This document provides information about using Scalding on Tez. It begins with prerequisites for using Scalding on Tez, including having a YARN cluster, Cascading 3.0, and the TEZ runtime library in HDFS. It then discusses setting memory and Java heap configuration flags for Tez jobs run through Scalding. The document provides a mini-howto for using Scalding on Tez in two steps - configuring the build.sbt and assembly.sbt files and setting some job flags. It discusses challenges encountered in practice and provides tips and an example Scalding on Tez application.
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.
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.
Hadoop Operations, Innovations and Enterprise Readiness with Hortonworks Data...Hortonworks
1. Hortonworks Data Platform 1.2 focuses on continued innovation with Apache Ambari and enhanced security and performance for Hive and HCatalog.
2. Key features include root cause analysis, usage heat maps, and improved ecosystem integration in Ambari, as well as enhanced security models and concurrency improvements.
3. Hortonworks ensures tight alignment with open source Apache projects by certifying the latest stable components and contributing leadership and code back to projects.
This document discusses the author's 10 year journey with Hadoop, from 2006 to 2016. It describes the evolution of key Hadoop technologies like HDFS, MapReduce, YARN and the addition of engines for SQL, NoSQL, streaming and in-memory processing. The document also addresses trends around growth of data from devices, users and the internet of things. It presents a vision of the future where Hadoop (YARN.next) will assemble and securely operate a flexible menu of data access applications and engines.
Delivering Apache Hadoop for the Modern Data Architecture Hortonworks
Join Hortonworks and Cisco as we discuss trends and drivers for a modern data architecture. Our experts will walk you through some key design considerations when deploying a Hadoop cluster in production. We'll also share practical best practices around Cisco-based big data architectures and Hortonworks Data Platform to get you started on building your modern data architecture.
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
Data Discovery, Visualization, and Apache HadoopHortonworks
In this webinar, we will discuss how Apache Hadoop works with your current infrastructure and how you can use data discovery and visualization tools to gain deeper insights from new data types stored in Hadoop and your existing data center investments.
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.
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.
Hadoop Operations, Innovations and Enterprise Readiness with Hortonworks Data...Hortonworks
1. Hortonworks Data Platform 1.2 focuses on continued innovation with Apache Ambari and enhanced security and performance for Hive and HCatalog.
2. Key features include root cause analysis, usage heat maps, and improved ecosystem integration in Ambari, as well as enhanced security models and concurrency improvements.
3. Hortonworks ensures tight alignment with open source Apache projects by certifying the latest stable components and contributing leadership and code back to projects.
This document discusses the author's 10 year journey with Hadoop, from 2006 to 2016. It describes the evolution of key Hadoop technologies like HDFS, MapReduce, YARN and the addition of engines for SQL, NoSQL, streaming and in-memory processing. The document also addresses trends around growth of data from devices, users and the internet of things. It presents a vision of the future where Hadoop (YARN.next) will assemble and securely operate a flexible menu of data access applications and engines.
Delivering Apache Hadoop for the Modern Data Architecture Hortonworks
Join Hortonworks and Cisco as we discuss trends and drivers for a modern data architecture. Our experts will walk you through some key design considerations when deploying a Hadoop cluster in production. We'll also share practical best practices around Cisco-based big data architectures and Hortonworks Data Platform to get you started on building your modern data architecture.
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
Data Discovery, Visualization, and Apache HadoopHortonworks
In this webinar, we will discuss how Apache Hadoop works with your current infrastructure and how you can use data discovery and visualization tools to gain deeper insights from new data types stored in Hadoop and your existing data center investments.
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.
Boost Performance with Scala – Learn From Those Who’ve Done It! Cécile Poyet
Scalding is a scala DSL for Cascading. Run on Hadoop, it’s a concise, functional, and very efficient way to build big data applications. One significant benefit of Scalding is that it allows easy porting of Scalding apps from MapReduce to newer, faster execution fabrics.
In this webinar, Cyrille Chépélov, of Transparency Rights Management, will share how his organization boosted the performance of their Scalding apps by over 50% by moving away from MapReduce to Cascading 3.0 on Apache Tez. Dhruv Kumar, Hortonworks Partner Solution Engineer, will then explain how you can interact with data on HDP using Scala and leverage Scala as a programming language to develop Big Data applications.
Discover Enterprise Security Features in Hortonworks Data Platform 2.1: Apach...Hortonworks
This document provides an overview of new security features in Hortonworks Data Platform (HDP) 2.1, including the Knox gateway for securing Hadoop REST APIs, extended access control lists (ACLs) in HDFS, and Apache Hive authorization using ATZ-NG. Knox provides a single access point and central security for REST APIs. Extended HDFS ACLs allow assigning different permissions to users and groups. Hive ATZ-NG implements SQL-style authorization with grants and revokes, integrating policies with the table lifecycle.
In 2012, we released Hortonworks Data Platform powered by Apache Hadoop and established partnerships with major enterprise software vendors including Microsoft and Teradata that are making enterprise ready Hadoop easier and faster to consume. As we start 2013, we invite you to join us for this live webinar where Shaun Connolly, VP of Strategy at Hortonworks, will cover the highlights of 2012 and the road ahead in 2013 for Hortonworks and Apache Hadoop.
Powering Fast Data and the Hadoop Ecosystem with VoltDB and HortonworksHortonworks
Developers increasingly are building dynamic, interactive real-time applications on fast streaming data to extract maximum value from data in the moment. To do so requires a data pipeline, the ability to make transactional decisions against state, and an export functionality that pushes data at high speeds to long-term Hadoop analytics stores like Hortonworks Data Platform (HDP). This enables data to arrive in your analytic store sooner, and allows these analytics to be leveraged with radically lower latency.
But successfully writing fast data applications that manage, process, and export streams of data generated from mobile, smart devices, sensors and social interactions is a big challenge.
Join Hortonworks and VoltDB, an in-memory scale-out relational database that simplifies fast data application development, to learn how you can ingest large volumes of fast-moving, streaming data and process it in real time. We will also cover how developing fast data applications is simplified, faster - and delivers more value when built on a fast in-memory, scale-out SQL database.
Don't Let Security Be The 'Elephant in the Room'Hortonworks
Don't let security be the "elephant in the room" for enterprise big data. As big data now includes sensitive data from various sources, there are hidden risks to simply adopting big data technologies without also implementing proper data protection. While traditional IT security approaches provide some coverage, they also have gaps and do not fully address protecting data across its lifecycle and wherever it may travel. A data-centric security approach that encrypts data at capture can lock down data and keep it protected as it is stored, processed, and shared across systems.
Combine SAS High-Performance Capabilities with Hadoop YARNHortonworks
The document discusses combining SAS capabilities with Hadoop YARN. It provides an introduction to YARN and how it allows SAS workloads to run on Hadoop clusters alongside other workloads. The document also discusses resource settings for SAS workloads on YARN and upcoming features for YARN like delegated containers and Kubernetes integration.
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...Hortonworks
The document discusses a Big Data Meetup organized by C-BAG (Chennai Big Data Analytic Group) on October 29, 2014 in Chennai. It provides details about two speakers, Dhruv Kumar from Concurrent Inc. and Vinay Shukla from Hortonworks, who will discuss reducing development time for production-grade Hadoop applications and Hortonworks' Hadoop platform respectively. The remainder of the document consists of presentation slides that cover topics including the modern data architecture with Hadoop, enterprise goals for data architecture, unlocking applications from new data types, and case studies.
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.
Discover HDP 2.2: Even Faster SQL Queries with Apache Hive and Stinger.nextHortonworks
The document discusses new features in Apache Hive 0.14 that improve SQL query performance. It introduces a cost-based optimizer that can optimize join orders, enabling faster query times. An example TPC-DS query is shown to demonstrate how the optimizer selects an efficient join order based on statistics about table and column sizes. Faster SQL queries are now possible in Hive through this query optimization capability.
Data Lake for the Cloud: Extending your Hadoop ImplementationHortonworks
As more applications are created using Apache Hadoop that derive value from the new types of data from sensors/machines, server logs, click-streams, and other sources, the enterprise "Data Lake" forms with Hadoop acting as a shared service. While these Data Lakes are important, a broader life-cycle needs to be considered that spans development, test, production, and archival and that is deployed across a hybrid cloud architecture.
If you have already deployed Hadoop on-premise, this session will also provide an overview of the key scenarios and benefits of joining your on-premise Hadoop implementation with the cloud, by doing backup/archive, dev/test or bursting. Learn how you can get the benefits of an on-premise Hadoop that can seamlessly scale with the power of the cloud.
This document contains a presentation about using open source software and commodity hardware to process big data in a cost effective manner. It discusses how Apache Hadoop can be used to collect, store, process and analyze large amounts of data without expensive proprietary software or hardware. The presentation provides examples of how Hadoop is being used by various companies and explores different approaches for refining, exploring and enriching data with Hadoop.
This webinar series covers Apache Kafka and Apache Storm for streaming data processing. Also, it discusses new streaming innovations for Kafka and Storm included in HDP 2.2
Introduction to Hortonworks Data PlatformHortonworks
This document introduces the Hortonworks Data Platform. It summarizes the key features of the platform, including its ability to simplify deployment, monitor and manage large clusters, integrate with any data source, and provide metadata services. The document demonstrates the Hortonworks Management Center and features for high availability, data integration, and metadata services. It concludes by discussing training, support, and certification services available from Hortonworks.
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.
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.
Your Self-Driving Car - How Did it Get So Smart?Hortonworks
This document summarizes a presentation given by Michael Ger, Dr. Andreas Pawlik, and Dr. Seunghan Han of NorCom and Hortonworks about their DaSense data science platform. DaSense is designed to help researchers developing autonomous vehicle systems by allowing them to more efficiently run simulations and test algorithms on large datasets using distributed high performance computing resources. It aims to accelerate the development process by enabling experiments that previously took days to be completed within hours or minutes by leveraging large compute clusters. DaSense provides tools for building end-to-end data science pipelines for tasks like data filtering, model training, evaluation and analysis.
Enabling the Real Time Analytical EnterpriseHortonworks
This document discusses enabling real-time analytics in the enterprise. It begins with an overview of the challenges of real-time analytics due to non-integrated systems, varied data types and volumes, and data management complexity. A case study on real-time quality analytics in automotive is presented, highlighting the need to analyze varied data sources quickly to address issues. The Hortonworks/Attunity solution is then introduced using Attunity Replicate to integrate data from various sources in real-time into Hortonworks Data Platform for analysis. A brief demonstration of data streaming from a database into Kafka and then Hortonworks Data Platform is shown.
Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...Hortonworks
This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.
Enrich a 360-degree Customer View with Splunk and Apache HadoopHortonworks
What if your organization could obtain a 360 degree view of the customer across offline, online and social and mobile channels? Attend this webinar with Splunk and Hortonworks and see examples of how marketing, business and operations analysts can reach across disparate data sets in Hadoop to spot new opportunities for up-sell and cross-sell. We'll also cover examples of how to measure buyer sentiment and changes in buyer behavior. Along with best practices on how to use data in Hadoop with Splunk to assign customer influence scores that online, call-center, and retail branches can use to customize more compelling products and promotions.
This document outlines classroom expectations and procedures for a math class. It details rules regarding entering the classroom, organizing binders, homework expectations, discipline procedures, emergency drills, and dismissal from class. Students are expected to be on time, prepared, respectful, and follow the teacher's instructions.
The document discusses various concepts and best practices in marketing management including the importance of understanding customers, monitoring competitors, developing a clear marketing strategy and plan, focusing on brands and positioning, and ensuring the organization is structured to support effective marketing. It provides definitions of key marketing terms and lists sins that can be committed if a company is not properly focused on customers, organized for marketing, or strategic in its planning. The skills needed for modern marketers are also outlined.
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.
Boost Performance with Scala – Learn From Those Who’ve Done It! Cécile Poyet
Scalding is a scala DSL for Cascading. Run on Hadoop, it’s a concise, functional, and very efficient way to build big data applications. One significant benefit of Scalding is that it allows easy porting of Scalding apps from MapReduce to newer, faster execution fabrics.
In this webinar, Cyrille Chépélov, of Transparency Rights Management, will share how his organization boosted the performance of their Scalding apps by over 50% by moving away from MapReduce to Cascading 3.0 on Apache Tez. Dhruv Kumar, Hortonworks Partner Solution Engineer, will then explain how you can interact with data on HDP using Scala and leverage Scala as a programming language to develop Big Data applications.
Discover Enterprise Security Features in Hortonworks Data Platform 2.1: Apach...Hortonworks
This document provides an overview of new security features in Hortonworks Data Platform (HDP) 2.1, including the Knox gateway for securing Hadoop REST APIs, extended access control lists (ACLs) in HDFS, and Apache Hive authorization using ATZ-NG. Knox provides a single access point and central security for REST APIs. Extended HDFS ACLs allow assigning different permissions to users and groups. Hive ATZ-NG implements SQL-style authorization with grants and revokes, integrating policies with the table lifecycle.
In 2012, we released Hortonworks Data Platform powered by Apache Hadoop and established partnerships with major enterprise software vendors including Microsoft and Teradata that are making enterprise ready Hadoop easier and faster to consume. As we start 2013, we invite you to join us for this live webinar where Shaun Connolly, VP of Strategy at Hortonworks, will cover the highlights of 2012 and the road ahead in 2013 for Hortonworks and Apache Hadoop.
Powering Fast Data and the Hadoop Ecosystem with VoltDB and HortonworksHortonworks
Developers increasingly are building dynamic, interactive real-time applications on fast streaming data to extract maximum value from data in the moment. To do so requires a data pipeline, the ability to make transactional decisions against state, and an export functionality that pushes data at high speeds to long-term Hadoop analytics stores like Hortonworks Data Platform (HDP). This enables data to arrive in your analytic store sooner, and allows these analytics to be leveraged with radically lower latency.
But successfully writing fast data applications that manage, process, and export streams of data generated from mobile, smart devices, sensors and social interactions is a big challenge.
Join Hortonworks and VoltDB, an in-memory scale-out relational database that simplifies fast data application development, to learn how you can ingest large volumes of fast-moving, streaming data and process it in real time. We will also cover how developing fast data applications is simplified, faster - and delivers more value when built on a fast in-memory, scale-out SQL database.
Don't Let Security Be The 'Elephant in the Room'Hortonworks
Don't let security be the "elephant in the room" for enterprise big data. As big data now includes sensitive data from various sources, there are hidden risks to simply adopting big data technologies without also implementing proper data protection. While traditional IT security approaches provide some coverage, they also have gaps and do not fully address protecting data across its lifecycle and wherever it may travel. A data-centric security approach that encrypts data at capture can lock down data and keep it protected as it is stored, processed, and shared across systems.
Combine SAS High-Performance Capabilities with Hadoop YARNHortonworks
The document discusses combining SAS capabilities with Hadoop YARN. It provides an introduction to YARN and how it allows SAS workloads to run on Hadoop clusters alongside other workloads. The document also discusses resource settings for SAS workloads on YARN and upcoming features for YARN like delegated containers and Kubernetes integration.
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...Hortonworks
The document discusses a Big Data Meetup organized by C-BAG (Chennai Big Data Analytic Group) on October 29, 2014 in Chennai. It provides details about two speakers, Dhruv Kumar from Concurrent Inc. and Vinay Shukla from Hortonworks, who will discuss reducing development time for production-grade Hadoop applications and Hortonworks' Hadoop platform respectively. The remainder of the document consists of presentation slides that cover topics including the modern data architecture with Hadoop, enterprise goals for data architecture, unlocking applications from new data types, and case studies.
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.
Discover HDP 2.2: Even Faster SQL Queries with Apache Hive and Stinger.nextHortonworks
The document discusses new features in Apache Hive 0.14 that improve SQL query performance. It introduces a cost-based optimizer that can optimize join orders, enabling faster query times. An example TPC-DS query is shown to demonstrate how the optimizer selects an efficient join order based on statistics about table and column sizes. Faster SQL queries are now possible in Hive through this query optimization capability.
Data Lake for the Cloud: Extending your Hadoop ImplementationHortonworks
As more applications are created using Apache Hadoop that derive value from the new types of data from sensors/machines, server logs, click-streams, and other sources, the enterprise "Data Lake" forms with Hadoop acting as a shared service. While these Data Lakes are important, a broader life-cycle needs to be considered that spans development, test, production, and archival and that is deployed across a hybrid cloud architecture.
If you have already deployed Hadoop on-premise, this session will also provide an overview of the key scenarios and benefits of joining your on-premise Hadoop implementation with the cloud, by doing backup/archive, dev/test or bursting. Learn how you can get the benefits of an on-premise Hadoop that can seamlessly scale with the power of the cloud.
This document contains a presentation about using open source software and commodity hardware to process big data in a cost effective manner. It discusses how Apache Hadoop can be used to collect, store, process and analyze large amounts of data without expensive proprietary software or hardware. The presentation provides examples of how Hadoop is being used by various companies and explores different approaches for refining, exploring and enriching data with Hadoop.
This webinar series covers Apache Kafka and Apache Storm for streaming data processing. Also, it discusses new streaming innovations for Kafka and Storm included in HDP 2.2
Introduction to Hortonworks Data PlatformHortonworks
This document introduces the Hortonworks Data Platform. It summarizes the key features of the platform, including its ability to simplify deployment, monitor and manage large clusters, integrate with any data source, and provide metadata services. The document demonstrates the Hortonworks Management Center and features for high availability, data integration, and metadata services. It concludes by discussing training, support, and certification services available from Hortonworks.
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.
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.
Your Self-Driving Car - How Did it Get So Smart?Hortonworks
This document summarizes a presentation given by Michael Ger, Dr. Andreas Pawlik, and Dr. Seunghan Han of NorCom and Hortonworks about their DaSense data science platform. DaSense is designed to help researchers developing autonomous vehicle systems by allowing them to more efficiently run simulations and test algorithms on large datasets using distributed high performance computing resources. It aims to accelerate the development process by enabling experiments that previously took days to be completed within hours or minutes by leveraging large compute clusters. DaSense provides tools for building end-to-end data science pipelines for tasks like data filtering, model training, evaluation and analysis.
Enabling the Real Time Analytical EnterpriseHortonworks
This document discusses enabling real-time analytics in the enterprise. It begins with an overview of the challenges of real-time analytics due to non-integrated systems, varied data types and volumes, and data management complexity. A case study on real-time quality analytics in automotive is presented, highlighting the need to analyze varied data sources quickly to address issues. The Hortonworks/Attunity solution is then introduced using Attunity Replicate to integrate data from various sources in real-time into Hortonworks Data Platform for analysis. A brief demonstration of data streaming from a database into Kafka and then Hortonworks Data Platform is shown.
Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...Hortonworks
This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.
Enrich a 360-degree Customer View with Splunk and Apache HadoopHortonworks
What if your organization could obtain a 360 degree view of the customer across offline, online and social and mobile channels? Attend this webinar with Splunk and Hortonworks and see examples of how marketing, business and operations analysts can reach across disparate data sets in Hadoop to spot new opportunities for up-sell and cross-sell. We'll also cover examples of how to measure buyer sentiment and changes in buyer behavior. Along with best practices on how to use data in Hadoop with Splunk to assign customer influence scores that online, call-center, and retail branches can use to customize more compelling products and promotions.
This document outlines classroom expectations and procedures for a math class. It details rules regarding entering the classroom, organizing binders, homework expectations, discipline procedures, emergency drills, and dismissal from class. Students are expected to be on time, prepared, respectful, and follow the teacher's instructions.
The document discusses various concepts and best practices in marketing management including the importance of understanding customers, monitoring competitors, developing a clear marketing strategy and plan, focusing on brands and positioning, and ensuring the organization is structured to support effective marketing. It provides definitions of key marketing terms and lists sins that can be committed if a company is not properly focused on customers, organized for marketing, or strategic in its planning. The skills needed for modern marketers are also outlined.
- The document discusses New Zealand's Open Government Information and Data Programme, providing examples of apps and tools developed through open data initiatives like GovHack.
- Over 3,000 people participated in GovHack across Australia and New Zealand, developing over 100 projects using open government datasets to address real issues.
- Examples of apps developed include tools for emergency services, immigrants, tourists, and more to help reduce wait times, find communities and flatmates, and bring together emergency information.
Analisis pengaruh karakteristik perusahaan terhadap kualitas pengungkapan corporate governance pada laporan tahunan. Penelitian ini menguji pengaruh ukuran perusahaan, profitabilitas, tingkat persebaran modal, leverage, dan klasifikasi industri terhadap kualitas pengungkapan corporate governance pada 66 perusahaan di LQ-45 tahun 2009-2010.
This document outlines various projects completed by Hartsfield Creative, including presentations for Nike Running involving brand development and strategic retail design, a grassroots activation for Nike SB taking a mobile product trial concept to the streets, work on retail displays and in-store promotions for various clients, collateral design for branding campaigns, logo design and brand representation, websites and digital media, and packaging design. It emphasizes the importance of branding, marketing, and an integrated design approach across mediums for client campaigns.
The document provides information on resume samples, templates, and other career resources for insurance examiners. It lists resume formats like chronological, functional, and combination resumes. It also provides tips and examples for writing resumes for new graduates, executives, and professionals. Additionally, it includes links to interview preparation materials like common questions, thank you letters, dress codes, and more to assist with the hiring process for insurance examiner roles.
This document provides information about an upcoming conference on business improvement in universities to be held on June 17-18, 2015 in Melbourne. The conference will focus on building business process improvement frameworks, balancing speed of change with risk mitigation, and realigning resources through shared services and outsourcing. University vice chancellors and senior staff from Queensland, Sydney, RMIT, and Tasmania will speak. The conference includes keynotes, panels, workshops, and networking opportunities. Registration information and prices are provided.
The document provides resources for flatbed drivers seeking resume samples, cover letters, interview questions and answers, and career advice articles. It lists resume formats including chronological, functional, curriculum vitae, combination, targeted, professional, new graduate, and executive resumes. It also provides links to additional interview preparation materials and fields/levels relevant to flatbed driving careers.
The document describes plans to develop a mobile application to facilitate communication between a school, teachers, parents, and students. Currently, communication is done manually through student diaries or phone calls. The new application will allow real-time communication and interactions directly on mobile devices. It will include administrative, faculty, and student modules. The administrative module allows managing student and teacher records and sending notifications. The faculty module enables teachers to post assignments and attendance. Students can check results and schedules through the mobile app. The project aims to keep parents updated and allow the school to quickly notify everyone of meetings or alerts.
The document describes a cognitive model that incorporates emotions to model decision making, specifically applied to modeling the behavior of the Red Baron fighter pilot. It develops an emotional model based on theories of emotions and combines this with a cognitive model. Relationships between emotions and behaviors are represented using fuzzy cognitive maps to allow validation of the model using hypothetical combat scenarios. The model shows how specific emotions relate to behavioral actions and outcomes, providing a way to include emotions in decision making processes.
This short document promotes creating presentations using Haiku Deck, a tool for making slideshows. It encourages the reader to get started making their own Haiku Deck presentation and sharing it on SlideShare. In a single sentence, it pitches the idea of using Haiku Deck to easily design presentations.
The panel of experts discussed the lack of STEM skills in Michigan and the need for reform in education. They argued that while Michigan has traditionally been a leader in engineering and technology, the education system is not preparing students for the high-tech jobs of the future. The panel recommended increasing STEM exposure for K-12 students, promoting project-based and hands-on learning, better connecting classroom lessons to real-world problems, and ensuring underprivileged students have access to STEM education to remain competitive. Recent studies show there will be 274,000 open STEM jobs in Michigan by 2018, but the current education system is not supplying graduates with the needed skills and interest in these fields.
The document compares the properties and applications of various Accura stereolithography materials. It lists the materials in different classes based on their properties including polypropylene-like, tough/durable, ABS-like, clear, casting, high temperature, and composites. For each material, it provides information on their mechanical properties, color, and suitability for different applications. It also provides contact information for ordering the materials.
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...Hortonworks
Companies in every industry look for ways to explore new data types and large data sets that were previously too big to capture, store and process. They need to unlock insights from data such as clickstream, geo-location, sensor, server log, social, text and video data. However, becoming a data-first enterprise comes with many challenges.
Join this webinar organized by three leaders in their respective fields and learn from our experts how you can accelerate the implementation of a scalable, cost-efficient and robust Big Data solution. Cisco, Hortonworks and Red Hat will explore how new data sets can enrich existing analytic applications with new perspectives and insights and how they can help you drive the creation of innovative new apps that provide new value to your business.
Pivotal deep dive_on_pivotal_hd_world_class_hdfs_platformEMC
The document discusses Pivotal HD, a Hadoop distribution from Pivotal. It provides an overview of key features of Pivotal HD 2.0 including improved support for real-time analytics using Gemfire XD, enhanced machine learning and SQL capabilities, and integration with the Isilon storage platform. The presentation highlights how Pivotal HD can help customers build a "data lake" to store all of their data and gain insights to create new data-driven services and applications.
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
Azure Cafe Marketplace with Hortonworks March 31 2016Joan Novino
Azure Big Data: “Got Data? Go Modern and Monetize”.
In this session you will learn how to architected, developed, and build completely in the open, Hortonworks Data Platform (HDP) that provides an enterprise ready data platform to adopt a Modern Data Architecture.
This document discusses Hortonworks and its mission to enable modern data architectures through Apache Hadoop. It provides details on Hortonworks' commitment to open source development through Apache, engineering Hadoop for enterprise use, and integrating Hadoop with existing technologies. The document outlines Hortonworks' services and the Hortonworks Data Platform (HDP) for storage, processing, and management of data in Hadoop. It also discusses Hortonworks' contributions to Apache Hadoop and related projects as well as enhancing SQL capabilities and performance in Apache Hive.
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
Best Practices For Building and Operating A Managed Data Lake - StampedeCon 2016StampedeCon
The document discusses using a data lake approach with EMC Isilon storage to address various business use cases. It describes how the solution provides shared storage for multiple workloads through multi-protocol support, enables data protection and isolation of client data, and allows testing applications across Hadoop distributions through a common platform. Examples are given of how this approach supports an enterprise data hub, data warehouse offloading, data integration, and enrichment services.
Apache Hadoop and its role in Big Data architecture - Himanshu Barijaxconf
In today’s world of exponentially growing big data, enterprises are becoming increasingly more aware of the business utility and necessity of harnessing, storing and analyzing this information. Apache Hadoop has rapidly evolved to become a leading platform for managing and processing big data, with the vital management, monitoring, metadata and integration services required by organizations to glean maximum business value and intelligence from their burgeoning amounts of information on customers, web trends, products and competitive markets. In this session, Hortonworks' Himanshu Bari will discuss the opportunities for deriving business value from big data by looking at how organizations utilize Hadoop to store, transform and refine large volumes of this multi-structured information. Connolly will also discuss the evolution of Apache Hadoop and where it is headed, the component requirements of a Hadoop-powered platform, as well as solution architectures that allow for Hadoop integration with existing data discovery and data warehouse platforms. In addition, he will look at real-world use cases where Hadoop has helped to produce more business value, augment productivity or identify new and potentially lucrative opportunities.
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to info@insideanalysis.com, or tweet with #DBSurvival.
Hortonworks provides an open source Apache Hadoop distribution called Hortonworks Data Platform (HDP). Their mission is to enable modern data architectures through delivering enterprise Apache Hadoop. They have over 300 employees and are headquartered in Palo Alto, CA. Hortonworks focuses on driving innovation through the open source Apache community process, integrating Hadoop with existing technologies, and engineering Hadoop for enterprise reliability and support.
Enterprise Hadoop is Here to Stay: Plan Your Evolution StrategyInside Analysis
The Briefing Room with Neil Raden and Teradata
Live Webcast on August 19, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=1acd0b7ace309f765dc3196001d26a5e
Modern enterprises have been able to solve information management woes with the data warehouse, now a staple across the IT landscape that has evolved to a high level of sophistication and maturity with thousands of global implementations. Today’s modern enterprise has a similar challenge; big data and the fast evolution of the Hadoop ecosystem create plenty of new opportunities but also a significant number of operational pains as new solutions emerge.
Register for this episode of The Briefing Room to hear veteran Analyst Neil Raden as he explores the details and nature of Hadoop’s evolution. He’ll be briefed by Cesar Rojas of Teradata, who will share how Teradata solves some of the Hadoop operational challenges. He will also explain how the integration between Hadoop and the data warehouse can help organizations develop a more responsive and robust data management environment.
Visit InsideAnlaysis.com for more information.
Pivotal introduces its new Pivotal HD platform for big data analytics. Pivotal HD integrates Hadoop, HBase, Pig, Hive and other big data tools into an enterprise-grade distribution. It also includes tools like Command Center for job and cluster monitoring and HAWQ for SQL queries on Hadoop. Pivotal positions Pivotal HD as addressing pain points with Hadoop like usability, manageability and performance in order to make big data analytics mission-critical for enterprises.
Bridging the Big Data Gap in the Software-Driven WorldCA Technologies
Implementing and managing a Big Data environment effectively requires essential efficiencies such as automation, performance monitoring and flexible infrastructure management. Discover new innovations that enable you to manage entire Big Data environments with unparalleled ease of use and clear enterprise visibility across a variety of data repositories.
To learn more about Mainframe solutions from CA Technologies, visit: http://bit.ly/1wbiPkl
Real-Time Processing in Hadoop for IoT Use Cases - Phoenix HUGskumpf
The document discusses real-time processing in Hadoop using the Hortonworks Data Platform (HDP). It provides an overview of using HDP for real-time streaming analytics in a logistics scenario. Example applications and architectures are presented, including using Kafka for ingesting sensor data, Storm for stream processing, and HBase for real-time querying. Demos will also illustrate integrating predictive analytics into streaming scenarios.
Vmware Serengeti - Based on Infochimps IronfanJim Kaskade
This document discusses virtualizing Hadoop for the enterprise. It begins with discussing trends driving changes in enterprise IT like cloud, mobile apps, and big data. It then discusses how Hadoop can address big, fast, and flexible data needs. The rest of the document discusses how virtualizing Hadoop through solutions like Project Serengeti can provide enterprises with elasticity, high availability, and operational simplicity for their Hadoop implementations. It also discusses how virtualization allows enterprises to integrate Hadoop with other workloads and data platforms.
Hortonworks & Bilot Data Driven Transformations with HadoopMats Johansson
- Traditional systems are under pressure due to their inability to manage new data sources and costly scaling. A modern data architecture using Apache Hadoop emerges to provide a centralized platform for all enterprise data and applications.
- Hortonworks Data Platform is powered by Apache Hadoop and provides a flexible, scalable platform for storing and processing all data types from any source and supports a variety of applications. It offers governance, security, and operations controls for enterprise data management.
Hadoop and the Future of SQL: Using BI Tools with Big DataSenturus
Hadoop is changing how businesses operate, learn about this emerging technology stack. View the webinar video recording and download this deck: http://www.senturus.com/resource-video/hadoop-future-sql/?rId=3410.
Learn the role SQL queries play for big data, and how SQL-on-Hadoop technologies enable organizations to leverage their existing SQL skills and investments in business intelligence (BI) tools to dramatically improve: 1) Recommendation engines for online retail, 2) Transactional fraud prevention for financial services, 3) Customized advertising and 4) Predictive failure analytics for manufacturing.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Hortonworks provides an overview of their Tez framework for improving Hadoop query processing. Tez aims to accelerate queries by expressing them as dataflow graphs that can be optimized, rather than relying solely on MapReduce. It also aims to empower users by allowing flexible definition of data pipelines and composition of inputs, processors, and outputs. Early results show a 100x speedup on benchmark queries compared to traditional MapReduce.
Similar to Boost Performance with Scala – Learn From Those Who’ve Done It! (20)
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
WWDC 2024 Keynote Review: For CocoaCoders AustinPatrick Weigel
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
61. Next Steps…
Download the Hortonworks Sandbox
Learn Hadoop
Build Your Analytic App
Try Hadoop 2
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http://hortonworks.com/partner/concurrent
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http://www.transparencyrights.com/
Contact us: events@hortonworks.com
Hortonworks has a singular focus - enabling Apache Hadoop as an enterprise data platform for any app and any data type
We were founded in 2011 by 24 developers from Yahoo where Hadoop was conceived to address data challenges at internet scale. What we now know of as Hadoop really started in 2005, when a team at Yahoo was directed to build out a large-scale data storage and processing technology that would allow them to improve their most critical application, Search.
Their challenge was essentially two-fold. First they needed to capture and archive the contents of the internet, and then process the data so that users could search through it effectively an efficiently. Clearly traditional approaches were both technically (due to the size of the data) and commercially (due to the cost) impractical. The result was the Apache Hadoop project that delivered large scale storage (HDFS) and processing (MapReduce).
Today we are over 600 employees and have partnered with over 900 companies who are the leaders in the data center
We have also been very fortunate to achieve very significant customer adoption with over 230 customers as of Q3 2014, spanning nearly every vertical.
Hortonworks was founded the sole intent to make Hadoop an enterprise data platform. With YARN as its foundation, HDP delivers a centralized architecture with true multi-tenancy for data-processing and shared services for Security, Governance and Operations to satisfy enterprise requirements, all deeply integrated and certified with leading datacenter technologies.
We are uniquely focused on this transformation of Hadoop and doing our work completely in open source. This is all predicated on our leadership in the community, which enables not only to best support users of but also provides uniquely present customer requirements within this open, thriving community.
Before we dive into Hadoop and its role within the modern data architecture, let’s set the context for why Hadoop has become important.
Existing approaches for data management have become both technically and commercially impractical.
Technically - these systems were never designed to store or process vast quantities of data
Commercially – the licensing structures with the traditonal approach are no longer feasible.
These two challenges combined with rate at which data is being produce predicated a need for a new approach to data systems. If we fast-forward another 3 to 5 years, more than half of the data under management within the enterprise will be from these new data sources.
Enter Hadoop.
Faced with this challenge the team at yahoo conceived and created apache hadoop to address the challenge. They then were convinced that contribution of this platform into an open community would speed innovation. They open sourced the technology and did so within the governance of the Apache Software Foundation. (ASF) This introduced two distinct significant advantages.
Not only could they manage new data types at scale but the now had a commercially feasible approach.
However, there will still significant challenges. The first generation of Hadoop was:
- designed and optimized for Batch only workloads,
- it required dedicated clusters for each application, and,
- it didn’t integrate easily with many of the existing technologies present in the data center.
Also, like any emerging technology, Hadoop was required to meet a certain level of readiness required by the enterprise.
After running Hadoop at scale at yahoo, the team spun out to form Hortonworks with the intent to address these challenges and make Hadoop enterprise ready.
In 2011, Hortonworks was founded with the 24 original Hadoop architects and engineers from Yahoo!
This original team had been working on a technology called YARN (Yet Another Resource Negotiator) that enable multiple applications to have access to all your enterprise data through an efficient centralized platform. It is the data operating system for hadoop that provides the versatility to handle any application and dataset no matter the size or type.
Moreover, YARN provided the centralized architecture around which the critical enterprise services of Security, Operations, and Governance could be centrally addressed and integrate with existing enterprise policies.
This work allowed for a new approach to data to emerge, the modern data architecture. At the heart of this approach is the capability for Hadoop to unify data and processing in an efficient data platform
Meet Jane. Jane loves music.
And Jane’s favourite music video platform has all the music Jane loves.
So Jane listens to music from the Platform.
After october 2013: went on different things, the topic was left in storage for a while
September 2014: new model, same concept; built on plain Cascading to simplify some of the hairiest SQL logic (Optiq lacks(ed) analytic functions, so the pretty much single SQL statement from SQL Server days had to be exploded into the 12 stages)
Met guys from Lausanne at the end of September. Was already curious about Scala / Scalding then, decided to spend two days to give it a spin.
Never turned back !
TEZ 0.6.2-SNAPSHOT is required, as
Warning: TEZ 0.7 runtime is not API-compatible with 0.6 (altough the source-level API is quite close). Cascading might change the Tez dependency from time to time…
The typical Hadoop+Tez stacks pulls in a Jetty, a Tomcat, a Jersey, multiple guavas, and the kitchen sink.
We believe our workload requires 270-ish MiB of native memory. When we have time, we’ll either power down for extra sticks of RAM, or attempt to shave 20 MiB of heap per TezChild.
(reportedly)
Hash joins means hash joins, but also .filter/mapWithValue, joinWithTiny, etc.
Hash joins means hash joins, but also .filter/mapWithValue, joinWithTiny, etc.
Who wants to see another « Word Count » ?
Who wants to see another « Word Count » ?
Who wants to see another « Word Count » ?
I’m not going to look into that, fairly standard code except where I’ve been naïve. You get the idea.