This document demonstrates using Hadoop, R, and Google Chart Tools for data visualization. It describes preparing the environment by installing necessary software. It then walks through writing an R script to analyze birth data on HDFS using MapReduce. The results are loaded into a Shiny application which renders interactive visualizations using the googleVis package. This showcases an end-to-end workflow for analyzing large datasets with R on Hadoop and visualizing the results.
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.
Cloudera Sessions - Clinic 1 - Getting Started With HadoopCloudera, Inc.
If you are interested in Hadoop and its capabilities, but you are not sure where to begin, this is the session for you. Learn the basics of Hadoop, see how to spin up a development cluster in the cloud or on-premise, and start exploring ETL processing with SQL and other familiar tools
Integrating R & Hadoop - Text Mining & Sentiment AnalysisAravind Babu
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.
Hadoop World 2011: Mike Olson Keynote PresentationCloudera, Inc.
Now in its fifth year, Apache Hadoop has firmly established itself as the platform of choice for organizations that need to efficiently store, organize, analyze, and harvest valuable insight from the flood of data that they interact with. Since its inception as an early, promising technology that inspired curiosity, Hadoop has evolved into a widely embraced, proven solution used in production to solve a growing number of business problems that were previously impossible to address. In his opening keynote, Mike will reflect on the growth of the Hadoop platform due to the innovative work of a vibrant developer community and on the rapid adoption of the platform among large enterprises. He will highlight how enterprises have transformed themselves into data-driven organizations, highlighting compelling use cases across vertical markets. He will also discuss Cloudera’s plans to stay at the forefront of Hadoop innovation and its role as the trusted solution provider for Hadoop in the enterprise. He will share Cloudera’s view of the road ahead for Hadoop and Big Data and discuss the vital roles for the key constituents across the Hadoop community, ecosystem and enterprises.
Speaking of big data analysis, what comes to mind is possibly using HDFS and MapReduce within Hadoop. But to write a MapReduce program, one must face the problem of learning how to write native java. One might wonder is it possible to use R, the most popular language adapted by data scientist, to implement MapReduce program? And through the integration or R and Hadoop, is it truly one can unleash the power of parallel computing and big data analysis?
This slide introduces how to install RHadoop step by step, and introduces how to write a MapReduce program through R. What is more, this slide will discuss whether RHadoop is really a light for big data analysis, or just another method to write MapReduce Program.
Please mail me if you found any problem toward the slide. EMAIL: tr.ywchiu@gmail.com
談到巨量資料,通常大家腦海中聯想到的就是使用Hadoop 的 MapReduce 和HDFS,但是撰寫MapReduce,則就必須要學會撰寫Java 或透過Thrift 接口才能撰寫。但R是否有辦法運行在Hadoop 上呢 ? 而使用R + Hadoop,是否就真的能結合R強大的分析功能,分析巨量資料呢 ?
本次講題將介紹如何Step by step 在Hadoop 上安裝RHadoop相關套件,並介紹如何撰寫R的MapReduce 程式。更重要的是,此次將探討使用RHadoop 是否為巨量資料分析找到一盞明燈? 或者只是另一套實作方法而已?
Hw09 Production Deep Dive With High AvailabilityCloudera, Inc.
ContextWeb is an online advertisement company that processes large volumes of log data using Hadoop. They process up to 120GB of raw log files per day. Their Hadoop cluster consists of 40 nodes and processes around 2000 MapReduce jobs per day. They developed techniques for partitioning data by date/time and using file revisions to allow incremental processing while ensuring data consistency and freshness of reports.
Common and unique use cases for Apache HadoopBrock Noland
The document provides an overview of Apache Hadoop and common use cases. It describes how Hadoop is well-suited for log processing due to its ability to handle large amounts of data in parallel across commodity hardware. Specifically, it allows processing of log files to be distributed per unit of data, avoiding bottlenecks that can occur when trying to process a single large file sequentially.
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.
Cloudera Sessions - Clinic 1 - Getting Started With HadoopCloudera, Inc.
If you are interested in Hadoop and its capabilities, but you are not sure where to begin, this is the session for you. Learn the basics of Hadoop, see how to spin up a development cluster in the cloud or on-premise, and start exploring ETL processing with SQL and other familiar tools
Integrating R & Hadoop - Text Mining & Sentiment AnalysisAravind Babu
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.
Hadoop World 2011: Mike Olson Keynote PresentationCloudera, Inc.
Now in its fifth year, Apache Hadoop has firmly established itself as the platform of choice for organizations that need to efficiently store, organize, analyze, and harvest valuable insight from the flood of data that they interact with. Since its inception as an early, promising technology that inspired curiosity, Hadoop has evolved into a widely embraced, proven solution used in production to solve a growing number of business problems that were previously impossible to address. In his opening keynote, Mike will reflect on the growth of the Hadoop platform due to the innovative work of a vibrant developer community and on the rapid adoption of the platform among large enterprises. He will highlight how enterprises have transformed themselves into data-driven organizations, highlighting compelling use cases across vertical markets. He will also discuss Cloudera’s plans to stay at the forefront of Hadoop innovation and its role as the trusted solution provider for Hadoop in the enterprise. He will share Cloudera’s view of the road ahead for Hadoop and Big Data and discuss the vital roles for the key constituents across the Hadoop community, ecosystem and enterprises.
Speaking of big data analysis, what comes to mind is possibly using HDFS and MapReduce within Hadoop. But to write a MapReduce program, one must face the problem of learning how to write native java. One might wonder is it possible to use R, the most popular language adapted by data scientist, to implement MapReduce program? And through the integration or R and Hadoop, is it truly one can unleash the power of parallel computing and big data analysis?
This slide introduces how to install RHadoop step by step, and introduces how to write a MapReduce program through R. What is more, this slide will discuss whether RHadoop is really a light for big data analysis, or just another method to write MapReduce Program.
Please mail me if you found any problem toward the slide. EMAIL: tr.ywchiu@gmail.com
談到巨量資料,通常大家腦海中聯想到的就是使用Hadoop 的 MapReduce 和HDFS,但是撰寫MapReduce,則就必須要學會撰寫Java 或透過Thrift 接口才能撰寫。但R是否有辦法運行在Hadoop 上呢 ? 而使用R + Hadoop,是否就真的能結合R強大的分析功能,分析巨量資料呢 ?
本次講題將介紹如何Step by step 在Hadoop 上安裝RHadoop相關套件,並介紹如何撰寫R的MapReduce 程式。更重要的是,此次將探討使用RHadoop 是否為巨量資料分析找到一盞明燈? 或者只是另一套實作方法而已?
Hw09 Production Deep Dive With High AvailabilityCloudera, Inc.
ContextWeb is an online advertisement company that processes large volumes of log data using Hadoop. They process up to 120GB of raw log files per day. Their Hadoop cluster consists of 40 nodes and processes around 2000 MapReduce jobs per day. They developed techniques for partitioning data by date/time and using file revisions to allow incremental processing while ensuring data consistency and freshness of reports.
Common and unique use cases for Apache HadoopBrock Noland
The document provides an overview of Apache Hadoop and common use cases. It describes how Hadoop is well-suited for log processing due to its ability to handle large amounts of data in parallel across commodity hardware. Specifically, it allows processing of log files to be distributed per unit of data, avoiding bottlenecks that can occur when trying to process a single large file sequentially.
The document discusses high availability in Hadoop 2.0 and YARN. It describes the differences between Hadoop 1.0 and 2.0, including changes to configuration files and directories. It then explains the components and workflow of YARN, including how it separates resource management and scheduling from job execution. Finally, it discusses setting up high availability for the NameNode using shared storage and Zookeeper.
The document outlines an introduction to big data analytics with R and Hadoop. It discusses big data concepts, Hadoop, R, and RHadoop. It provides instructions on installing and configuring Java, Hadoop, and R. It describes using RHadoop to perform MapReduce jobs in R to analyze large datasets stored in Hadoop.
1) Hadoop is a framework for distributed processing of large datasets across clusters of computers using a simple programming model.
2) Virtualizing Hadoop enables rapid deployment, high availability, elastic scaling, and consolidation of big data workloads on a common infrastructure.
3) Serengeti is a tool that automates the deployment and management of Hadoop clusters on vSphere in under 30 minutes through simple commands.
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem – HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on – it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.
Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo!`s Hadoop clusters. A key component that enables this efficient operation is data compression. With regard to compression algorithms, there is an underlying tension between compression ratio and compression performance. Consequently, Hadoop provides support for several compression algorithms, including gzip, bzip2, Snappy, LZ4 and others. This plethora of options can make it difficult for users to select appropriate codecs for their MapReduce jobs. This talk attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented. The talk also describes enhancements we have made to the bzip2 codec that improve its performance. This will be of particular interest to the increasing number of users operating on “Big Data” who require the best possible ratios. The impact of using the Intel IPP libraries is also investigated; these have the potential to improve performance significantly. Finally, a few proposals for future enhancements to Hadoop in this area are outlined.
Speaker: Govind Kamat, Member of Technical Staff, Yahoo!
Pattern: an open source project for migrating predictive models onto Apache H...Paco Nathan
This document discusses Pattern, an open source project for migrating predictive models onto Apache Hadoop. It aims to provide enterprise data workflows, sample code, and a roadmap for migrating predictive model markup language (PMML) models to run at scale on Hadoop clusters. The document also discusses how Pattern customers have experimented with using it for their predictive analytics needs.
Managing growth in Production Hadoop DeploymentsDataWorks Summit
This document discusses managing growth in production Hadoop deployments as more users and workloads are added to an initial Hadoop cluster. It begins by describing how an initial small cluster for ETL workloads can succeed but then struggle as data scientists, BI teams, and mobile teams also start using the cluster. The document then covers three main failure categories that can occur due to too much data, too many jobs, or too many users accessing the cluster. It provides examples of pressure points within these categories and recommends strategies to address them like using quotas, optimizing small files, tuning queues, and implementing access controls.
Hadoop is an open-source framework for distributed processing of large datasets across clusters of computers. It allows for the parallel processing of large datasets stored across multiple servers. Hadoop uses HDFS for reliable storage and MapReduce as a programming model for distributed computing. HDFS stores data reliably in blocks across nodes, while MapReduce processes data in parallel using map and reduce functions.
The Search Is Over: Integrating Solr and Hadoop in the Same Cluster to Simpli...lucenerevolution
Presented by M.C. Srivas | MapR. See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
This session addresses the biggest issue facing Big Data – Search, Discovery and Analytics need to be integrated. While creating and maintaining separate SOLR and Hadoop clusters is time consuming, error prone and difficult to keep in synch, most Hadoop installations do not integrate with SOLR within the same cluster. Find out how to easily integrate these capabilities into a single cluster. The session will also touch on some of the technical aspects of Big Data Search including how to; protect against silent index corruption that permeates large distributed clusters, overcome the shard distribution problem by leveraging Hadoop to ensure accurate distributed search results, and provide real-time indexing for distributed search including support for streaming data capture. Srivas will also share relevant experiences from his days at Google where he ran one of the major search infrastructure teams where GFS, BigTable and MapReduce were used extensively.
This document discusses integrating Hadoop and Oracle databases. It begins with an introduction of the presenter and their experience. It then provides an overview of Hadoop and Exadata architectures. Several options for integrating the two are presented, including Fuse, Sqoop, and Oracle connectors. A case study is described where unused Exadata storage servers were configured as a Hadoop cluster, called Exadoop. Testing showed improved performance by adding parallelism and Fuse. Overall the document evaluates using Hadoop and Oracle together.
Pig Tutorial | Twitter Case Study | Apache Pig Script and Commands | EdurekaEdureka!
This Edureka Pig Tutorial ( Pig Tutorial Blog Series: https://goo.gl/KPE94k ) will help you understand the concepts of Apache Pig in depth.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Below are the topics covered in this Pig Tutorial:
1) Entry of Apache Pig
2) Pig vs MapReduce
3) Twitter Case Study on Apache Pig
4) Apache Pig Architecture
5) Pig Components
6) Pig Data Model
7) Running Pig Commands and Pig Scripts (Log Analysis)
Hadoop Summit San Jose 2013: Compression Options in Hadoop - A Tale of Tradeo...Sumeet Singh
Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo's Hadoop clusters. A key component that enables this efficient operation is data compression.
With regard to compression algorithms, there is an underlying tension between compression ratio and compression performance. Consequently, Hadoop provides support for several compression algorithms, including gzip, bzip2, Snappy, LZ4 and others. This plethora of options can make it difficult for users to select appropriate codecs for their MapReduce jobs. This paper attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented.
The paper also describes enhancements we have made to the bzip2 codec that improve its performance. This will be of particular interest to the increasing number of users operating on "Big Data" who require the best possible ratios. The impact of using the Intel IPP libraries is also investigated; these have the potential to improve performance significantly. Finally, a few proposals for future enhancements to Hadoop in this area are outlined.
Extend starfish to Support the Growing Hadoop EcosystemFei Dong
The document discusses extending the Starfish system to optimize multi-job workflows, iterative workflows, and key-value stores like HBase. It describes applying Starfish's profiling and optimization capabilities to the Cascading workflow system and supporting iterative MapReduce jobs. Optimizing the Alidade geolocation application is used as a case study.
This document summarizes a presentation about integrating Hadoop and Oracle technologies. It discusses how Hadoop is growing rapidly in popularity and can be used to manage large volumes of data more cost effectively. It then outlines several options for integrating Hadoop and Oracle, including using Oracle's Fuse, Sqoop and Big Data Connectors. A case study is presented where unused Exadata storage servers were configured as a Hadoop cluster to analyze HDFS data using Oracle SQL and external tables. Testing showed the Fuse integration performed the best for loading data between the systems.
NYC-Meetup- Introduction to Hadoop EchosystemAL500745425
This document provides an overview of the Hadoop ecosystem. It discusses what big data is, characteristics of big data like volume, velocity and variety. It then describes the problems with legacy solutions and how Hadoop provides an improved approach by processing data locally, expecting hardware failures and handling failover elegantly. The core components of Hadoop like HDFS, MapReduce, YARN and HBase are explained. Finally, it discusses what's next in terms of downloading Hadoop, using support and training resources from Hortonworks.
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.
The document discusses Jeff Hammerbacher's presentation on Hadoop, Cloudera, and eBay. It begins with introductions and an outline of topics to be covered, including what Hadoop is (its core components HDFS and MapReduce), how it is being used at Facebook and eBay, and what Cloudera is building. The presentation then goes into detailed explanations of Hadoop, its architecture and subprojects, as well as use cases at large companies.
Pig Latin is a language for analyzing large datasets that combines aspects of SQL and MapReduce. It allows users to specify data transformations through a sequence of steps, similar to writing a program. Each step performs a single operation like filtering, grouping, or aggregation. Pig Latin programs are compiled into MapReduce jobs that can run on Hadoop. The language aims to be more intuitive for programmers compared to SQL while avoiding the rigidity of MapReduce. It supports user-defined functions, nested data types, and operating directly on files without importing data.
Ever wonder what Hadoop might look like in 12 months or 24 months or longer? Apache Hadoop MapReduce has undergone a complete re-haul to emerge as Apache Hadoop YARN, a generic compute fabric to support MapReduce and other application paradigms. As a result, Hadoop looks very different from itself 12 months ago. This talk will take you through some ideas for YARN itself and the many myriad ways it is really moving the needle for MapReduce, Pig, Hive, Cascading and other data-processing tools in the Hadoop ecosystem.
OSDC 2013 | Introduction into Hadoop by Olivier RenaultNETWAYS
Hortonworks is a company that was founded in 2011 to focus on developing and supporting Apache Hadoop for enterprise use. They distribute the Hortonworks Data Platform (HDP), which is the only 100% open source enterprise Hadoop distribution. HDP includes core Hadoop components like HDFS, YARN, MapReduce as well as data services like Hive, Pig, HBase. It also includes operational services to help manage and monitor large Hadoop clusters.
The document discusses high availability in Hadoop 2.0 and YARN. It describes the differences between Hadoop 1.0 and 2.0, including changes to configuration files and directories. It then explains the components and workflow of YARN, including how it separates resource management and scheduling from job execution. Finally, it discusses setting up high availability for the NameNode using shared storage and Zookeeper.
The document outlines an introduction to big data analytics with R and Hadoop. It discusses big data concepts, Hadoop, R, and RHadoop. It provides instructions on installing and configuring Java, Hadoop, and R. It describes using RHadoop to perform MapReduce jobs in R to analyze large datasets stored in Hadoop.
1) Hadoop is a framework for distributed processing of large datasets across clusters of computers using a simple programming model.
2) Virtualizing Hadoop enables rapid deployment, high availability, elastic scaling, and consolidation of big data workloads on a common infrastructure.
3) Serengeti is a tool that automates the deployment and management of Hadoop clusters on vSphere in under 30 minutes through simple commands.
Hadoop is commonly used for processing large swaths of data in batch. While many of the necessary building blocks for data processing exist within the Hadoop ecosystem – HDFS, MapReduce, HBase, Hive, Pig, Oozie, and so on – it can be a challenge to assemble and operationalize them as a production ETL platform. This presentation covers one approach to data ingest, organization, format selection, process orchestration, and external system integration, based on collective experience acquired across many production Hadoop deployments.
Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo!`s Hadoop clusters. A key component that enables this efficient operation is data compression. With regard to compression algorithms, there is an underlying tension between compression ratio and compression performance. Consequently, Hadoop provides support for several compression algorithms, including gzip, bzip2, Snappy, LZ4 and others. This plethora of options can make it difficult for users to select appropriate codecs for their MapReduce jobs. This talk attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented. The talk also describes enhancements we have made to the bzip2 codec that improve its performance. This will be of particular interest to the increasing number of users operating on “Big Data” who require the best possible ratios. The impact of using the Intel IPP libraries is also investigated; these have the potential to improve performance significantly. Finally, a few proposals for future enhancements to Hadoop in this area are outlined.
Speaker: Govind Kamat, Member of Technical Staff, Yahoo!
Pattern: an open source project for migrating predictive models onto Apache H...Paco Nathan
This document discusses Pattern, an open source project for migrating predictive models onto Apache Hadoop. It aims to provide enterprise data workflows, sample code, and a roadmap for migrating predictive model markup language (PMML) models to run at scale on Hadoop clusters. The document also discusses how Pattern customers have experimented with using it for their predictive analytics needs.
Managing growth in Production Hadoop DeploymentsDataWorks Summit
This document discusses managing growth in production Hadoop deployments as more users and workloads are added to an initial Hadoop cluster. It begins by describing how an initial small cluster for ETL workloads can succeed but then struggle as data scientists, BI teams, and mobile teams also start using the cluster. The document then covers three main failure categories that can occur due to too much data, too many jobs, or too many users accessing the cluster. It provides examples of pressure points within these categories and recommends strategies to address them like using quotas, optimizing small files, tuning queues, and implementing access controls.
Hadoop is an open-source framework for distributed processing of large datasets across clusters of computers. It allows for the parallel processing of large datasets stored across multiple servers. Hadoop uses HDFS for reliable storage and MapReduce as a programming model for distributed computing. HDFS stores data reliably in blocks across nodes, while MapReduce processes data in parallel using map and reduce functions.
The Search Is Over: Integrating Solr and Hadoop in the Same Cluster to Simpli...lucenerevolution
Presented by M.C. Srivas | MapR. See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
This session addresses the biggest issue facing Big Data – Search, Discovery and Analytics need to be integrated. While creating and maintaining separate SOLR and Hadoop clusters is time consuming, error prone and difficult to keep in synch, most Hadoop installations do not integrate with SOLR within the same cluster. Find out how to easily integrate these capabilities into a single cluster. The session will also touch on some of the technical aspects of Big Data Search including how to; protect against silent index corruption that permeates large distributed clusters, overcome the shard distribution problem by leveraging Hadoop to ensure accurate distributed search results, and provide real-time indexing for distributed search including support for streaming data capture. Srivas will also share relevant experiences from his days at Google where he ran one of the major search infrastructure teams where GFS, BigTable and MapReduce were used extensively.
This document discusses integrating Hadoop and Oracle databases. It begins with an introduction of the presenter and their experience. It then provides an overview of Hadoop and Exadata architectures. Several options for integrating the two are presented, including Fuse, Sqoop, and Oracle connectors. A case study is described where unused Exadata storage servers were configured as a Hadoop cluster, called Exadoop. Testing showed improved performance by adding parallelism and Fuse. Overall the document evaluates using Hadoop and Oracle together.
Pig Tutorial | Twitter Case Study | Apache Pig Script and Commands | EdurekaEdureka!
This Edureka Pig Tutorial ( Pig Tutorial Blog Series: https://goo.gl/KPE94k ) will help you understand the concepts of Apache Pig in depth.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Below are the topics covered in this Pig Tutorial:
1) Entry of Apache Pig
2) Pig vs MapReduce
3) Twitter Case Study on Apache Pig
4) Apache Pig Architecture
5) Pig Components
6) Pig Data Model
7) Running Pig Commands and Pig Scripts (Log Analysis)
Hadoop Summit San Jose 2013: Compression Options in Hadoop - A Tale of Tradeo...Sumeet Singh
Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo's Hadoop clusters. A key component that enables this efficient operation is data compression.
With regard to compression algorithms, there is an underlying tension between compression ratio and compression performance. Consequently, Hadoop provides support for several compression algorithms, including gzip, bzip2, Snappy, LZ4 and others. This plethora of options can make it difficult for users to select appropriate codecs for their MapReduce jobs. This paper attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented.
The paper also describes enhancements we have made to the bzip2 codec that improve its performance. This will be of particular interest to the increasing number of users operating on "Big Data" who require the best possible ratios. The impact of using the Intel IPP libraries is also investigated; these have the potential to improve performance significantly. Finally, a few proposals for future enhancements to Hadoop in this area are outlined.
Extend starfish to Support the Growing Hadoop EcosystemFei Dong
The document discusses extending the Starfish system to optimize multi-job workflows, iterative workflows, and key-value stores like HBase. It describes applying Starfish's profiling and optimization capabilities to the Cascading workflow system and supporting iterative MapReduce jobs. Optimizing the Alidade geolocation application is used as a case study.
This document summarizes a presentation about integrating Hadoop and Oracle technologies. It discusses how Hadoop is growing rapidly in popularity and can be used to manage large volumes of data more cost effectively. It then outlines several options for integrating Hadoop and Oracle, including using Oracle's Fuse, Sqoop and Big Data Connectors. A case study is presented where unused Exadata storage servers were configured as a Hadoop cluster to analyze HDFS data using Oracle SQL and external tables. Testing showed the Fuse integration performed the best for loading data between the systems.
NYC-Meetup- Introduction to Hadoop EchosystemAL500745425
This document provides an overview of the Hadoop ecosystem. It discusses what big data is, characteristics of big data like volume, velocity and variety. It then describes the problems with legacy solutions and how Hadoop provides an improved approach by processing data locally, expecting hardware failures and handling failover elegantly. The core components of Hadoop like HDFS, MapReduce, YARN and HBase are explained. Finally, it discusses what's next in terms of downloading Hadoop, using support and training resources from Hortonworks.
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.
The document discusses Jeff Hammerbacher's presentation on Hadoop, Cloudera, and eBay. It begins with introductions and an outline of topics to be covered, including what Hadoop is (its core components HDFS and MapReduce), how it is being used at Facebook and eBay, and what Cloudera is building. The presentation then goes into detailed explanations of Hadoop, its architecture and subprojects, as well as use cases at large companies.
Pig Latin is a language for analyzing large datasets that combines aspects of SQL and MapReduce. It allows users to specify data transformations through a sequence of steps, similar to writing a program. Each step performs a single operation like filtering, grouping, or aggregation. Pig Latin programs are compiled into MapReduce jobs that can run on Hadoop. The language aims to be more intuitive for programmers compared to SQL while avoiding the rigidity of MapReduce. It supports user-defined functions, nested data types, and operating directly on files without importing data.
Ever wonder what Hadoop might look like in 12 months or 24 months or longer? Apache Hadoop MapReduce has undergone a complete re-haul to emerge as Apache Hadoop YARN, a generic compute fabric to support MapReduce and other application paradigms. As a result, Hadoop looks very different from itself 12 months ago. This talk will take you through some ideas for YARN itself and the many myriad ways it is really moving the needle for MapReduce, Pig, Hive, Cascading and other data-processing tools in the Hadoop ecosystem.
OSDC 2013 | Introduction into Hadoop by Olivier RenaultNETWAYS
Hortonworks is a company that was founded in 2011 to focus on developing and supporting Apache Hadoop for enterprise use. They distribute the Hortonworks Data Platform (HDP), which is the only 100% open source enterprise Hadoop distribution. HDP includes core Hadoop components like HDFS, YARN, MapReduce as well as data services like Hive, Pig, HBase. It also includes operational services to help manage and monitor large Hadoop clusters.
Tez: Accelerating Data Pipelines - fifthelt3rmin4t0r
This document provides an overview of Tez, an Apache project that provides a framework for executing data processing jobs on Hadoop clusters. Tez allows expressing data processing jobs as directed acyclic graphs (DAGs) of tasks and executes these tasks in a optimized manner. It addresses limitations of MapReduce by providing a more flexible execution engine that can optimize performance and resource utilization.
Hortonworks' mission is to enable modern data architectures by delivering an enterprise-ready Apache Hadoop platform. They contribute the majority of code to Apache Hadoop and its related projects. Hortonworks develops the Hortonworks Data Platform (HDP), which provides core Hadoop services along with operational and data services to make Hadoop an enterprise data platform. Hortonworks aims to power data architectures by enabling Hadoop as a multi-purpose platform for batch, interactive, streaming and other workloads through projects like YARN, Tez, and improvements to Hive.
This document provides an overview of installing and programming with Apache Spark on the Hortonworks Data Platform (HDP). It discusses how Spark fits within HDP and can be used for batch processing, streaming, SQL queries and machine learning. The document outlines how to install Spark on HDP using Ambari and describes Spark programming with Resilient Distributed Datasets (RDDs), transformations, actions and caching/persistence. It provides examples of Spark APIs and programming patterns.
Pig is a platform for analyzing large datasets that sits between low-level MapReduce programming and high-level SQL queries. It provides a language called Pig Latin that allows users to specify data analysis programs without dealing with low-level details. Pig Latin scripts are compiled into sequences of MapReduce jobs for execution. HCatalog allows data to be shared between Pig, Hive, and other tools by reading metadata about schemas, locations, and formats.
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
This document provides an overview of installing and programming with Apache Spark on Hortonworks Data Platform (HDP). It introduces Spark and its components, benefits over other frameworks, and Hortonworks' commitment to Spark. The document outlines an example Spark programming workflow using Resilient Distributed Datasets (RDDs) in Scala, and covers common RDD transformations, actions, and persistence methods. It also discusses Spark deployment modes like standalone and on YARN, and reference HDP architectures using Spark.
1) The webinar covered Apache Hadoop on the open cloud, focusing on key drivers for Hadoop adoption like new types of data and business applications.
2) Requirements for enterprise Hadoop include core services, interoperability, enterprise readiness, and leveraging existing skills in development, operations, and analytics.
3) The webinar demonstrated Hortonworks Apache Hadoop running on Rackspace's Cloud Big Data Platform, which is built on OpenStack for security, optimization, and an open platform.
This document provides an introduction to Apache Pig, including:
- Pig is a system for processing large unstructured data using HDFS and MapReduce. It uses a high-level data flow language called Pig Latin.
- Pig aims to increase programmer productivity by abstracting low-level MapReduce jobs and providing a procedural language for parallel data flows.
- Pig components include the Pig engine for parsing, optimizing, and executing queries, and the Grunt shell for running interactive commands.
- The document then covers Pig data types, input/output, relational operations, user-defined functions, and new features in Pig version 0.10.0.
Introduction to the Hortonworks YARN Ready ProgramHortonworks
The recently launched YARN Ready Program will accelerate multi-workload Hadoop in the Enterprise. The program enables developers to integrate new and existing applications with YARN-based Hadoop. We will cover:
--the program and it's benefits
--why it is important to customers
--tools and guides to help you get started
--technical resources to support you
--marketing recognition you can leverage
Storm Demo Talk - Colorado Springs May 2015Mac Moore
The document discusses real-time processing capabilities in Hadoop and Hortonworks Data Platform (HDP). It begins with an introduction to Hortonworks and an overview of real-time streaming architectures on HDP. It then demonstrates streaming capabilities with and without predictive analytics additions. The document highlights how HDP provides a centralized architecture and open data platform to enable real-time and batch processing of any type of data for analytics applications.
Spark is an open-source software framework for rapid calculations on in-memory datasets. It uses Resilient Distributed Datasets (RDDs) that can be recreated if lost and supports transformations and actions on RDDs. Spark is useful for batch, interactive, and real-time processing across various problem domains like SQL, streaming, and machine learning via MLlib.
Hadoop 2.0 - Solving the Data Quality ChallengeInside Analysis
The Briefing Room with Dr. Claudia Imhoff and RedPoint Global
Live Webcast on July 22, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=7bb4cbc33402c3b5f649343052cb9a6d
Whether data is big or small, quality remains the critical characteristic. While traditional approaches to cleansing data have made strides, nonetheless, data quality remains a serious hurdle for all organizations. This is especially true for identity resolution in customer data, but also for a range of other data sets, including social, supply chain, financial and other domains. One of the most promising approaches for solving this decades-old challenge incorporates the power of massive parallel processing, a la Hadoop.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Claudia Imhoff, who will explain how Hadoop 2.0 and its YARN architecture can make a serious impact on the previously intractable problem of data quality. She’ll be briefed by George Corugedo of RedPoint Global, who will show how his company’s platform can serve as a super-charged marshaling area for accessing, cleansing and delivering high-quality data. He’ll explain how RedPoint was one of the first applications to be certified for running on YARN, which is the latest rendition of the now-ubiquitous Hadoop.
Visit InsideAnlaysis.com for more information.
Supporting Financial Services with a More Flexible Approach to Big DataHortonworks
The document discusses how Hortonworks Data Platform (HDP) enables a modern data architecture with Apache Hadoop. HDP provides a common data set stored in HDFS that can be accessed through various applications for batch, interactive, and real-time processing. This allows organizations to store all their data in one place and access it simultaneously through multiple means. YARN is the architectural center of HDP and enables this modern data architecture. HDP also provides enterprise capabilities like security, governance, and operations to make Hadoop suitable for business use.
Introduction to Microsoft HDInsight and BI ToolsDataWorks Summit
This document discusses Hortonworks Data Platform (HDP) for Windows. It includes an agenda for the presentation which covers an introduction to HDP for Windows, integrating HDP with Microsoft tools, and a demo. The document lists the speakers and provides information on Windows support for Hadoop components. It describes what is included in HDP for Windows, such as deployment choices and full interoperability across platforms. Integration with Microsoft tools like SQL Server, Excel, and Power BI is highlighted. A demo of using Excel to interact with HDP is promised.
2013 Nov 20 Toronto Hadoop User Group (THUG) - Hadoop 2.2.0Adam Muise
The document discusses Hadoop 2.2.0 and new features in YARN and MapReduce. Key points include: YARN introduces a new application framework and resource management system that replaces the jobtracker, allowing multiple data processing engines besides MapReduce; MapReduce is now a library that runs on YARN; Tez is introduced as a new data processing framework to improve performance beyond MapReduce.
Discover HDP 2.1: Apache Solr for Hadoop SearchHortonworks
This document appears to be a presentation about Apache Solr for Hadoop search using the Hortonworks Data Platform (HDP). The agenda includes an overview of Apache Solr and Hadoop search, a demo of Hadoop search, and a question and answer section. The presentation discusses how Solr provides scalable indexing of data stored in HDFS and powerful search capabilities. It also includes a reference architecture showing how Solr integrates with Hadoop for search and indexing.
This document discusses Apache Slider, an open source project that aims to make it easy to deploy existing applications onto YARN clusters. Slider allows long-running applications to be managed on YARN through components like Application Masters, agents, and application packages. It also integrates with cluster management frameworks like Ambari to provision, monitor and manage applications running on YARN. The document outlines key Slider concepts and provides examples of application specification and configuration files used by Slider.
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Predictions for 2019 and Beyond: Data at the Heart of Your IoT StrategyHortonworks
Forrester forecasts* that direct spending on the Internet of Things (IoT) will exceed $400 Billion by 2023. From manufacturing and utilities, to oil & gas and transportation, IoT improves visibility, reduces downtime, and creates opportunities for entirely new business models.
But successful IoT implementations require far more than simply connecting sensors to a network. The data generated by these devices must be collected, aggregated, cleaned, processed, interpreted, understood, and used. Data-driven decisions and actions must be taken, without which an IoT implementation is bound to fail.
https://hortonworks.com/webinar/iot-predictions-2019-beyond-data-heart-iot-strategy/
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
Johns Hopkins - Using Hadoop to Secure Access Log EventsHortonworks
In this webinar, we talk with experts from Johns Hopkins as they share techniques and lessons learned in real-world Apache Hadoop implementation.
https://hortonworks.com/webinar/johns-hopkins-using-hadoop-securely-access-log-events/
Catch a Hacker in Real-Time: Live Visuals of Bots and Bad GuysHortonworks
Cybersecurity today is a big data problem. There’s a ton of data landing on you faster than you can load, let alone search it. In order to make sense of it, we need to act on data-in-motion, use both machine learning, and the most advanced pattern recognition system on the planet: your SOC analysts. Advanced visualization makes your analysts more efficient, helps them find the hidden gems, or bombs in masses of logs and packets.
https://hortonworks.com/webinar/catch-hacker-real-time-live-visuals-bots-bad-guys/
We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
Curing Kafka Blindness with Hortonworks Streams Messaging ManagerHortonworks
With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers.
With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer.
https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/
Interpretation Tool for Genomic Sequencing Data in Clinical EnvironmentsHortonworks
The healthcare industry—with its huge volumes of big data—is ripe for the application of analytics and machine learning. In this webinar, Hortonworks and Quanam present a tool that uses machine learning and natural language processing in the clinical classification of genomic variants to help identify mutations and determine clinical significance.
Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
The document provides an overview of Apache Druid, an open-source distributed real-time analytics database. It discusses Druid's architecture including segments, indexing, and nodes like brokers, historians and coordinators. It also covers integrating Druid with Hortonworks Data Platform for unified querying and visualization of streaming and historical data.
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
Gaining business advantages from big data is moving beyond just the efficient storage and deep analytics on diverse data sources to using AI methods and analytics on streaming data to catch insights and take action at the edge of the network.
https://hortonworks.com/webinar/accelerating-data-science-real-time-analytics-scale/
TIME SERIES: APPLYING ADVANCED ANALYTICS TO INDUSTRIAL PROCESS DATAHortonworks
Thanks to sensors and the Internet of Things, industrial processes now generate a sea of data. But are you plumbing its depths to find the insight it contains, or are you just drowning in it? Now, Hortonworks and Seeq team to bring advanced analytics and machine learning to time-series data from manufacturing and industrial processes.
Blockchain with Machine Learning Powered by Big Data: Trimble Transportation ...Hortonworks
Trimble Transportation Enterprise is a leading provider of enterprise software to over 2,000 transportation and logistics companies. They have designed an architecture that leverages Hortonworks Big Data solutions and Machine Learning models to power up multiple Blockchains, which improves operational efficiency, cuts down costs and enables building strategic partnerships.
https://hortonworks.com/webinar/blockchain-with-machine-learning-powered-by-big-data-trimble-transportation-enterprise/
Delivering Real-Time Streaming Data for Healthcare Customers: ClearsenseHortonworks
For years, the healthcare industry has had problems of data scarcity and latency. Clearsense solved the problem by building an open-source Hortonworks Data Platform (HDP) solution while providing decades worth of clinical expertise. Clearsense is delivering smart, real-time streaming data, to its healthcare customers enabling mission-critical data to feed clinical decisions.
https://hortonworks.com/webinar/delivering-smart-real-time-streaming-data-healthcare-customers-clearsense/
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
The document discusses Apache NiFi and streaming change data capture (CDC) with Attunity Replicate. It provides an overview of NiFi's capabilities for dataflow management and visualization. It then demonstrates how Attunity Replicate can be used for real-time CDC to capture changes from source databases and deliver them to NiFi for further processing, enabling use cases across multiple industries. Examples of source systems include SAP, Oracle, SQL Server, and file data, with targets including Hadoop, data warehouses, and cloud data stores.