Eric Baldeschwieler, CTO of Hortonworks, presents on Apache Hadoop for big science. He discusses the history and motivation for Hadoop, including its origins at Yahoo in 2005. Baldeschwieler outlines several use cases for Hadoop in domains like genomics, oil and gas, and high-energy physics. He also explores futures for Hadoop, including innovations in YARN and the Stinger initiative to improve Hive for interactive queries.
In this webinar, we'll:
-Examine the key drivers and use cases for High Availability, performance and scalability for Apache Hadoop.
-Walk through an overview of reference architecture for a Non-Stop Hadoop implementation.
-Show how you can get started with Non-Stop Hadoop with the Hortonworks Data Platform.
Hortonworks Yarn Code Walk Through January 2014Hortonworks
This slide deck accompanies the Webinar recording YARN Code Walk through on Jan. 22, 2014, on Hortonworks.com/webinars under Past Webinars, or
https://hortonworks.webex.com/hortonworks/lsr.php?AT=pb&SP=EC&rID=129468197&rKey=b645044305775657
Discover hdp 2.2: Data storage innovations in Hadoop Distributed Filesystem (...Hortonworks
Hortonworks Data Platform 2.2 include HDFS for data storage . In this 30-minute webinar, we discussed data storage innovations, including Heterogeneous storage, encryption, and operational security enhancements.
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
Hortonworks Technical Workshop: Real Time Monitoring with Apache HadoopHortonworks
Real Time Monitoring requires a high scalable infrastructure of message bus, database, distributed event processing and scalable analytics engine. By bringing together leading open source projects of Apache Kafka, Apache HBase, Apache Storm and Apache Hive, the Hortonworks Data Platform offers a comprehensive Real Time Analysis platform. In this session, we will provide an in-depth overview all the key technology components and demonstrate a working solution for monitoring a fleet of trucks.
Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=0278dc8aa49a9991e1ce436c71f53d30
In this webinar, we'll:
-Examine the key drivers and use cases for High Availability, performance and scalability for Apache Hadoop.
-Walk through an overview of reference architecture for a Non-Stop Hadoop implementation.
-Show how you can get started with Non-Stop Hadoop with the Hortonworks Data Platform.
Hortonworks Yarn Code Walk Through January 2014Hortonworks
This slide deck accompanies the Webinar recording YARN Code Walk through on Jan. 22, 2014, on Hortonworks.com/webinars under Past Webinars, or
https://hortonworks.webex.com/hortonworks/lsr.php?AT=pb&SP=EC&rID=129468197&rKey=b645044305775657
Discover hdp 2.2: Data storage innovations in Hadoop Distributed Filesystem (...Hortonworks
Hortonworks Data Platform 2.2 include HDFS for data storage . In this 30-minute webinar, we discussed data storage innovations, including Heterogeneous storage, encryption, and operational security enhancements.
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
Hortonworks Technical Workshop: Real Time Monitoring with Apache HadoopHortonworks
Real Time Monitoring requires a high scalable infrastructure of message bus, database, distributed event processing and scalable analytics engine. By bringing together leading open source projects of Apache Kafka, Apache HBase, Apache Storm and Apache Hive, the Hortonworks Data Platform offers a comprehensive Real Time Analysis platform. In this session, we will provide an in-depth overview all the key technology components and demonstrate a working solution for monitoring a fleet of trucks.
Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=0278dc8aa49a9991e1ce436c71f53d30
Hortonworks - What's Possible with a Modern Data Architecture?Hortonworks
This is Mark Ledbetter's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?” Mark is vice president for industry solutions at Hortonworks. He has more than twenty-five years experience in the software industry with a focus on Retail and supply chain.
This is the presentation from the "Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS" webinar on May 28, 2014. Rohit Bahkshi, a senior product manager at Hortonworks, and Vinod Vavilapalli, PMC for Apache Hadoop, discuss an overview of YARN in HDFS and new features in HDP 2.1. Those new features include: HDFS extended ACLs, HTTPs wire encryption, HDFS DataNode caching, resource manager high availability, application timeline server, and capacity scheduler pre-emption.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Enterprise Hadoop with Hortonworks and Nimble StorageHortonworks
Join us to learn how Hortonworks Data Platform and Nimble Storage provide an enterprise-ready data platform for multi-workload data processing. HDP supports an array of processing methods — from batch through interactive to real-time, with key capabilities required of an enterprise data platform — spanning Governance, Security and Operations. Nimble Storage provides the performance, capacity, and availability for HDP and allows you to take advantage of Hadoop with minimal changes to existing data architectures and skillsets.
Integrating Hadoop Into the Enterprise – Hadoop Summit 2012Jonathan Seidman
A look at common patterns being applied to leverage Hadoop with traditional data management systems and the emerging landscape of tools which provide access and analysis of Hadoop data with existing systems such as data warehouses, relational databases, and business intelligence tools.
YARN Ready: Integrating to YARN with Tez Hortonworks
YARN Ready webinar series helps developers integrate their applications to YARN. Tez is one vehicle to do that. We take a deep dive including code review to help you get started.
Building a Big Data platform with the Hadoop ecosystemGregg Barrett
This presentation provides a brief insight into a Big Data platform using the Hadoop ecosystem.
To this end the presentation will touch on:
-views of the Big Data ecosystem and it’s components
-an example of a Hadoop cluster
-considerations when selecting a Hadoop distribution
-some of the Hadoop distributions available
-a recommended Hadoop distribution
Rescue your Big Data from Downtime with HP Operations Bridge and Apache HadoopHortonworks
How can you simplify the management and monitoring of your Hadoop environment? Ensure IT can focus on the right business priorities supported by Hadoop? Take a look at this presentation and learn how you can simplify the management and monitoring of your Hadoop environment, and ensure IT can focus on the right business priorities supported by Hadoop.
Learn how when an organizations combine HP and Vertica Analytics Platform and Hortonworks, they can quickly explore and analyze broad variety of data types to transform to actionable information that allows them to better understand how their customers and site visitors interact with their business, offline and online.
Stinger.Next by Alan Gates of HortonworksData Con LA
ver the last 13 months the Apache Hive community, which included 145 developers and 44 companies working together through the Stinger initiative, delivered 390,000 lines of code and 1600 resolved JIRA tickets. This is only the beginning. The Hive community has already started the next phase of extending the Speed, Scale, and SQL compliance in Hive. As Hadoop 2.0 with YARN evolves to enable a dizzying array of powerful engines that allow us to interact with ever growing data in new ways, well known tools such as SQL need to scale with it. This session will provide a technical illustration of the challenges facing SQL on Hadoop today and what the road ahead looks like as the user community drives more innovation. Stinger.next is the next multi-phase initiative to evolve Hive as the de facto SQL engine for Hadoop designed to deliver Speed, Scale and better SQL.
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
Many enterprises are turning to Apache Hadoop to enable Big Data Analytics and reduce the costs of traditional data warehousing. Yet, it is hard to succeed when 80% of the time is spent on moving data and only 20% on using it. It’s time to swap the 80/20! The Big Data experts at Attunity and Hortonworks have a solution for accelerating data movement into and out of Hadoop that enables faster time-to-value for Big Data projects and a more complete and trusted view of your business. Join us to learn how this solution can work for you.
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
( EMC World 2012 ) :Apache Hadoop is now enterprise ready. This session reviews the features/roadmap of Hadoop. We will review some of the key capabilities of GPHD 1.x and our plans for 2012.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
Predictive Analytics and Machine Learning…with SAS and Apache HadoopHortonworks
In this interactive webinar, we'll walk through use cases on how you can use advanced analytics like SAS Visual Statistics and In-Memory Statistic with Hortonworks’ data platform (HDP) to reveal insights in your big data and redefine how your organization solves complex problems.
Create a Smarter Data Lake with HP Haven and Apache HadoopHortonworks
An organization’s information is spread across multiple repositories, on-premise and in the cloud, with limited ability to correlate information and derive insights. The Smart Content Hub solution from HP and Hortonworks enables a shared content infrastructure that transparently synchronizes information with existing systems and offers an open standards-based platform for deep analysis and data monetization.
- Leverage 100% of your data: Text, images, audio, video, and many more data types can be automatically consumed and enriched using HP Haven (powered by HP IDOL and HP Vertica), making it possible to integrate this valuable content and insights into various line of business applications.
- Democratize and enable multi-dimensional content analysis: - Empower your analysts, business users, and data scientists to search and analyze Hadoop data with ease, using the 100% open source Hortonworks Data Platform.
- Extend the enterprise data warehouse: Synchronize and manage content from content management systems, and crack open the files in whatever format they happen to be in.
- Dramatically reduce complexity with enterprise-ready SQL engine: Tap into the richest analytics that support JOINs, complex data types, and other capabilities only available with HP Vertica SQL on the Hortonworks Data Platform.
Speakers:
- Ajay Singh, Director, Technical Channels, Hortonworks
- Will Gardella, Product Management, HP Big Data
Mr. Slim Baltagi is a Systems Architect at Hortonworks, with over 4 years of Hadoop experience working on 9 Big Data projects: Advanced Customer Analytics, Supply Chain Analytics, Medical Coverage Discovery, Payment Plan Recommender, Research Driven Call List for Sales, Prime Reporting Platform, Customer Hub, Telematics, Historical Data Platform; with Fortune 100 clients and global companies from Financial Services, Insurance, Healthcare and Retail.
Mr. Slim Baltagi has worked in various architecture, design, development and consulting roles at.
Accenture, CME Group, TransUnion, Syntel, Allstate, TransAmerica, Credit Suisse, Chicago Board Options Exchange, Federal Reserve Bank of Chicago, CNA, Sears, USG, ACNielsen, Deutshe Bahn.
Mr. Baltagi has also over 14 years of IT experience with an emphasis on full life cycle development of Enterprise Web applications using Java and Open-Source software. He holds a master’s degree in mathematics and is an ABD in computer science from Université Laval, Québec, Canada.
Languages: Java, Python, JRuby, JEE , PHP, SQL, HTML, XML, XSLT, XQuery, JavaScript, UML, JSON
Databases: Oracle, MS SQL Server, MYSQL, PostreSQL
Software: Eclipse, IBM RAD, JUnit, JMeter, YourKit, PVCS, CVS, UltraEdit, Toad, ClearCase, Maven, iText, Visio, Japser Reports, Alfresco, Yslow, Terracotta, Toad, SoapUI, Dozer, Sonar, Git
Frameworks: Spring, Struts, AppFuse, SiteMesh, Tiles, Hibernate, Axis, Selenium RC, DWR Ajax , Xstream
Distributed Computing/Big Data: Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, HBase, R, RHadoop, Cloudera CDH4, MapR M7, Hortonworks HDP 2.1
Hortonworks - What's Possible with a Modern Data Architecture?Hortonworks
This is Mark Ledbetter's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?” Mark is vice president for industry solutions at Hortonworks. He has more than twenty-five years experience in the software industry with a focus on Retail and supply chain.
This is the presentation from the "Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS" webinar on May 28, 2014. Rohit Bahkshi, a senior product manager at Hortonworks, and Vinod Vavilapalli, PMC for Apache Hadoop, discuss an overview of YARN in HDFS and new features in HDP 2.1. Those new features include: HDFS extended ACLs, HTTPs wire encryption, HDFS DataNode caching, resource manager high availability, application timeline server, and capacity scheduler pre-emption.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Enterprise Hadoop with Hortonworks and Nimble StorageHortonworks
Join us to learn how Hortonworks Data Platform and Nimble Storage provide an enterprise-ready data platform for multi-workload data processing. HDP supports an array of processing methods — from batch through interactive to real-time, with key capabilities required of an enterprise data platform — spanning Governance, Security and Operations. Nimble Storage provides the performance, capacity, and availability for HDP and allows you to take advantage of Hadoop with minimal changes to existing data architectures and skillsets.
Integrating Hadoop Into the Enterprise – Hadoop Summit 2012Jonathan Seidman
A look at common patterns being applied to leverage Hadoop with traditional data management systems and the emerging landscape of tools which provide access and analysis of Hadoop data with existing systems such as data warehouses, relational databases, and business intelligence tools.
YARN Ready: Integrating to YARN with Tez Hortonworks
YARN Ready webinar series helps developers integrate their applications to YARN. Tez is one vehicle to do that. We take a deep dive including code review to help you get started.
Building a Big Data platform with the Hadoop ecosystemGregg Barrett
This presentation provides a brief insight into a Big Data platform using the Hadoop ecosystem.
To this end the presentation will touch on:
-views of the Big Data ecosystem and it’s components
-an example of a Hadoop cluster
-considerations when selecting a Hadoop distribution
-some of the Hadoop distributions available
-a recommended Hadoop distribution
Rescue your Big Data from Downtime with HP Operations Bridge and Apache HadoopHortonworks
How can you simplify the management and monitoring of your Hadoop environment? Ensure IT can focus on the right business priorities supported by Hadoop? Take a look at this presentation and learn how you can simplify the management and monitoring of your Hadoop environment, and ensure IT can focus on the right business priorities supported by Hadoop.
Learn how when an organizations combine HP and Vertica Analytics Platform and Hortonworks, they can quickly explore and analyze broad variety of data types to transform to actionable information that allows them to better understand how their customers and site visitors interact with their business, offline and online.
Stinger.Next by Alan Gates of HortonworksData Con LA
ver the last 13 months the Apache Hive community, which included 145 developers and 44 companies working together through the Stinger initiative, delivered 390,000 lines of code and 1600 resolved JIRA tickets. This is only the beginning. The Hive community has already started the next phase of extending the Speed, Scale, and SQL compliance in Hive. As Hadoop 2.0 with YARN evolves to enable a dizzying array of powerful engines that allow us to interact with ever growing data in new ways, well known tools such as SQL need to scale with it. This session will provide a technical illustration of the challenges facing SQL on Hadoop today and what the road ahead looks like as the user community drives more innovation. Stinger.next is the next multi-phase initiative to evolve Hive as the de facto SQL engine for Hadoop designed to deliver Speed, Scale and better SQL.
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
Many enterprises are turning to Apache Hadoop to enable Big Data Analytics and reduce the costs of traditional data warehousing. Yet, it is hard to succeed when 80% of the time is spent on moving data and only 20% on using it. It’s time to swap the 80/20! The Big Data experts at Attunity and Hortonworks have a solution for accelerating data movement into and out of Hadoop that enables faster time-to-value for Big Data projects and a more complete and trusted view of your business. Join us to learn how this solution can work for you.
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
( EMC World 2012 ) :Apache Hadoop is now enterprise ready. This session reviews the features/roadmap of Hadoop. We will review some of the key capabilities of GPHD 1.x and our plans for 2012.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
Predictive Analytics and Machine Learning…with SAS and Apache HadoopHortonworks
In this interactive webinar, we'll walk through use cases on how you can use advanced analytics like SAS Visual Statistics and In-Memory Statistic with Hortonworks’ data platform (HDP) to reveal insights in your big data and redefine how your organization solves complex problems.
Create a Smarter Data Lake with HP Haven and Apache HadoopHortonworks
An organization’s information is spread across multiple repositories, on-premise and in the cloud, with limited ability to correlate information and derive insights. The Smart Content Hub solution from HP and Hortonworks enables a shared content infrastructure that transparently synchronizes information with existing systems and offers an open standards-based platform for deep analysis and data monetization.
- Leverage 100% of your data: Text, images, audio, video, and many more data types can be automatically consumed and enriched using HP Haven (powered by HP IDOL and HP Vertica), making it possible to integrate this valuable content and insights into various line of business applications.
- Democratize and enable multi-dimensional content analysis: - Empower your analysts, business users, and data scientists to search and analyze Hadoop data with ease, using the 100% open source Hortonworks Data Platform.
- Extend the enterprise data warehouse: Synchronize and manage content from content management systems, and crack open the files in whatever format they happen to be in.
- Dramatically reduce complexity with enterprise-ready SQL engine: Tap into the richest analytics that support JOINs, complex data types, and other capabilities only available with HP Vertica SQL on the Hortonworks Data Platform.
Speakers:
- Ajay Singh, Director, Technical Channels, Hortonworks
- Will Gardella, Product Management, HP Big Data
Mr. Slim Baltagi is a Systems Architect at Hortonworks, with over 4 years of Hadoop experience working on 9 Big Data projects: Advanced Customer Analytics, Supply Chain Analytics, Medical Coverage Discovery, Payment Plan Recommender, Research Driven Call List for Sales, Prime Reporting Platform, Customer Hub, Telematics, Historical Data Platform; with Fortune 100 clients and global companies from Financial Services, Insurance, Healthcare and Retail.
Mr. Slim Baltagi has worked in various architecture, design, development and consulting roles at.
Accenture, CME Group, TransUnion, Syntel, Allstate, TransAmerica, Credit Suisse, Chicago Board Options Exchange, Federal Reserve Bank of Chicago, CNA, Sears, USG, ACNielsen, Deutshe Bahn.
Mr. Baltagi has also over 14 years of IT experience with an emphasis on full life cycle development of Enterprise Web applications using Java and Open-Source software. He holds a master’s degree in mathematics and is an ABD in computer science from Université Laval, Québec, Canada.
Languages: Java, Python, JRuby, JEE , PHP, SQL, HTML, XML, XSLT, XQuery, JavaScript, UML, JSON
Databases: Oracle, MS SQL Server, MYSQL, PostreSQL
Software: Eclipse, IBM RAD, JUnit, JMeter, YourKit, PVCS, CVS, UltraEdit, Toad, ClearCase, Maven, iText, Visio, Japser Reports, Alfresco, Yslow, Terracotta, Toad, SoapUI, Dozer, Sonar, Git
Frameworks: Spring, Struts, AppFuse, SiteMesh, Tiles, Hibernate, Axis, Selenium RC, DWR Ajax , Xstream
Distributed Computing/Big Data: Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, HBase, R, RHadoop, Cloudera CDH4, MapR M7, Hortonworks HDP 2.1
Explores the notion of "Hadoop as a Data Refinery" within an organisation, be it one with an existing Business Intelligence system or none - looks at 'agile data' as a a benefit of using Hadoop as the store for historical, unstructured and very-large-scale datasets.
The final slides look at the challenge of an organisation becoming "data driven"
Hadoop as Data Refinery - Steve LoughranJAX London
Apache Hadoop is often described as a "Big Data Platform" but what does that mean? One way to better understand Hadoop is to talk about how Hadoop is used. This talk discusses using Hadoop as a "Data Refinery", which is a common use case. The concept is very much like a traditional oil refinery except with data, pulling in large quantities of "crude data" over pipelines, refining some into useful business intelligence; refining other pieces into slightly less crude data that stays in the cluster until needed later. This metaphor proves useful when considering how Hadoop could be adopted in an organisation that already has data warehousing and business intelligence systems -and when contemplating how to hook up a Hadoop cluster to the sources of data inside and outside that organisation. A key point to remember is that storing data in Hadoop is not a means to an end any more than storing data in a database is: it is extracting information from that data. Using Hadoop as a front end "data refinery" means that it can integrate with existing Business Intelligence systems, while providing the platform for new applications.
Enterprise Apache Hadoop: State of the UnionHortonworks
So what's in store for 2014? This deck was from Shaun Connolly's (VP of Strategy, Hortonworks) State of the Union webinar.
In this deck, you'll find:
- Reflection on Enterprise Hadoop Market in 2013
- The latest releases and innovations within the open source community
- Highlights of what's in store for Apache Hadoop and Big Data in 2014
Supporting Financial Services with a More Flexible Approach to Big DataHortonworks
Financial services companies can reap tremendous benefits from 'Big Data' and they have moved quickly to deploy it. But these companies also place heavy demands on 'Big Data' infrastructure for flexibility, reliability and performance. In this webinar, Hortonworks joins WANDisco to look at three examples of using 'Big Data' to get a more comprehensive view of customer behavior and activity in the banking and insurance industries. Then we'll pull out the common threads from these examples, and see how a flexible next-generation Hadoop architecture lets you get a step up on improving your business performance. Join us to learn:
How to leverage data from across an entire global enterprise
How to analyze a wide variety of structured and unstructured data to get quick, meaningful answers to critical questions
What industry leaders have put in place
Transform Your Business with Big Data and Hortonworks Pactera_US
Customer insight and marketplace predictions are a few of the profitable benefits found in big data technology. Leading companies are using the advanced analytics solution to find new revenue streams, increase customer satisfaction and optimize the supply chain.
Part of the core Hadoop project, YARN is the architectural center of Hadoop that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform, unlocking an entirely new approach to analytics. It is the foundation of the new generation of Hadoop and is enabling organizations everywhere to realize a Modern Data Architecture.
M. Florence Dayana - Hadoop Foundation for Analytics.pptxDr.Florence Dayana
Hadoop Foundation for Analytics
History of Hadoop
Features of Hadoop
Key Advantages of Hadoop
Why Hadoop
Versions of Hadoop
Eco Projects
Essential of Hadoop ecosystem
RDBMS versus Hadoop
Key Aspects of Hadoop
Components of Hadoop
Are you confused by Big Data? Get in touch with this new "black gold" and familiarize yourself with undiscovered insights through our complimentary introductory lesson on Big Data and Hadoop!
Hortonworks and Platfora in Financial Services - WebinarHortonworks
Big Data Analytics is transforming how banks and financial institutions unlock insights, make more meaningful decisions, and manage risk. Join this webinar to see how you can gain a clear understanding of the customer journey by leveraging Platfora to interactively analyze the mass of raw data that is stored in your Hortonworks Data Platform. Our experts will highlight use cases, including customer analytics and security analytics.
Speakers: Mark Lochbihler, Partner Solutions Engineer at Hortonworks, and Bob Welshmer, Technical Director at Platfora
47. Example: predicting CTR (search ads)
Rank = bid * CTR
Predict CTR for each ad to
determine placement, based on:
- Historical CTR
- Keyword match
- Etc…
Approach: supervised learning
49. MapReduce
• MapReduce is a distributed computing programming model
• It works like a Unix pipeline:
– cat input | grep | sort | uniq -c > output
– Input | Map | Shuffle & Sort | Reduce |
Output
• Strengths:
– Easy to use! Developer just writes a couple of
functions
– Moves compute to data
• Schedules work on HDFS node with data if possible
– Scans through data, reducing seeks
– Automatic reliability and re-execution on failure
49
49
I want to thank Chris for inviting me here today.Chris and team have done a number of projects with Hadoop.They are a great resource for Big Data projects.Chris is an Apache Board member and was a contributor to Hadoop even before we spun it out of the Nutch project.
As the volume of data has exploded, we increasingly see organizations acknowledge that not all data belongs in a traditional database. The drivers are both cost (as volumes grow, database licensing costs can become prohibitive) and technology (databases are not optimized for very large datasets).Instead, we increasingly see Hadoop – and HDP in particular – being introduced as a complement to the traditional approaches. It is not replacing the database but rather is a complement: and as such, must integrate easily with existing tools and approaches. This means it must interoperate with:Existing applications – such as Tableau, SAS, Business Objects, etc,Existing databases and data warehouses for loading data to / from the data warehouseDevelopment tools used for building custom applicationsOperational tools for managing and monitoring
Hadoop started to enhance SearchScience clusters launched in 2006 as early proof of conceptScience results drive new applications -> becomes core Hadoop business
At Hortonworks today, our focus is very clear: we Develop, Distribute and Support a 100% open source distribution of Enterprise Apache Hadoop.We employ the core architects, builders and operators of Apache Hadoop and drive the innovation in the open source community.We distribute the only 100% open source Enterprise Hadoop distribution: the Hortonworks Data PlatformGiven our operational expertise of running some of the largest Hadoop infrastructure in the world at Yahoo, our team is uniquely positioned to support youOur approach is also uniquely endorsed by some of the biggest vendors in the IT marketYahoo is both and investor and a customer, and most importantly, a development partner. We partner to develop Hadoop, and no distribution of HDP is released without first being tested on Yahoo’s infrastructure and using the same regression suite that they have used for years as they grew to have the largest production cluster in the worldMicrosoft has partnered with Hortonworks to include HDP in both their off-premise offering on Azure but also their on-premise offering under the product name HDInsight. This also includes integration with both Visual Studio for application development but also with System Center for operational management of the infrastructureTeradata includes HDP in their products in order to provide the broadest possible range of options for their customers
Tell inception story, plan to differentiate Yahoo, recruit talent, insure that Y! was not built on legacy private systemFrom YST
I want to thank Chris for inviting me here today.Chris and team have done a number of projects with Hadoop.They are a great resource for Big Data projects.Chris is an Apache Board member and was a contributor to Hadoop even before we spun it out of the Nutch project.
Archival use case at big bank:10K files a day == 400GBNeed to store all in EBCDIC format for complianceNeed to also convert to Hadoop for analyticsCompute a checksum for every record and keep a tally of which primary keys changed each dayAlso, bring together financial, customer, and weblogs for new insightsShare with Palantir, Aster Data, Vertica, Teradata, and more…Step One: Create tables or partitionsIn Step one of the dataflow the mainframe or another orchestration and control program notifies HCatalog of its intention to create a table or add a partition if the table exists. This would use standard SQL data definition language (DDL) such as CREATE TABLE and DESCRIBE TABLE (see http://incubator.apache.org/hcatalog/docs/r0.4.0/cli.html#HCatalog+DDL). Multiple tables need to be created though. Some tables are job-specific temporary tables while other tables need to be more permanent. Raw format storage data can be stored in an HCat table, partitioned by some date field (month or year, for example). The staged record data will most certainly be stored in HCatalog partitioned by month (see http://incubator.apache.org/hcatalog/docs/r0.4.0/dynpartition.html). Then any missing month in the table can be easily detected and generated from the raw format storage on the fly. In essence, HCatalog allows creation of tables which up-levels this architectural challenge from one of managing a bunch of manually created files and a loose naming convention to a strong yet abstract table structure much like a mature database solution would have.Step Two: Parallel IngestBefore or after tables are defined in the system, we can start adding data to the system in parallel using WebHDFS or DistCP. In the Teradata-Hortonworks Data Platform these architectural components work seamlessly with the standard HDFS namenode to notify DFS clients of all the datanodes to which to write data. For example, a file made up of 10,000 64 megabyte blocks could be transferred to a 100-node HDFS cluster using all 100 nodes at once. By asking WebHDFS for the write locations for each block, a multi-threaded or chunking client application could write each 64MB block in parallel, 100 blocks or more at a time, effectively dividing the 10,000-block into 100 waves of copying. 100 copy waves would complete 100 times faster than 10,000 one-by-one block copies. Parallel ingest with HCatalog, WebHDFS and/or DistCP will lead to massive speed gains.Critically, the system can copy chunked data directly into partitions in pre-defined tables in HCatalog. This means that each month, staged record data can join the staging tables without dropping previous months and staged data can be partitioned by month while each month itself is loaded using as many parallel ingest servers as solution architecture desires to balance cost with performance.Step Three: Notify on UploadNext, the Parallel ingest system needs to notify the HCatalog engine the files have been uploaded and, simultaneously, any end user transformation or analytics workload waiting for the partition need to be notified that the file is ready to support queries. By “ready” we mean that the partition is whole and is completely copied into HDFS. HCatalog has built in blocking and non-blocking notification APIs that use standard message buses to notify any interested parties that workload—be it MapReduce or HDFS copy work—is complete and valid (see: http://incubator.apache.org/hcatalog/docs/r0.4.0/notification.html). The way this system works is any job created through HCatalog is acknowledged with an output location. The messaging system later replies that a job is complete and since, when the job was submitted, the eventual output location was returned, the calling application can immediately return to the target output file and find its needed data. In this next-gen ETL use case, we will be using this notification system to immediately fire a Hive job to begin transformation whenever a partition is added to the raw or staged data tables. This will make the construction of systems that depend on these transformations easier in that these systems needn’t poll for data nor do those dependent systems need to hard-code file locations for sources and sinks of data moving through the dataflow.Step Four: Fire Off UDFsSince HCatalog can notify interested parties in the completion of file I/O tasks, and since Hcatalog stores file data underneath abstracted table and partition names and locations, invoking the core UDFs that transform mainframe’s data into standard SQL data types can be programmatic. In other words, when a partition is created and the data backing it fully loaded into HDFS, a persistent Hive client can wake up, being notified of the new data and grab that data to load into Teradata. Step Five: Invoke Parallel Transport (Q1, 2013)Coming in the first quarter of 2013 or soon thereafter, Teradata and Hortonworks Data Platform will communicate using Teradata’s parallel transport mechanism. This will provide the same performance benefits as parallel ingest but for the final step in the dataflow. For now, systems integrators and/or Teradata and Hortonworks team members can implement a few DFS clients to load chunks or segments of the table data into Teradata in parallel.
Example: hi tech surveys, customer sat and product satSurveys have multiple-choice and freeformInput and analyze the plain-text sectionsJoin cross-channel support requests and device telemetry back to customerAnother example: wireless carrier and “golden path”
Example: retail custom homepageClusters of related productsSet up models in Hbase that influence when user behaviors trigger recommendationsOR inform users when they enter of custom recommendations
Community developed frameworksMachine learning / Analytics (MPI, GraphLab, Giraph, Hama, Spark, …)Services inside Hadoop (memcache, HBase, Storm…)Low latency computing (CEP or stream processing)
Community developed frameworksMachine learning / Analytics (MPI, GraphLab, Giraph, Hama, Spark, …)Services inside Hadoop (memcache, HBase, Storm…)Low latency computing (CEP or stream processing)
Hortonworks SandboxHortonworks accelerates Hadoop skills development with an easy-to-use, flexible and extensible platform to learn, evaluate and use Apache HadoopWhat is it: virtualized single-node implementation of the enterprise-ready Hortonworks Data PlatformProvides demos, videos and step-by-step hands-on tutorialsPre-built partner integrations and access to datasetsWhat it does: Dramatically accelerates the process of learning Apache HadoopSee It -- demos and videos to illustrate use casesLearn It -- multi level step by step tutorials Do It -- hands on exercises for faster skills developmentHow it helps: Accelerate and validates the use of Hadoop within your unique data architectureUse your data to explore and investigate your use casesZERO to big data in 15 minutes
But beyond Core Hadoop, Hortonworkers are also deeply involved in the ancillary projects that are necessary for more general usage.As you can see, in both code count as well as committers, we contribute more than any others to Core Hadoop. And for the other key projects such as Pig, Hive, Hcatalog, Ambari we are doing the same.This community leadership across both core Hadoop and also the related open source projects is crucial in enabling us to play the critical role in turning Hadoop into Enterprise Hadoop.
So how does this get brought together into our distribution? It is really pretty straightforward, but also very unique:We start with this group of open source projects that I described and that we are continually driving in the OSS community. [CLICK] We then package the appropriate versions of those open source projects, integrate and test them using a full suite, including all the IP for regression testing contributed by Yahoo, and [CLICK] contribute back all of the bug fixes to the open source tree. From there, we package and certify a distribution in the from of the Hortonworks Data Platform (HDP) that includes both Hadoop Core as well as the related projects required by the Enterprise user, and provide to our customers.Through this application of Enterprise Software development process to the open source projects, the result is a 100% open source distribution that has been packaged, tested and certified by Hortonworks. It is also 100% in sync with the open source trees.
As the volume of data has exploded, we increasingly see organizations acknowledge that not all data belongs in a traditional database. The drivers are both cost (as volumes grow, database licensing costs can become prohibitive) and technology (databases are not optimized for very large datasets).Instead, we increasingly see Hadoop – and HDP in particular – being introduced as a complement to the traditional approaches. It is not replacing the database but rather is a complement: and as such, must integrate easily with existing tools and approaches. This means it must interoperate with:Existing applications – such as Tableau, SAS, Business Objects, etc,Existing databases and data warehouses for loading data to / from the data warehouseDevelopment tools used for building custom applicationsOperational tools for managing and monitoring