This document provides an overview of real-time processing capabilities on Hortonworks Data Platform (HDP). It discusses how a trucking company uses HDP to analyze sensor data from trucks in real-time to monitor for violations and integrate predictive analytics. The company collects data using Kafka and analyzes it using Storm, HBase and Hive on Tez. This provides real-time dashboards as well as querying of historical data to identify issues with routes, trucks or drivers. The document explains components like Kafka, Storm and HBase and how they enable a unified YARN-based architecture for multiple workloads on a single HDP cluster.
Combine SAS High-Performance Capabilities with Hadoop YARNHortonworks
What if you could assemble all your data in one system and run your critical analytic applications in parallel, regardless of the format, age or location of the data? Today, thanks to the economics of Apache Hadoop-based data platforms, in particular YARN, this is possible.
SAS applications bring highly advanced, in-memory analytic processing to the data in Hadoop and enable a rich set of additional use cases with high performance analytic needs. Join this webinar and learn how, by combining their solutions, Hortonworks and SAS offer more flexibility to choose best of breed SAS HPA and LASR analytic applications in conjunction with trusted Hadoop workloads. Hear how it is possible to leverage Hadoop clusters to extend the power of SAS analytics.
You will hear directly from our experts how SAS HPA and LASR have been integrated with Hadoop YARN to:
Enable a modern data architecture without the need for fragmented processing clusters for each workload.
Ensure low-latency local data access directly from the data nodes.
Create Unified Resource Management window-panes for managing SAS HPA, LASR and HDP resources.
Speakers:
Arun Murthy, Founder and Architect at Hortonworks
Arun is a Apache Hadoop PMC member and has been a full time contributor to the project since the inception in 2006. He is also the lead of the MapReduce project and has focused on building NextGen MapReduce (YARN). Prior to co-founding Hortonworks, Arun was responsible for all MapReduce code and configuration deployed across the 42,000+ servers at Yahoo! He jointly holds the current world sorting record using Apache Hadoop.
Paul Kent, Vice President Big Data at SAS
Paul Kent is Vice President of Big Data initiatives at SAS. He spends his time between Customers, Partners and the Research & Development teams discussing, evangelizing and developing software at the confluence of big data and high performance computing. A datacenter rack full of current-generation 64bit x86 processors represents a very large aggregate memory space, thousands of threads and plentiful IO that can be harnessed to solve problems at a much larger scale than we have traditionally been accustomed to.
The Enterprise Data Lake has become the defacto repository of both structured and unstructured data within an enterprise. Being able to discover information across both structured and unstructured data using search is a key capability of enterprise data lake. In this workshop, we will provide an in-depth overview of HDP Search with focus on configuration, sizing and tuning. We will also deliver a working example to showcase the usage of HDP Search along with the rest of platform capabilities to deliver real world solution.
Deep learning with Hortonworks and Apache Spark - Hortonworks technical workshopHortonworks
Rich media is exploding all around us. From our personal usage to retailers monitoring store traffic for optimized associate placement, there is wide and growing application of rich media. Despite the pervasive usage, enterprises have had limited choice of generally available tools to analyze rich media. In this session we will look into leveraging deep learning algorithms for rich media analysis and provide practical hands on example of image recognition using Apache Hadoop and Spark.
Hp Converged Systems and Hortonworks - Webinar SlidesHortonworks
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 HP and Hortonworks Data Platform to get you started on building your modern data architecture.
Learn how to:
- Leverage best practices for deployment
- Choose a deployment model
- Design your Hadoop cluster
- Build a Modern Data Architecture and vision for the Data Lake
Combine SAS High-Performance Capabilities with Hadoop YARNHortonworks
What if you could assemble all your data in one system and run your critical analytic applications in parallel, regardless of the format, age or location of the data? Today, thanks to the economics of Apache Hadoop-based data platforms, in particular YARN, this is possible.
SAS applications bring highly advanced, in-memory analytic processing to the data in Hadoop and enable a rich set of additional use cases with high performance analytic needs. Join this webinar and learn how, by combining their solutions, Hortonworks and SAS offer more flexibility to choose best of breed SAS HPA and LASR analytic applications in conjunction with trusted Hadoop workloads. Hear how it is possible to leverage Hadoop clusters to extend the power of SAS analytics.
You will hear directly from our experts how SAS HPA and LASR have been integrated with Hadoop YARN to:
Enable a modern data architecture without the need for fragmented processing clusters for each workload.
Ensure low-latency local data access directly from the data nodes.
Create Unified Resource Management window-panes for managing SAS HPA, LASR and HDP resources.
Speakers:
Arun Murthy, Founder and Architect at Hortonworks
Arun is a Apache Hadoop PMC member and has been a full time contributor to the project since the inception in 2006. He is also the lead of the MapReduce project and has focused on building NextGen MapReduce (YARN). Prior to co-founding Hortonworks, Arun was responsible for all MapReduce code and configuration deployed across the 42,000+ servers at Yahoo! He jointly holds the current world sorting record using Apache Hadoop.
Paul Kent, Vice President Big Data at SAS
Paul Kent is Vice President of Big Data initiatives at SAS. He spends his time between Customers, Partners and the Research & Development teams discussing, evangelizing and developing software at the confluence of big data and high performance computing. A datacenter rack full of current-generation 64bit x86 processors represents a very large aggregate memory space, thousands of threads and plentiful IO that can be harnessed to solve problems at a much larger scale than we have traditionally been accustomed to.
The Enterprise Data Lake has become the defacto repository of both structured and unstructured data within an enterprise. Being able to discover information across both structured and unstructured data using search is a key capability of enterprise data lake. In this workshop, we will provide an in-depth overview of HDP Search with focus on configuration, sizing and tuning. We will also deliver a working example to showcase the usage of HDP Search along with the rest of platform capabilities to deliver real world solution.
Deep learning with Hortonworks and Apache Spark - Hortonworks technical workshopHortonworks
Rich media is exploding all around us. From our personal usage to retailers monitoring store traffic for optimized associate placement, there is wide and growing application of rich media. Despite the pervasive usage, enterprises have had limited choice of generally available tools to analyze rich media. In this session we will look into leveraging deep learning algorithms for rich media analysis and provide practical hands on example of image recognition using Apache Hadoop and Spark.
Hp Converged Systems and Hortonworks - Webinar SlidesHortonworks
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 HP and Hortonworks Data Platform to get you started on building your modern data architecture.
Learn how to:
- Leverage best practices for deployment
- Choose a deployment model
- Design your Hadoop cluster
- Build a Modern Data Architecture and vision for the Data Lake
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
Hortonworks Technical Workshop - build a yarn ready application with apache ...Hortonworks
YARN has fundamentally transformed the Hadoop landscape. It has opened hadoop from a single workload system to one that can now support a multitude of fit for purpose processing. In this workshop we will provide an overview of Apache Slider that enables custom applications to run natively in the cluster as a YARN Ready Application. The workshop will include working examples and provide an overview of work being pursued in the community around YARN Docker integration.
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.
Hortonworks Technical Workshop: What's New in HDP 2.3Hortonworks
The recently launched HDP 2.3 is a major advancement of Open Enterprise Hadoop. It represents the best of community led development with innovations spanning Apache Hadoop, Apache Ambari, Ranger, HBase, Spark and Storm. In this session we will provide an in-depth overview of new functionality and discuss it's impact on new and ongoing big data initiatives.
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.
Enabling the Real Time Analytical EnterpriseHortonworks
Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.
Discover HDP 2.1: Apache Solr for Hadoop SearchHortonworks
Apache Solr is the open source platform for searching data stored in Hadoop. Solr powers search on many of the world's largest Internet sites, enabling powerful full-text search and near real-time indexing. Whether users search for tabular, text, geo-location or sensor data in Hadoop, they find it quickly with Apache Solr. Hortonworks Data Platform 2.1 includes Apache Solr.
In this deck from their 30-minute webinar, Rohit Bakhshi, Hortonworks product manager, and Paul Codding, Hortonworks solution engineer describe how Solr works within HDP's YARN-based architecture.
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.
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.
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.
Discover HDP 2.1: Interactive SQL Query in Hadoop with Apache HiveHortonworks
In February 2013, the open source community launched the Stinger Initiative to improve speed, scale and SQL semantics in Apache Hive. After thirteen months of constant, concerted collaboration (and more than 390,000 new lines of Java code) Stinger is complete with Hive 0.13.
In this presentation, Carter Shanklin, Hortonworks director of product management, and Owen O'Malley, Hortonworks co-founder and committer to Apache Hive, discuss how Hive enables interactive query using familiar SQL semantics.
Discover HDP2.1: Apache Storm for Stream Data Processing in HadoopHortonworks
For the first time, Hortonworks Data Platform ships with Apache Storm for processing stream data in Hadoop.
In this presentation, Himanshu Bari, Hortonworks senior product manager, and Taylor Goetz, Hortonworks engineer and committer to Apache Storm, cover Storm and stream processing in HDP 2.1:
+ Key requirements of a streaming solution and common use cases
+ An overview of Apache Storm
+ Q & A
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
Double Your Hadoop Hardware Performance with SmartSenseHortonworks
Hortonworks SmartSense provides proactive recommendations that improve cluster performance, security and operations. And since 30% of issues are configuration related, Hortonworks SmartSense makes an immediate impact on Hadoop system performance and availability, in some cases boosting hardware performance by two times. Learn how SmartSense can help you increase the efficiency of your Hadoop hardware, through customized cluster recommendations.
View the on-demand webinar: https://hortonworks.com/webinar/boosts-hadoop-hardware-performance-2x-smartsense/
Hortonworks Data Platform 2.2 includes Apache HBase for fast NoSQL data access. In this 30-minute webinar, we discussed HBase innovations that are included in HDP 2.2, including: support for Apache Slider; Apache HBase high availability (HA); block ache compression; and wire-level encryption.
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
Zeppelin has become a popular way to unlock the value of data lake due to its user interface and appeal to business users. These business users ask their IT department for access to Zeppelin. Enterprise IT department want to help their business users but they have several enterprise concerns such as enterprise security, integration with their corporate LDAP/AD, scalability and multi-user environment, integration with Ranger and Kerberos. This session will walk through enterprise concerns and how these concerns can be handled with Zeppelin.
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
Hortonworks Technical Workshop - build a yarn ready application with apache ...Hortonworks
YARN has fundamentally transformed the Hadoop landscape. It has opened hadoop from a single workload system to one that can now support a multitude of fit for purpose processing. In this workshop we will provide an overview of Apache Slider that enables custom applications to run natively in the cluster as a YARN Ready Application. The workshop will include working examples and provide an overview of work being pursued in the community around YARN Docker integration.
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.
Hortonworks Technical Workshop: What's New in HDP 2.3Hortonworks
The recently launched HDP 2.3 is a major advancement of Open Enterprise Hadoop. It represents the best of community led development with innovations spanning Apache Hadoop, Apache Ambari, Ranger, HBase, Spark and Storm. In this session we will provide an in-depth overview of new functionality and discuss it's impact on new and ongoing big data initiatives.
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.
Enabling the Real Time Analytical EnterpriseHortonworks
Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.
Discover HDP 2.1: Apache Solr for Hadoop SearchHortonworks
Apache Solr is the open source platform for searching data stored in Hadoop. Solr powers search on many of the world's largest Internet sites, enabling powerful full-text search and near real-time indexing. Whether users search for tabular, text, geo-location or sensor data in Hadoop, they find it quickly with Apache Solr. Hortonworks Data Platform 2.1 includes Apache Solr.
In this deck from their 30-minute webinar, Rohit Bakhshi, Hortonworks product manager, and Paul Codding, Hortonworks solution engineer describe how Solr works within HDP's YARN-based architecture.
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.
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.
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.
Discover HDP 2.1: Interactive SQL Query in Hadoop with Apache HiveHortonworks
In February 2013, the open source community launched the Stinger Initiative to improve speed, scale and SQL semantics in Apache Hive. After thirteen months of constant, concerted collaboration (and more than 390,000 new lines of Java code) Stinger is complete with Hive 0.13.
In this presentation, Carter Shanklin, Hortonworks director of product management, and Owen O'Malley, Hortonworks co-founder and committer to Apache Hive, discuss how Hive enables interactive query using familiar SQL semantics.
Discover HDP2.1: Apache Storm for Stream Data Processing in HadoopHortonworks
For the first time, Hortonworks Data Platform ships with Apache Storm for processing stream data in Hadoop.
In this presentation, Himanshu Bari, Hortonworks senior product manager, and Taylor Goetz, Hortonworks engineer and committer to Apache Storm, cover Storm and stream processing in HDP 2.1:
+ Key requirements of a streaming solution and common use cases
+ An overview of Apache Storm
+ Q & A
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
Double Your Hadoop Hardware Performance with SmartSenseHortonworks
Hortonworks SmartSense provides proactive recommendations that improve cluster performance, security and operations. And since 30% of issues are configuration related, Hortonworks SmartSense makes an immediate impact on Hadoop system performance and availability, in some cases boosting hardware performance by two times. Learn how SmartSense can help you increase the efficiency of your Hadoop hardware, through customized cluster recommendations.
View the on-demand webinar: https://hortonworks.com/webinar/boosts-hadoop-hardware-performance-2x-smartsense/
Hortonworks Data Platform 2.2 includes Apache HBase for fast NoSQL data access. In this 30-minute webinar, we discussed HBase innovations that are included in HDP 2.2, including: support for Apache Slider; Apache HBase high availability (HA); block ache compression; and wire-level encryption.
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
Zeppelin has become a popular way to unlock the value of data lake due to its user interface and appeal to business users. These business users ask their IT department for access to Zeppelin. Enterprise IT department want to help their business users but they have several enterprise concerns such as enterprise security, integration with their corporate LDAP/AD, scalability and multi-user environment, integration with Ranger and Kerberos. This session will walk through enterprise concerns and how these concerns can be handled with Zeppelin.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
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.
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.
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
Apache Ambari is a single framework for IT administrators to provision, manage and monitor a Hadoop cluster. Apache Ambari 1.7.0 is included with Hortonworks Data Platform 2.2.
In this 30-minute webinar, Hortonworks Product Manager Jeff Sposetti and Apache Ambari committer Mahadev Konar discussed new capabilities including:
Improvements to Ambari core - such as support for ResourceManager HA
Extensions to Ambari platform - introducing Ambari Administration and Ambari Views
Enhancements to Ambari Stacks - dynamic configuration recommendations and validations via a "Stack Advisor"
Supporting Financial Services with a More Flexible Approach to Big DataWANdisco Plc
In this webinar, WANdisco and Hortonworks 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
Hortonworks Data In Motion Webinar Series Pt. 2Hortonworks
How Hortonworks DataFlow (HDF), powered by Apache NIFi, MiNiFi, Kafka and Storm, and it’s associated HDF Certification Program make it easier and faster to integrate different systems together. Highlights on the latest partner integrations from HPE, SAS, Attunity, Impetus Technologies, Kepware and Midfin Systems. “
Watch the webinar on-demand: http://hortonworks.com/webinar/make-big-data-ecosystem-work-better/
HDF Partner certification program: http://hortonworks.com/partners/product-integration-certification/#hdf-integration
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber.
Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable.
At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads.
At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Why do we now have electric cars at prodcution scale, quad copters and drones cheap enough for the home hobbyist, and VR displays being bought by companies like Facebook?
Because the technology is there now, thanks to advances made in other industries, solving problems at scale in a big marketplace.
At Scale (in this case):
$270 bn smartphone mkt in 2014
$120 bn internet advertising (proj 2015)
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.
Our goal since our inception has been very simple: to enable a Modern Data Architecture with Enterprise Hadoop. Everything we do is with this architectural goal in mind.
Single focus - enabling Apache Hadoop as an enterprise data platform for any app and any data type
In the open, partner for success.
Everything in the open.
Joint deep engineering Microsoft (HD Insight), HP, SAP and Teradata
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
Our product, the Hortonworks Data Platform (or HDP for short) is a completely open source, enterprise-grade data platform that’s comprised of dozens of Apache open source projects including Apache Hadoop and YARN at its center.
We have a comprehensive engineering, testing, and certification process that integrates and packages all of these components into a cohesive platform that the enterprise can consume and deploy at scale. And our model enables us to proactively manage new innovations and new open source projects into HDP as they emerge.
To ensure the highest quality, we have a test suite, unique to Hortonworks, that is comprised of 10’s of thousands of system and integration tests that we run at scale on a regular basis including on the world’s largest Hadoop clusters at Yahoo! as part of our co-development relationship.
While our pure-play competitors focus on proprietary components for security, operations, and governance, we invest in new open source projects that address these areas.
For example, earlier in 2014, we acquired a small company called XA Secure that provided a comprehensive security and administration product. We flipped the technology in wholesale into open source as Apache Ranger.
Since our security, operations and governance technologies are open source projects, our partners are able to work with us on those projects to ensure deep integration within our joint solution architectures.
Our goal since our inception has been very simple: to enable a Modern Data Architecture with Enterprise Hadoop. Everything we do is with this architectural goal in mind.
Elastic Search Flume Sink does exist
Elastic Search Flume Sink does exist
The key abstraction in Kafka is the topic. Producers publish their records to a topic, and consumers subscribe to one or more topics
A Kafka topic is just a sharded write-ahead log
Messages are not deleted when they are read but retained with some configurable SLA (say a few days or a week). This allows usage in situations where the consumer of data may need to reload data.
It also makes it possible to support space-efficient publish-subscribe as there is a single shared log no matter how many consumers; in traditional messaging systems there is usually a queue per consumer, so adding a consumer doubles your data size. This makes Kafka a good fit for things outside the bounds of normal messaging systems such as acting as a pipeline for offline data systems such as Hadoop. These offline systems may load only at intervals as part of a periodic ETL cycle, or may go down for several hours for maintenance, during which time Kafka is able to buffer even TBs of unconsumed data if needed
Replication for HA/fault tolerance is built in
Pull based system for consumers instead of pushed base
Crude benchmark:
Basically, single threaded synchronous messages are 400k per second when using 6 "datanode-ish" servers. This goes up to 2+ MM when using partitions and asynchronous messages. Server specs in the benchmark:
Intel Xeon 2.5 GHz processor with six cores
Six 7200 RPM SATA drives
32GB of RAM
1Gb Ethernet
A traditional queue retains messages in-order on the server, and if multiple consumers consume from the queue then the server hands out messages in the order they are stored. However, although the server hands out messages in order, the messages are delivered asynchronously to consumers, so they may arrive out of order on different consumers. This effectively means the ordering of the messages is lost in the presence of parallel consumption. Messaging systems often work around this by having a notion of "exclusive consumer" that allows only one process to consume from a queue, but of course this means that there is no parallelism in processing.
Kafka does it better. By having a notion of parallelism—the partition—within the topics, Kafka is able to provide both ordering guarantees and load balancing over a pool of consumer processes. This is achieved by assigning the partitions in the topic to the consumers in the consumer group so that each partition is consumed by exactly one consumer in the group. By doing this we ensure that the consumer is the only reader of that partition and consumes the data in order.
http://www.quora.com/Kafka-writes-every-message-to-broker-disk-Still-performance-wise-it-is-better-than-some-of-the-in-memory-message-storing-message-queues-Why-is-that
Elastic Search Flume Sink does exist
Elastic Search Flume Sink does exist
Elastic Search Flume Sink does exist
This all changed with the introduction of Hadoop 2 and YARN. Introduced in October, 2013 it changed everything.
Introduced in MR-279 by Arun Murthy in 2009, Arun and the team at Hortonworks architected and led it’s development as the core change in Hadoop 2. Our view was that to truly enable Hadoop as a component of a broad data architecture, YARN was the fundamental requirement as it turns Hadoop from a single application data system to a multi application data system. This is foundational to our approach of innovating from the core outwards to build Enterprise Hadoop.
With YARN it is now possible to land all data in one cluster and then access it in multiple ways: from batch to interactive to real-time.
Today, YARN, at the core of Hadoop is the center of our focus on innovation in and around Hadoop. It is clearly the enabling technology that has started a transition to a data lake within organizations.
Simply stated… Hortonworks Architected & led development of YARN in order to enable the Modern Data Architecture
Elastic Search Flume Sink does exist
Data is ingested, it’s on the dashboard, and it’s in HDFS.