstructs missing or corrupt blocks, and the DistributedRaidFileSystem that allows reads from corrupt files by reconstructing missing blocks on the fly.
Cloudera Desktop
The document summarizes a presentation about the public beta release of Cloudera Desktop, a web-based platform for accessing and managing Hadoop clusters. Cloudera Desktop aims to improve the user experience of interacting with Hadoop by providing a desktop metaphor and allowing users to browse files, design and run jobs, and check cluster health directly from a web browser. The presentation demonstrates features of Cloudera Desktop and discusses the motivations for its web-based, desktop-inspired interface and extensible platform design. It encourages attendees to try the free public beta and provide feedback.
3 avro hug-2010-07-21
Avro is a data serialization system that provides data interchange and interoperability between systems. It allows for efficient encoding of data and schema evolution. Avro defines data using JSON schemas and provides dynamic typing which allows data to be read without code generation. It includes a file format and supports MapReduce workflows. Avro aims to become the standard data format for Hadoop applications by providing rich data types, interoperability between languages, and compatibility between versions.
Public Terabyte Dataset Project: Web crawling with Amazon Elastic MapReduce
The document discusses the Public Terabyte Dataset Project which aims to create a large crawl of top US domains for public use on Amazon's cloud. It describes how the project uses various Amazon Web Services like Elastic MapReduce and SimpleDB along with technologies like Hadoop, Cascading, and Tika for web crawling and data processing. Common issues encountered include configuration problems, slow performance from fetching all web pages or using Tika language detection, and generating log files instead of results.
Yahoo! Hadoop User Group - May Meetup - Extraordinarily rapid and robust data...
Cascalog is a query language for Hadoop written in Clojure. It provides an alternative to Pig and Hive that leverages Clojure's features like being a functional language, Lisp-based syntax, and first-class integration with Java. Cascalog allows for full use of Clojure during queries with features like custom operations, dynamic queries, and side-by-side use with other Clojure code. BackType uses Cascalog for tasks like identifying influencers and engagement on Twitter.
January 2011 HUG: Howl Presentation
This document introduces Howl, a table management service for Hadoop that aims to provide a shared schema, data typing system, and table abstraction across tools like Pig, Hive, and MapReduce. It discusses Howl's motivations of enabling collaboration, interoperability, and evolvability. The architecture takes elements from Hive and allows different tools to share data. Howl presents data stored in HDFS as tables that can be partitioned and with columns of specific data types. It provides loaders and storages for Pig and input/output formats for MapReduce. The roadmap includes adding write support for Hive, notifications, and other features to improve interoperability between tools processing data on Hadoop.
January 2011 HUG: Pig Presentation
1) Pig 0.8 focuses on usability, integration, performance, and backwards compatibility with 0.7.
2) It allows UDFs to be written in scripting languages like Jython and provides better statistics and integration with Hadoop job history.
3) Other new features include invoking static Java functions as UDFs, improved HBase integration, and casting relations to scalars.
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Public Terabyte Dataset Project: Web crawling with Amazon Elastic MapReduce
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Similar to Hadoop at Yahoo! -- Hadoop World NY 2009
Hadoop @ Yahoo! - Internet Scale Data Processing
From Eric Baldeschwieler's presentation "Hadoop @ Yahoo! - Internet Scale Data Processing" at the 2009 Cloud Computing Expo in Santa Clara, CA, USA. Here's the talk description on the Expo's site: http://cloudcomputingexpo.com/event/session/509
Architecting the Future of Big Data and Search
The document discusses the potential for integrating Apache Lucene and Apache Hadoop technologies. It covers their histories and current uses, as well as opportunities and challenges around making them work better together through tighter integration or code sharing. Developers and businesses are interested in ways to improve searching large amounts of data stored using Hadoop technologies.
Eric Baldeschwieler Keynote from Storage Developers Conference
- Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable storage of petabytes of data and large-scale computations across commodity hardware.
- Apache Hadoop is used widely by internet companies to analyze web server logs, power search engines, and gain insights from large amounts of social and user data. It is also used for machine learning, data mining, and processing audio, video, and text data.
- The future of Apache Hadoop includes making it more accessible and easy to use for enterprises, addressing gaps like high availability and management, and enabling partners and the community to build on it through open APIs and a modular architecture.
Testing Big Data: Automated Testing of Hadoop with QuerySurge
Are You Ready? Stepping Up To The Big Data Challenge In 2016 - Learn why Testing is pivotal to the success of your Big Data Strategy.
According to a new report by analyst firm IDG, 70% of enterprises have either deployed or are planning to deploy big data projects and programs this year due to the increase in the amount of data they need to manage.
The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth.
Learn why testing your enterprise's data is pivotal for success with big data and Hadoop. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data - all with one data testing tool.
Big-Data Hadoop Tutorials - MindScripts Technologies, Pune
MindScripts Technologies, is the leading Big-Data Hadoop Training institutes in Pune, providing a complete Big-Data Hadoop Course with Cloud-Era certification.
Hadoop basics
Owen O'Malley is an architect at Yahoo who works full-time on Hadoop. He discusses Hadoop's origins, how it addresses the problem of scaling applications to large datasets, and its key components including HDFS and MapReduce. Yahoo uses Hadoop extensively, including for building its Webmap and running experiments on large datasets.
Data infrastructure at Facebook
Data infrastructure at Facebook with reference to the conference paper " Data warehousing and analytics infrastructure at facebook"
Datewarehouse
Hadoop - Hive - scrive
Hadoop and the Data Warehouse: Point/Counter Point
Robin Bloor and Teradata
Live Webcast on April 22, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=2e69345c0a6a4e5a8de6fc72652e3bc6
Can you replace the data warehouse with Hadoop? Is Hadoop an ideal ETL subsystem? And what is the real magic of Hadoop? Everyone is looking to capitalize on the insights that lie in the vast pools of big data. Generating the value of that data relies heavily on several factors, especially choosing the right solution for the right context. With so many options out there, how do organizations best integrate these new big data solutions with the existing data warehouse environment?
Register for this episode of The Briefing Room to hear veteran analyst Dr. Robin Bloor as he explains where Hadoop fits into the information ecosystem. He’ll be briefed by Dan Graham of Teradata, who will offer perspective on how Hadoop can play a critical role in the analytic architecture. Bloor and Graham will interactively discuss big data in the big picture of the data center and will also seek to dispel several common misconceptions about Hadoop.
Visit InsideAnlaysis.com for more information.
Big data or big deal
The document discusses Hadoop and big data technologies. It begins with an introduction to big data concepts and the various Hadoop components like HDFS, MapReduce, YARN, Hive, Pig and Mahout. It then explains how big data is different from traditional data warehousing through the concept of schema-on-read. Finally, it provides recommendations on tools for working with big data technologies locally and in the cloud, as well as sources of inspiration like sandbox environments, Apache projects and GitHub.
Hadoop
This document provides an overview of big data, Hadoop, and related concepts:
- Big data refers to large datasets that cannot be processed efficiently by traditional systems due to their size. Sources include social media, smartphones, machines, and log files.
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements the MapReduce programming model.
- Key Hadoop components include HDFS for storage, MapReduce for distributed processing, and related projects like Pig, Hive, HBase, Flume, Oozie, and Sqoop. Companies use Hadoop for applications involving large datasets, such as log analysis, recommendations, and business intelligence
Hadoop
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes key Hadoop components like HDFS for distributed file storage and MapReduce for distributed processing. Several companies that use Hadoop at large scale are mentioned, including Yahoo, Amazon and Facebook. Applications of Hadoop in healthcare for storing and analyzing large amounts of medical data are discussed. The document concludes that Hadoop is well-suited for big data applications due to its scalability, fault tolerance and cost effectiveness.
Oct 2011 CHADNUG Presentation on Hadoop
Josh Patterson gave a presentation on Hadoop and how it has been used. He discussed his background working on Hadoop projects including for the Tennessee Valley Authority. He outlined what Hadoop is, how it works, and examples of use cases. This includes how Hadoop was used to store and analyze large amounts of smart grid sensor data for the openPDC project. He discussed integrating Hadoop with existing enterprise systems and tools for working with Hadoop like Pig and Hive.
Hortonworks.bdb
This document discusses Hortonworks and its mission to enable modern data architectures through Apache Hadoop. It provides details on Hortonworks' commitment to open source development through Apache, engineering Hadoop for enterprise use, and integrating Hadoop with existing technologies. The document outlines Hortonworks' services and the Hortonworks Data Platform (HDP) for storage, processing, and management of data in Hadoop. It also discusses Hortonworks' contributions to Apache Hadoop and related projects as well as enhancing SQL capabilities and performance in Apache Hive.
Big dataanalyticsinthecloud
Siva Narayanan presents on big data analytics in the cloud using Qubole. Traditional big data projects require buying hardware, software, and hiring people to manage it, often resulting in underutilized clusters. Qubole offers a platform for interactive big data analytics in the cloud, automating cluster management and providing optimizations to improve performance. It integrates with various data sources and tools, and allows scheduling workflows and analysis. Qubole aims to make big data analytics easier, more cost-effective and faster in the cloud.
Hadoop ecosystem framework n hadoop in live environment
The document provides an overview of the Hadoop ecosystem and how several large companies such as Google, Yahoo, Facebook, and others use Hadoop in production. It discusses the key components of Hadoop including HDFS, MapReduce, HBase, Pig, Hive, Zookeeper and others. It also summarizes some of the large-scale usage of Hadoop at these companies for applications such as web indexing, analytics, search, recommendations, and processing massive amounts of data.
Testing Big Data: Automated ETL Testing of Hadoop
Learn why testing your enterprise's data is pivotal for success with Big Data and Hadoop. See how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.
Hadoop content
This 40-hour course provides training to become a Hadoop developer. It covers Hadoop and big data fundamentals, Hadoop file systems, administering Hadoop clusters, importing and exporting data with Sqoop, processing data using Hive, Pig, and MapReduce, the YARN architecture, NoSQL programming with MongoDB, and reporting tools. The course includes hands-on exercises, datasets, installation support, interview preparation, and guidance from instructors with over 8 years of experience working with Hadoop.
Cloud Computing @Yahoo!
The document discusses Yahoo's cloud computing strategy and services. It outlines Yahoo's unique capabilities due to its large scale user base and data. Yahoo aims to provide both horizontal cloud services that are optimized for its infrastructure and functional cloud services that help developers build applications. Some examples of Yahoo's cloud services are Hadoop, BOSS, YQL and analytics tools. The cloud strategy is focused on driving innovation, ROI and benefiting users, developers and Yahoo.
Hadoop HDFS.ppt
This document provides an overview of big data and Hadoop. It defines big data as high-volume, high-velocity, and high-variety data that requires new techniques to capture value. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for storage and MapReduce for parallel processing. Benefits of Hadoop are its ability to handle large amounts of structured and unstructured data quickly and cost-effectively at large scales.
Big Data with hadoop, Spark and BigQuery (Google cloud next Extended 2017 Kar...
Google Next Extended (https://cloudnext.withgoogle.com/) is an annual Google event focusing on Google cloud technologies. This presentation is from tech talk held in Google Next Extended 2017 Karachi event
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Big-Data Hadoop Tutorials - MindScripts Technologies, Pune
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Hadoop and the Data Warehouse: Point/Counter Point
Hadoop and the Data Warehouse: Point/Counter Point
Hadoop ecosystem framework n hadoop in live environment
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Overview
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Hadoop at Yahoo! -- Hadoop World NY 2009
- 1. Hadoop at Yahoo! Eric Baldeschwieler VP Hadoop Software Development Yahoo!
- 5. The Largest Hadoop User 2006 now Hardware Internal Hadoop Users building new datacenter PB Disk, >82PB Today Nodes, >25,000 Today
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- 15. Futures
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Editor's Notes
- Load Balancing : Brooklyn (DNS) directs users to their local datacenter RSS Feeds : Feed-norm leverages Yahoo Traffic Server to normalize, cache, and proxy site feeds for Auto Apps Image and Video Delivery : All images and thumbnails displayed on the page Substantial part the 20-25 billion objects YCS serves a day Stats Coming Site thumbnails (Auto-apps) These are the Metro applications generated from web sites that are added to the left column Metro is currently storing about 220K thumbnails replicated on both US coasts Usage is currently about 55K/second (heavily cached by YCS) growing 100% month over month Attachment Store Mail uses YMDB (MObStor pre-cursor) to store 10TB of attachments Search Index : Data mining to obtain the top-n user search queries Ads Optimization: On-going refreshes to the Ad ranking model for revenue optimization Content Optimization: Computation of Content centric user profiles to get user segmentation Models generation refresh for content categorization User centric recommendation module Machine Learning: Model creation for various purposes at Yahoo Spam Filters: Utilizing Co-occurrence and other data intensive techniques for mail spam detection
- Load Balancing : Brooklyn (DNS) directs users to their local datacenter RSS Feeds : Feed-norm leverages Yahoo Traffic Server to normalize, cache, and proxy site feeds for Auto Apps Image and Video Delivery : All images and thumbnails displayed on the page Substantial part the 20-25 billion objects YCS serves a day Stats Coming Site thumbnails (Auto-apps) These are the Metro applications generated from web sites that are added to the left column Metro is currently storing about 220K thumbnails replicated on both US coasts Usage is currently about 55K/second (heavily cached by YCS) growing 100% month over month Attachment Store Mail uses YMDB (MObStor pre-cursor) to store 10TB of attachments Search Index : Data mining to obtain the top-n user search queries Ads Optimization: On-going refreshes to the Ad ranking model for revenue optimization Content Optimization: Computation of Content centric user profiles to get user segmentation Models generation refresh for content categorization User centric recommendation module Machine Learning: Model creation for various purposes at Yahoo Spam Filters: Utilizing Co-occurrence and other data intensive techniques for mail spam detection
- Load Balancing : Brooklyn (DNS) directs users to their local datacenter RSS Feeds : Feed-norm leverages Yahoo Traffic Server to normalize, cache, and proxy site feeds for Auto Apps Image and Video Delivery : All images and thumbnails displayed on the page Substantial part the 20-25 billion objects YCS serves a day Stats Coming Site thumbnails (Auto-apps) These are the Metro applications generated from web sites that are added to the left column Metro is currently storing about 220K thumbnails replicated on both US coasts Usage is currently about 55K/second (heavily cached by YCS) growing 100% month over month Attachment Store Mail uses YMDB (MObStor pre-cursor) to store 10TB of attachments Search Index : Data mining to obtain the top-n user search queries Ads Optimization: On-going refreshes to the Ad ranking model for revenue optimization Content Optimization: Computation of Content centric user profiles to get user segmentation Models generation refresh for content categorization User centric recommendation module Machine Learning: Model creation for various purposes at Yahoo Spam Filters: Utilizing Co-occurrence and other data intensive techniques for mail spam detection