Red Hat Middleware Solutions for SAP Systems
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An overview of Red Hat JBoss Middleware technology and how it can enhance and compliment your SAP landscape.
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Decoupling Compute and Storage for Data Workloads
This was presented by Carlos Quieroz, Head of Data Platform at Development Bank of Singapore, at the Data Transformation in Financial Services meetup in Singapore jointly hosted by Accenture, Talend, BigDataSG Hadoop, and Alluxio.
The Architecture of Decoupling Compute and Storage with Alluxio
Haoyuan Li presented on Alluxio, a memory-speed virtual distributed storage system he created. Alluxio addresses the challenges of decoupling compute and storage by serving data from memory, accelerating access. It provides a unified namespace and cache across multiple storage systems like HDFS, S3 and Swift. Alluxio has been adopted by many large companies to improve performance for analytics and machine learning workloads involving big data.
Achieving compute and storage independence for data-driven workloads
Alluxio provides a unified interface to access data across multiple storage systems, allowing compute and storage to scale independently for data-driven applications. It uses a virtual unified file system with a global namespace and server-side API translation to abstract data location and access. Alluxio intelligently manages data placement across memory, SSDs and HDDs using multi-tier caching for local performance on remote data. This allows flexible deployment of compute like Spark on any cloud while keeping data fully controlled on-premises. Alluxio is seeing wide adoption with many large production deployments handling thousands of nodes. Upcoming features include POSIX API support and preview of version 2.0.
HDF5 iRODS
Numerous scientific teams use the HDF5 format to store very large datasets. Efficient use of this data in a distributed environment depends on client applications being able to read any subset of the data without transferring the entire file to the local machine. The goal of the HDF5-iRODS Project was to develop an HDF5-iRODS module for the iRODS datagrid server that supported this capability, and to apply the technology to an NCSA/SDSC Strategic Applications Program (SAP) project, FLASH.
A joint team from The HDF Group (representing NCSA) and the SDSC SRB group collaborated to accomplish the project goal. The team implemented five HDF5 microservices functions on the iRODS server, and developed an iRODS FLASH slice client application. The client implementation also includes a JNI interface that allows HDFView, a standard tool for browsing HDF5 files, to access HDF5 files stored remotely in iRODS. Finally, three new collection client/server calls were added to the iRODS APIs, making it easier for users to query the content of an iRODS collection.
Big Data Overview/Teaser (6 Aug 2013)
On Tuesday, 6 Aug 2013 the Canadian Chamber of Commerce hosted me for a very brief talk on Big Data. In the 20 minutes I stretched to 25, I shared a few interesting stories about big data, considered privacy implications, focused on opportunities in Hong Kong, and ruminated on the future. This is the deck I used.
Information Retrieval, Applied Statistics and Mathematics onBigData - German ...
This document discusses information retrieval and applied statistics using big data. It summarizes that supercomputers can now process 18 TB of main memory, 1008 CPU cores, and 113 TFLOPS on a single rack, compared to earlier supercomputers requiring entire buildings. It also discusses Hadoop and how distributed processing across many nodes can retrieve 1 TB of data in 100 milliseconds with 1000 nodes, compared to 100 seconds on a single node. Finally, it outlines challenges of processing vast amounts of data and need for feature extraction, dimension reduction, and parallelization across clusters.
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The Architecture of Decoupling Compute and Storage with Alluxio
Haoyuan Li presented on Alluxio, a memory-speed virtual distributed storage system he created. Alluxio addresses the challenges of decoupling compute and storage by serving data from memory, accelerating access. It provides a unified namespace and cache across multiple storage systems like HDFS, S3 and Swift. Alluxio has been adopted by many large companies to improve performance for analytics and machine learning workloads involving big data.
Achieving compute and storage independence for data-driven workloads
Alluxio provides a unified interface to access data across multiple storage systems, allowing compute and storage to scale independently for data-driven applications. It uses a virtual unified file system with a global namespace and server-side API translation to abstract data location and access. Alluxio intelligently manages data placement across memory, SSDs and HDDs using multi-tier caching for local performance on remote data. This allows flexible deployment of compute like Spark on any cloud while keeping data fully controlled on-premises. Alluxio is seeing wide adoption with many large production deployments handling thousands of nodes. Upcoming features include POSIX API support and preview of version 2.0.
HDF5 iRODS
Numerous scientific teams use the HDF5 format to store very large datasets. Efficient use of this data in a distributed environment depends on client applications being able to read any subset of the data without transferring the entire file to the local machine. The goal of the HDF5-iRODS Project was to develop an HDF5-iRODS module for the iRODS datagrid server that supported this capability, and to apply the technology to an NCSA/SDSC Strategic Applications Program (SAP) project, FLASH.
A joint team from The HDF Group (representing NCSA) and the SDSC SRB group collaborated to accomplish the project goal. The team implemented five HDF5 microservices functions on the iRODS server, and developed an iRODS FLASH slice client application. The client implementation also includes a JNI interface that allows HDFView, a standard tool for browsing HDF5 files, to access HDF5 files stored remotely in iRODS. Finally, three new collection client/server calls were added to the iRODS APIs, making it easier for users to query the content of an iRODS collection.
Big Data Overview/Teaser (6 Aug 2013)
On Tuesday, 6 Aug 2013 the Canadian Chamber of Commerce hosted me for a very brief talk on Big Data. In the 20 minutes I stretched to 25, I shared a few interesting stories about big data, considered privacy implications, focused on opportunities in Hong Kong, and ruminated on the future. This is the deck I used.
Information Retrieval, Applied Statistics and Mathematics onBigData - German ...
This document discusses information retrieval and applied statistics using big data. It summarizes that supercomputers can now process 18 TB of main memory, 1008 CPU cores, and 113 TFLOPS on a single rack, compared to earlier supercomputers requiring entire buildings. It also discusses Hadoop and how distributed processing across many nodes can retrieve 1 TB of data in 100 milliseconds with 1000 nodes, compared to 100 seconds on a single node. Finally, it outlines challenges of processing vast amounts of data and need for feature extraction, dimension reduction, and parallelization across clusters.
Brief redhatj bossportfoliooverview-v1.0
This document provides an overview of Red Hat JBoss middleware portfolio. It discusses Red Hat's history and growth as an open source leader. The document then outlines Red Hat's middleware products which help customers integrate applications and data, accelerate application development, and automate business processes and rules. Specific products are mentioned like JBoss Enterprise Application Platform, JBoss Fuse, JBoss BPM Suite, and how they provide benefits like lower TCO, better support, and avoiding vendor lock-in compared to proprietary solutions.
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Next Generation Hadoop Introduction
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- Founded in 1993 and went public in 1999.
- Provides a range of open source products and solutions including operating systems, middleware, management tools, and more.
- Has over 8,300 employees and offices in 35+ countries serving over 90% of Fortune 500 companies.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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2. Which barriers stand in the way of AI adoption.
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4. Organizational processes and structures that may inhibit effective AI adoption.
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This document provides an introduction to Red Hat JBoss middleware. It discusses how Red Hat focuses on community-powered innovation to develop open source middleware projects. It then summarizes Red Hat's portfolio of middleware products including application servers, integration, BPM, and cloud platforms. The document outlines the benefits of Red Hat's subscription model for middleware which provides long-term support, security updates, and assistance versus maintaining community projects in production. It also introduces Red Hat's Platform as a Service, OpenShift, and the available JBoss xPaaS services it provides.
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Red Hat is an open source software company that provides Linux operating systems, middleware, storage, and cloud computing solutions. Some key facts:
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2014 july 24_what_ishadoop
This document discusses big data and Hadoop. It notes that traditional technologies are not well-suited to handle the volume of data generated today. Hadoop was created by companies like Google and Yahoo to address this challenge through its distributed file system HDFS and processing framework MapReduce. The document promotes Hadoop and the Hortonworks Data Platform for storing, processing, and analyzing large volumes of diverse data in a cost-effective manner.
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The document discusses Red Hat's vision for open hybrid cloud middleware using JBoss middleware products. It describes how Red Hat sees middleware bridging the gap between complex enterprise applications and platform as a service (PaaS) offerings. The document outlines Red Hat's plans to deliver specialized PaaS offerings for application development, integration, business process management, and mobile using JBoss products to create an open hybrid cloud middleware platform.
Fuse overview
This document provides an overview of Red Hat JBoss Fuse, an open source integration platform. It discusses the history and components of JBoss Fuse, including Apache Camel, CXF, ActiveMQ, Karaf and Fabric8. It describes how JBoss Fuse can enable integration everywhere in a real-time enterprise by integrating applications, services, devices and partners through its lightweight footprint and deployment options both on-premise and in the cloud. The document also highlights key benefits of JBoss Fuse such as reducing costs, simplifying management and enabling new business opportunities through greater connectivity and data sharing.
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Come learn about the work IBM is doing to ensure to that:
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- that key tools and capabilities are available (monitoring, post mortem investigation)
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A short introduction to Big Data; what it is and what are the implications for performance and capacity management.
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This document discusses new features in Red Hat Enterprise Virtualization 3.1, including an HTML5 GWT web admin, improved network management, NIC hot plug, direct LUN support, live snapshots, storage live migration, a POSIX file system storage domain, and Gluster integration. Red Hat Enterprise Virtualization provides a KVM hypervisor and management tools for deploying and managing virtual machines on cloud infrastructure.
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The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
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Introduction to Red Hat
Red Hat is a leading open source solutions provider with over $1 billion in revenue. Some key points about Red Hat:
- Founded in 1993 and went public in 1999.
- Provides a range of open source products and solutions including operating systems, middleware, management tools, and more.
- Has over 8,300 employees and offices in 35+ countries serving over 90% of Fortune 500 companies.
- Offers subscription-based support and assistance for its open source technologies.
Why Virtualization is important by Tom Phelan of BlueData
This presentation will investigate the advantages and disadvantage of running an n-node Hadoop cluster in each of the following environments:
• Bare metal
• Private cloud using a traditional virtual machine environment
• Private cloud using a containerized virtual environment
Tom will follow this with a discussion of real Hadoop Use Cases and how each would, or would not, be suitable for running in each environment.
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Adobe and the Dell EMC RackHD team provide an overview on how Adobe is modernizing their datacenters using public and private clouds enabled by infrastructure as code technologies to abstract their infrastructure for application deployments and improve operational efficiencies.
Big data insights with Red Hat JBoss Data Virtualization
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During this session, we’ll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data. You’ll learn how Red Hat JBoss Data Virtualization:
Can help you integrate your existing and growing data infrastructure.
Integrates big data with your existing enterprise data infrastructure.
Lets non-technical users access big data result sets.
We’ll also provide typical uses cases and examples and a demonstration of the integration of Hadoop sentiment analysis with sales data.
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Big Data is one of the hot topics and has got the attention of the IT industry globally. It is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become. More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies, cost reductions and reduced risk.
This presentation focuses on why, what, how of big data as we explore some of Microsoft's big data solutions - HDInsight azure service and PowerBI, providing insights into the world of Big data.
Data Virtualization Primer - Introduction
This document provides an introduction to data virtualization and JBoss Data Virtualization. It discusses key concepts like data services, connectors, models, and virtual databases. It describes the architecture and components of JBoss Data Virtualization including tools for creating data views, the runtime environment, and repository. Use cases like business intelligence, 360-degree views, cloud data integration, and big data integration are also covered.
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- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
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- Verstehen des DLAU-Tools und wie man es am besten nutzt
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Red Hat Middleware Solutions for SAP Systems
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