This document discusses protecting big data with Intel technologies. It summarizes Intel's Distribution for Apache Hadoop software, which includes encryption and role-based access control features. The software provides an encryption framework that extends Hadoop's compression codec and establishes a common encryption API. It also allows different key storage systems to integrate for key management. Performance tests show Intel AES-NI instructions accelerate encryption and decryption, providing up to 19.8x faster decryption compared to non-AES-NI.
Hadoop, Big Data, and the Future of the Enterprise Data Warehousetervela
Under the umbrella of big data, the nature of data warehousing inside enterprises is undergoing a massive transformation. Originally designed as a clearinghouse for organizing data to discover and analyze historical trends, business units are now putting extreme pressure on their data groups to enhance their services. Their goals: provide better customer service, real-time marketing, and more efficient business operations.
In this webcast, Big Data expert Barry Thompson will discuss how will enterprise data warehouses are evolving to meet these challenges. Some of the topics we will cover include:
- How Hadoop and other big data technologies are coexisting with traditional data warehouses
- Dealing with multiple big data sources – and multiple versions of the truth
- Techniques like warehouse replication and parallel data loading that enable platforms with different levels of service for different types of applications
Hadoop, Big Data, and the Future of the Enterprise Data Warehousetervela
Under the umbrella of big data, the nature of data warehousing inside enterprises is undergoing a massive transformation. Originally designed as a clearinghouse for organizing data to discover and analyze historical trends, business units are now putting extreme pressure on their data groups to enhance their services. Their goals: provide better customer service, real-time marketing, and more efficient business operations.
In this webcast, Big Data expert Barry Thompson will discuss how will enterprise data warehouses are evolving to meet these challenges. Some of the topics we will cover include:
- How Hadoop and other big data technologies are coexisting with traditional data warehouses
- Dealing with multiple big data sources – and multiple versions of the truth
- Techniques like warehouse replication and parallel data loading that enable platforms with different levels of service for different types of applications
"A Study of I/O and Virtualization Performance with a Search Engine based on ...Lucidworks (Archived)
Documentum xPlore provides an integrated Search facility for the Documentum Content Server. The standalone search engine is based on EMC's xDB (Native XML database) and Lucene. In this talk we will introduce xPlore and some of its key components and capabilities. These include aspects of a tight integration of Lucene with the XML database: xQuery translation and optimization into Lucene query/API's as well as transactional update Lucene). In addition, xPlore is being deployed aggressively into virtualized environments (both disk I/O and VM). We cover some performance results and tuning tips in these areas.
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4Frazer Clement
MySQL Cluster 7.4 has been benchmarked executing over 200 million queries per second on commodity hardware. This presentation from Oracle OpenWorld 2015 describes MySQL Cluster's architecture and gives some detail on how this benchmark was achieved, as well as some tips on getting started with MySQL Cluster 7.4.
Hadoop World 2011: Unlocking the Value of Big Data with Oracle - Jean-Pierre ...Cloudera, Inc.
Analyzing new and diverse digital data streams can reveal new sources of economic value, provide fresh insights into customer behavior and identify market trends early on. But this influx of new data can create challenges for IT departments. To derive real business value from Big Data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Attend this session to learn how Oracle’s end-to-end value chain for Big Data can help you unlock the value of Big Data.
Introduction to Hortonworks Data Platform for WindowsHortonworks
According to IDC, Windows Servers run more than 50% of the servers in the Enterprise Data Center. Hortonworks has worked closely with Microsoft to port Apache Hadoop to Windows to enable organizations to take advantage of this emerging Big Data technology. Join us in this informative webinar to hear about the new Hortonworks Data Platform for Windows.
In less than an hour, you’ll learn:
-Key capabilities available in Hortonworks Data Platform for Windows
-How HDP for Windows integrates with Microsoft tools
-Key workloads and use cases for driving Hadoop today
The Comprehensive Approach: A Unified Information ArchitectureInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Slides from the Live Webcast on May 29, 2012
The worlds of Business Intelligence (BI) and Big Data Analytics can seem at odds, but only because we have yet to fully experience comprehensive approach to managing big data – a Unified Big Data Architecture. The dynamics continue to change as vendors begin to emphasize the importance of leveraging SQL, engineering and operational skills, as well as incorporating novel uses of MapReduce to improve distributed analytic processing.
Register for this episode of The Briefing Room to learn the value of taking a strategic approach for managing big data from veteran BI and data warehouse consultant Richard Hackathorn. He'll be briefed by Chris Twogood of Teradata, who will outline his company's recent advances in bridging the gap between Hadoop and SQL to unlock deeper insights and explain the role of Teradata Aster and SQL-MapReduce as a Discovery Platform for Hadoop environments.
For more information visit: http://www.insideanalysis.com
Watch us on YouTube: http://www.youtube.com/playlist?list=PL5EE76E2EEEC8CF9E
Updated deck of previous GOTO talk from Chicago. Looking at the current pace of technology and how we have evolved our process at Carbon Five to handle dynamic teams and fast, iterative development.
Overview of some of the processes and techniques we use to help accelerate product development at Carbon Five. Dives into Design Thinking, Product Design Sprints, Agile XP, release planning and story writing.
The Big Data Scotland 2015 conference brought together business leaders and technologists from across the country to explore the value of Big Data & Analytics. The conference considered technological developments, market trends and business strategy; showcasing innovative examples of analytics being used effectively across a range of practical applications. The event offered a unique opportunity for technologists to come together for knowledge exchange, networking and debate.
"A Study of I/O and Virtualization Performance with a Search Engine based on ...Lucidworks (Archived)
Documentum xPlore provides an integrated Search facility for the Documentum Content Server. The standalone search engine is based on EMC's xDB (Native XML database) and Lucene. In this talk we will introduce xPlore and some of its key components and capabilities. These include aspects of a tight integration of Lucene with the XML database: xQuery translation and optimization into Lucene query/API's as well as transactional update Lucene). In addition, xPlore is being deployed aggressively into virtualized environments (both disk I/O and VM). We cover some performance results and tuning tips in these areas.
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4Frazer Clement
MySQL Cluster 7.4 has been benchmarked executing over 200 million queries per second on commodity hardware. This presentation from Oracle OpenWorld 2015 describes MySQL Cluster's architecture and gives some detail on how this benchmark was achieved, as well as some tips on getting started with MySQL Cluster 7.4.
Hadoop World 2011: Unlocking the Value of Big Data with Oracle - Jean-Pierre ...Cloudera, Inc.
Analyzing new and diverse digital data streams can reveal new sources of economic value, provide fresh insights into customer behavior and identify market trends early on. But this influx of new data can create challenges for IT departments. To derive real business value from Big Data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Attend this session to learn how Oracle’s end-to-end value chain for Big Data can help you unlock the value of Big Data.
Introduction to Hortonworks Data Platform for WindowsHortonworks
According to IDC, Windows Servers run more than 50% of the servers in the Enterprise Data Center. Hortonworks has worked closely with Microsoft to port Apache Hadoop to Windows to enable organizations to take advantage of this emerging Big Data technology. Join us in this informative webinar to hear about the new Hortonworks Data Platform for Windows.
In less than an hour, you’ll learn:
-Key capabilities available in Hortonworks Data Platform for Windows
-How HDP for Windows integrates with Microsoft tools
-Key workloads and use cases for driving Hadoop today
The Comprehensive Approach: A Unified Information ArchitectureInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Slides from the Live Webcast on May 29, 2012
The worlds of Business Intelligence (BI) and Big Data Analytics can seem at odds, but only because we have yet to fully experience comprehensive approach to managing big data – a Unified Big Data Architecture. The dynamics continue to change as vendors begin to emphasize the importance of leveraging SQL, engineering and operational skills, as well as incorporating novel uses of MapReduce to improve distributed analytic processing.
Register for this episode of The Briefing Room to learn the value of taking a strategic approach for managing big data from veteran BI and data warehouse consultant Richard Hackathorn. He'll be briefed by Chris Twogood of Teradata, who will outline his company's recent advances in bridging the gap between Hadoop and SQL to unlock deeper insights and explain the role of Teradata Aster and SQL-MapReduce as a Discovery Platform for Hadoop environments.
For more information visit: http://www.insideanalysis.com
Watch us on YouTube: http://www.youtube.com/playlist?list=PL5EE76E2EEEC8CF9E
Updated deck of previous GOTO talk from Chicago. Looking at the current pace of technology and how we have evolved our process at Carbon Five to handle dynamic teams and fast, iterative development.
Overview of some of the processes and techniques we use to help accelerate product development at Carbon Five. Dives into Design Thinking, Product Design Sprints, Agile XP, release planning and story writing.
The Big Data Scotland 2015 conference brought together business leaders and technologists from across the country to explore the value of Big Data & Analytics. The conference considered technological developments, market trends and business strategy; showcasing innovative examples of analytics being used effectively across a range of practical applications. The event offered a unique opportunity for technologists to come together for knowledge exchange, networking and debate.
(SEC301) Strategies for Protecting Data Using Encryption in AWSAmazon Web Services
Protecting sensitive data in the cloud typically requires encryption. Managing the keys used for encryption can be challenging as your sensitive data passes between services and applications. AWS offers several options for using encryption and managing keys to help simplify the protection of your data at rest. This session will help you understand which features are available and how to use them, with emphasis on AWS Key Management Service and AWS CloudHSM. Adobe Systems Incorporated will present their experience using AWS encryption services to solve data security needs.
Big Data, Big Content, and Aligning Your Storage StrategyHitachi Vantara
Fred Oh's presentation for SNW Spring, Monday 4/2/12, 1:00–1:45PM
Unstructured data growth is in an explosive state, and has no signs of slowing down. Costs continue to rise along with new regulations mandating longer data retention. Moreover, disparate silos, multivendor storage assets and less than optimal use of existing assets have all contributed to ‘accidental architectures.’ And while they can be key drivers for organizations to explore incremental, innovative solutions to their data challenges, they may provide only short-term gain. Join us for this session as we outline the business benefits of a truly unified, integrated platform to manage all block, file and object data that allows enterprises can make the most out of their storage resources. We explore the benefits of an integrated approach to multiprotocol file sharing, intelligent file tiering, federated search and active archiving; how to simplify and reduce the need for backup without the risk of losing availability; and the economic benefits of an integrated architecture approach that leads to lowering TCSO by 35% or more.
SAP HANA and Apache Hadoop for Big Data Management (SF Scalable Systems Meetup)Will Gardella
In this presentation I argue that the future of data management may see a split between (1) real-time in-memory systems such as SAP HANA for most enterprise workloads (2) disk-based free and open-source Apache Hadoop for certain specialized big data uses.
The presentation starts with a definition of what is intended by the term big data, then talks about SAP HANA and Apache Hadoop from the perspective of suitability for enterprise use with a special concentration on Hadoop. (The basics of SAP HANA were covered in the immediately preceding session). This is followed by a description of currently available SAP support for Apache Hadoop in SAP BI 4.0 and SAP Data Services / EIM. Due to time constraints I did not discuss Apache Hadoop support built into Sybase IQ.
Big Data Beyond Hadoop*: Research Directions for the FutureOdinot Stanislas
Michael Wrinn
Research Program Director, University Research Office,
Intel Corporation
Jason Dai
Engineering Director and Principal Engineer,
Intel Corporation
In this slidecast, Richard Treadway and Rich Seger from NetApp discuss the company's storage solutions for Big Data and HPC. The company's HPC solutions for Lustre support massive performance and storage density without sacrificing efficiency.
Cutting Big Data Down to Size with AMD and DellAMD
Matt Kimball, AMD Server Solutions Marketing presentation on "Cutting Big Data Down to Size with AMD and Dell" from Dell World.
Learn how “Hadoop” solutions are helping companies overcome growing pressures on IT budgets with an innovative approach to Big Data.
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
With DataPortal Business Data Sharing Software, business data can be shared with hundreds of partners within minutes, with “Point-and-Click” ease.
No development, works across database vendors, minimal setup and configuration, (no cost, no manual installation for client), SSL encryption, no firewall modification, no unnecessary conversion (e.g. XML).
Using a Field Programmable Gate Array to Accelerate Application PerformanceOdinot Stanislas
Intel s'intéresse tout particulièrement aux FPGA et notamment au potentiel qu'ils apportent lorsque les ISV et développeurs ont des besoins très spécifiques en Génomique, traitement d'images, traitement de bases de données, et même dans le Cloud. Dans ce document vous aurez l'occasion d'en savoir plus sur notre stratégie, et sur un programme de recherche lancé par Intel et Altera impliquant des Xeon E5 équipés... de FPGA
Intel is looking at FPGA and what they bring to ISVs and developers and their very specific needs in genomics, image processing, databases, and even in the cloud. In this document you will have the opportunity to learn more about our strategy, and a research program initiated by Intel and Altera involving Xeon E5 with... FPGA inside.
Auteur(s)/Author(s):
P. K. Gupta, Director of Cloud Platform Technology, Intel Corporation
Hands-on Lab: How to Unleash Your Storage Performance by Using NVM Express™ B...Odinot Stanislas
(FR)
Voici un excellent document qui explique étape après étape comment installer, monitorer et surtout correctement benchmarker ses SSD PCIe/NVMe (pas si simple que ça). Autre élément clé : comment analyser la charge I/O de véritables applications? Combien d'IOPS, en read, en write, quelle bande passante et surtout quel impact sur la durée de vie des SSD? Bref à mettre en toute les mains, et un merci à mon collègue Andrey Kudryavtsev.
(EN)
An excellent content which describe step by step how to install, monitor and benchmark PCIe/NVMe SSD (many trick not so simple). Another key learning: how to measure real I/O activities on a real workload? How many R/W IOPS, block size, throughtput, and finally what's the impact on SSD endurance and (real)life? A must read, and a huge thanks to my colleague Andrey Kudryavtsev.
Auteurs/Authors:
Andrey Kudryavtsev, SSD Solution Architect, Intel Corporation
Zhdan Bybin, Application Engineer, Intel Corporation
Le SDN et NFV sont très à la mode en ce moment car en passant des appliance physiques aux équipement réseau massivement logiciel, celà devrait offrir une grande flexibilité et agilité aux entreprises (et telco en particulier). Néanmoins chainer des services réseau est un exercice encore très complexe et ce document vous explique ce qu'il est déjà possible de faire sur OpenStack en couplant par exemple : un load balancer (BigIP), un Firewall (BigIP), un réseau virtuel WAN (RiverBed) ou encore un routeur virtuel (Brocade).
Ceph: Open Source Storage Software Optimizations on Intel® Architecture for C...Odinot Stanislas
Après la petite intro sur le stockage distribué et la description de Ceph, Jian Zhang réalise dans cette présentation quelques benchmarks intéressants : tests séquentiels, tests random et surtout comparaison des résultats avant et après optimisations. Les paramètres de configuration touchés et optimisations (Large page numbers, Omap data sur un disque séparé, ...) apportent au minimum 2x de perf en plus.
SNIA : Swift Object Storage adding EC (Erasure Code)Odinot Stanislas
In depth presentation on EC integration in Swift object storage. Content delivered by Paul Luse, Sr. Staff Engineer @ Intel and Kevin Greenan, Staff Software Engineer - Box during fall SNIA event
PCI Express* based Storage: Data Center NVM Express* Platform TopologiesOdinot Stanislas
(FR)
Le PCI Express se démocratise de plus en plus dans les serveurs. Présents depuis des années comme bus pour les cartes d'extensions, on va maintenant le trouver en façades des serveurs pour servir des disque flash 2,5 pouces (connecteur SF-8639) et sous la forme de câble appelés OCulink.
(EN)
PCI Express is becoming more and more present in servers. As a communication bus for extension cards since years, now it will serve 2.5 inches flash drive and through PCIe cables named OCulink.
Auteurs/Authors:
Michael Hall
Director of Technology Solutions Enabling, Data Center Group, Intel Corporation
Jonmichael Hands
Technical Program Manager, Non-Volatile Memory Solutions Group, Intel Corporation
Bare-metal, Docker Containers, and Virtualization: The Growing Choices for Cl...Odinot Stanislas
(FR)
Introduction très sympathique autour des environnements Cloud avec un focus particulier sur la virtualisation et les containers (Docker)
(ENG)
Friendly presentation about Cloud solutions with a focus on virtualization and containers (Docker).
Author: Nicholas Weaver – Principal Architect, Intel Corporation
Software Defined Storage - Open Framework and Intel® Architecture TechnologiesOdinot Stanislas
(FR)
Dans cette présentation vous aurez le plaisir d'y trouver une introduction plutôt détaillées sur la notion de "SDS Controller" qui est en résumé la couche applicative destinée à contrôler à terme toutes les technologies de stockage (SAN, NAS, stockage distribué sur disque, flash...) et chargée de les exposer aux orchestrateurs de Cloud et donc aux applications.
(ENG)
This presentation cover in detail the notion of "SDS Controller" which is in summary a software stack able to handle all storage technologies (SAN, NDA, distributed file systems on disk, flash...) and expose it to Cloud orchestrators and applications. Lots of good content.
Virtualizing the Network to enable a Software Defined Infrastructure (SDI)Odinot Stanislas
Une très intéressante présentation autour de la virtualisation des réseaux contenant des explications détaillées autour des VLAN, VXLAN, mais aussi d'NVGRE et surtout de GENEVE (Generic Network Virtualization Encapsulation) supporté pour la première fois sur la dernière carte 40 GbE d'Intel (XL710)
Intel développe une "ONP" (Open Network Platform) dit autrement un switch ouvert offrant les fonctions de base nécessaires au SDN. Si vous souhaitez connaitre le matériel utilisé, les stack logicielle exploitée et les compatibilité avec notamment les orchestrateurs, ce doc est fait pour vous.
Moving to PCI Express based SSD with NVM ExpressOdinot Stanislas
Une très bonne présentation qui introduit la technologie NVM Express qui sera à coup sure l'interface du futur (proche) des "disques" SSD. Adieu SAS et SATA, bienvenu au PCI Express dans les serveurs (et postes clients)
Intel and Siveo wrote this content which explain how their Cloud Orchestrator is working. You will learn how to configure it, benefit from automatical workload placement feature and manage multiple hypervisors transparently.
Intel IT Open Cloud - What's under the Hood and How do we Drive it?Odinot Stanislas
L'IT d'Intel fait sa révolution et s'impose d'agir comme un "Cloud Service Provider". La transformation est initiée avec au programme la mise en place d'un Cloud Fédéré, Interopérable et Open mais aussi d'un framework de maturité, du DevOps et de la prise de risque. Bref, vraiment intéressant
Configuration and Deployment Guide For Memcached on Intel® ArchitectureOdinot Stanislas
This Configuration and Deployment Guide explores designing and building a Memcached infrastructure that is scalable, reliable, manageable and secure. The guide uses experience with real-world deployments as well as data from benchmark tests. Configuration guidelines on clusters of Intel® Xeon®- and Atom™-based servers take into account differing business scenarios and inform the various tradeoffs to accommodate different Service Level Agreement (SLA) requirements and Total Cost of Ownership (TCO) objectives.
Dans ce document vous trouverez les dernières améliorations faites sur OpenStack et comment certaines technologies Intel dopent la performance et la sécurité de l'environnement Cloud. Quelques exemple avec :
Comment créer des "pool" de VM sécurisées avec possibilité de géo tagging (technologies Intel présentent dans les serveurs HP, DELL, IBM… + Folsom, Nova, Horizon, Open Attestation)
Comment doper la sécurité du nouveau module de gestion des clés d'OpenStack (technologies Intel + Barbican)
Comment benchmarker le stockage object Swift avec COSBench (qui supporte maintenant Ceph, S3 et Amplidata)
Auteurs:
Girish Gopal - Strategic Planning, Intel Corporation
Malini Bhandaru - Security Architect, Intel Corporation
Scale-out Storage on Intel® Architecture Based Platforms: Characterizing and ...Odinot Stanislas
Issue du salon orienté développeurs d'Intel (l'IDF) voici une présentation plutôt sympa sur le stockage dit "scale out" avec une présentation des différents fournisseurs de solutions (slide 6) comprenant ceux qui font du mode fichier, bloc et objet. Puis du benchmark sur certains d'entre eux dont Swift, Ceph et GlusterFS.
Big Data and Intel® Intelligent Systems Solution for Intelligent transportationOdinot Stanislas
Explications sur comment il est possible d'utiliser la puissance d'Hadoop pour analyser les vidéos des caméras présentent sur les réseaux routiers avec pour objectif d'identifier l'état du trafic, le type de véhicule en déplacement et même l'usurpation de plaques d'immatriculation.
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
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
When stars align: studies in data quality, knowledge graphs, and machine lear...
Protect Your Big Data with Intel<sup>®</sup> Xeon<sup>®</sup> Processors a..
1. Protect Your Big Data with Intel® Xeon®
Processors and Intel® Software Products
for Apache* Hadoop*
Bing Wang, Product Manager, Intel
Tianyou Li, System Architect & Engineering Manager, Intel
Haidong Xia, Cloud Security Designer, Intel
BIGS003
2. Agenda
• Big Data Security Trend
• Intel® Distribution for Apache Hadoop*
• Intel Distribution for Apache Hadoop Encryption
• Intel Distribution for Apache Hadoop Role Based
Access Control
• Summary/Call to Action
The PDF for this Session presentation is available from our
Technical Session Catalog at the end of the day at:
intel.com/go/idfsessionsBJ
URL is on top of Session Agenda Pages in Pocket Guide
2
3. Agenda
• Big Data Security Trend
• Intel® Distribution for Apache Hadoop*
• Intel Distribution for Apache Hadoop Encryption
• Intel Distribution for Apache Hadoop Role Based
Access Control
• Summary/Call to Action
3
4. Big Data Insights … New Frontier for Innovation
Billions >3000 exabytes 690% Storage
connected users and of new integrated growth
devices sharing devices & Cloud Volume
traffic Sensed data
Arrival of Skype*
Facebook*
629m
Scientific data
massive data 663m Cell Unstructured
Social data
Phones data
5.3 bn Structured
Network data
data
Hotmail* Corporate data
Yahoo* 364m
273m
Time
Traditional MPP - $50K
Dramatic Data processing
ROI costs
per terabyte
New analytics tools &
Biz info processing
products &
insights
690 percent growth in storage capacity based off Intel analysis and IDC data,
between 2010 (26,066 petabytes) to 2015 (179,327) which is ~690%
4
5. Big Data Security Concerns
Data Protection Access Control
• How to protect sensitive
• Who can access the
data:
data?
−PII, customer info, IP,
−Need granular control
credit card, …
for data access
• Regulatory and compliance
requirments
• Encryption is method BIG DATA
of choice for data
protection • No built-in access
• Encryption was control in current Big
infeasible due to Data framework
performance
overhead
5
6. Agenda
• Big Data Security Trend
• Intel® Distribution for Apache Hadoop*
• Intel Distribution for Apache Hadoop Encryption
• IDH Role Based Access Control
• Summary/Call to Action
6
7. Intel® Distribution for Apache
Hadoop* Software
This session
focus
Automatic tuning of Multi-site scalability and
Industry’s 1st hardware- Role-based access control
Hadoop* cluster adaptive replication in
assisted encryption & granular ACLs in HBase*
configuration HBase
Intel® Manager for Apache Hadoop* software
Deployment, Configuration, Monitoring, Alerts, and Security
Mahout*
Data Exchange
Sqoop* 1.4.1
Oozie* Pig* R Hive*
0.7
HBase 0.94.1
3.3.0 0.9.2 connectors 0.9.0
Columnar Store
Machine
Workflow Scripting Statistics SQL Query
ZooKeeper* 3.4.5
Learning
Coordination
YARN (MRv2)
Distributed Processing Framework
Flume* 1.3.0
Log Collector
HDFS 2.0.3
Hadoop Distributed File System
Intel proprietary Intel enhancements contributed back to open source Open source components included without change
7
8. Hadoop* Encryption: Protect Data from
“Disk Leak”
&$!@... Data I have the key
was encrypted, and passphrase,
how can I crack I can recover
it? the data via
Intel tool
8
9. Agenda
• Big Data Security Trend
• Intel® Distribution for Apache Hadoop*
• Intel Distribution for Apache Hadoop Encryption
• Intel Distribution for Apache Hadoop Role Based
Access Control
• Summary/Call to Action
9
10. Data Protection with Intel® AES-NI
Efficient Ways to Use Encryption for Data Protection
Intel® AES-NI: Data at Rest
Full disk encryption software
• 7 instructions that protects data while saving to disk
expose special Data in Motion
Secure transactions used
math functions pervasively in
ecommerce, banking, etc.
built in the
processor Internet Intranet
accelerate AES
• Makes enabled
encryption
software faster Data in Process
and stronger Most enterprise and cloud applications offer
encryption options to secure information and
protect confidentiality
10 Intel® Advanced Encryption Standard New Instructions
11. Intel® Distribution for Apache Hadoop*
Software: Encryption Framework
HDFS MapReduce
Derivative RecordReader
Decrypt
Encrypt Map
Combiner
Client
Partitioner
Local
Decrypt Merge & Sort
Reduce
Derivative
Encrypt
RecordWriter
11
11
12. Crypto Codec Framework
• Extends compression codec and establishes a
common abstraction of the API level that can be
shared by all crypto codec implementations as well
as users that use the API
CryptoCodec cryptoCodec = (CryptoCodec) ReflectionUtils.newInstance(codecClass,
conf);
CryptoContext cryptoContext = new CryptoContext();
...
cryptoCodec.setCryptoContext(cryptoContext);
CompressionInputStream input = cryptoCodec.createInputStream(inputStream);
…
• Provides a foundation for other components in
Hadoop* such as MapReduce or HBase* to support
encryption features
12
14. Crypto Codec File Format
Block Block Block Block …
Sync Block Algorithm Original Encrypted
Mark header header Size Size (4 byte)
(16 byte) (4 byte)
Encryption data …
Stream
Version Key Exten-
header Stream IV (16
(4 profile sion
length (4 header byte)
byte) header header
byte)
Encryption Data
Compressed Compressed Compressed Compressed
…
Size (4 byte) data Size (4 byte) data
14
15. Crypto Codec: API Example
The usage is aligned with compression codec but with context
supporting.
Configuration conf = new Configuration();
CryptoCodec cryptoCodec =
(CryptoCodec) ReflectionUtils.newInstance(AESCodec.class, conf);
CryptoContext cryptoContext = new CryptoContext();
cryptoContext.setKey(Key.derive(password));
cryptoCodec.setCryptoContext(cryptoContext);
DataInputStream input = inputFile.getFileSystem(conf).open(inputFile);
DataOutputStream outputStream = outputFile.getFileSystem(conf).create(outputFile);
CompressionOutputStream output = cryptoCodec.createOutputStream(outputStream);
// encrypt the stream
writeStream(input, output);
input.close();
output.close();
15
16. Crypto Codec: A Simple MapReduce
Example
The usage is aligned with compression codec usage in MapReduce
job but with context resolving.
Job job = Job.getInstance(conf, "example");
JobConf jobConf = (JobConf)job.getConfiguration();
FileMatches fileMatches = new FileMatches(
KeyContext.refer("KEY00", Key.KeyType.SYMMETRIC_KEY, "AES", 128));
fileMatches.addMatch("^.*/input1.intelaes$",
KeyContext.refer("KEY01", Key.KeyType.SYMMETRIC_KEY, "AES", 128));
String keyStoreFile = "file:///" + secureDir + "/my.keystore";
String keyStorePasswordFile = "file:///" + secureDir + "/my.keystore.passwords";
KeyProviderConfig keyProviderConfig =
KeyProviderCryptoContextProvider.getKeyStoreKeyProviderConfig(
keyStoreFile, "JCEKS", null, keyStorePasswordFile, true);
KeyProviderCryptoContextProvider.setInputCryptoContextProvider(
jobConf, fileMatches, true, keyProviderConfig);
16
17. Key Distribution and Protection for
MapReduce
• Targets
– A framework at MapReduce side for enabling crypto codec in
MapReduce job such as key context resolving, distribution
and protection
– Enabling different key storage or management systems to
plug-in for providing keys
– Satisfying the common requirements that stage and file of a
single job may use different keys
• A complete key management system is not part of
Intel® Distribution for Apache Hadoop* Software
– An API to integrate with an external key manage system is
included
17
18. Test Environment
Processor Intel® Xeon® processor E5-2690 @2.90GHz (32
core, only 1 core is used)
Software Intel® Distribution for Apache Hadoop* version
2.3
Memory 32GB
Operating System CentOS* 6.3
Encryption OpenSSL* 1.0.1c (Intel® AES-NI enabled)
Software
File System Apache Hadoop Distributed File System
(HDFS*)—namemode, datanode, and the test
program were all run on the same server
Storage 240 GB Intel® Solid-State Drive (SSD) 320 Series
Test Input 1 GB text file
Input Buffer Size 64K, 4K, 1K – data size for calling
encryption/decryption interface each time
18
19. Encryption in Memory
AES Encryption
Higher is better
500 Up to
450
400
5.3x
350
Speed(MB/s)
300
250
200
150
100
50
0
64k 4k 1k
AES-NI 460 457 454
No AES-NI 87 87 86
AES = Intel® Advanced Encryption Standard New Instructions
Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance
tests, such as SYSmark* and MobileMark*, are measured using specific computer systems, components, software, operations and functions.
19 4/10/2013
Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you
in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more
19 information go to http://www.intel.com/performance.
20. Decryption in Memory
AES-Decryption
Higher is better
1400 Up to
1200 19.8x
1000
Speed(MB/s)
800
600
400
200
0
64k 4k 1k
AES-NI 1266 1259 1253
No AES-NI 64 63 63
AES = Intel® Advanced Encryption Standard New Instructions
Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance
tests, such as SYSmark* and MobileMark*, are measured using specific computer systems, components, software, operations and functions.
20 4/10/2013
Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you
in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more
20 information go to http://www.intel.com/performance.
21. Combining Encryption with Compression
(Memory-to-HDFS Transfer)
600 Higher is better
500 489
475 468 464
436 435
400
Throughput (MB/s)
292 282
300 280
200
114 113 115
100 84 86 89
58 56 53 52 57 55 52 59 55 52 51 56 55 53 58 55 53 51 56 55 52
0
64k 4k 1k
hdfs io write aes w/ AES-NI aes w/o AES-NI
snappy + hdfs io aes + snappy w/ AES-NI aes + snappy w/o AES-NI
gzip + hdfs io aes + gzip w/ AES-NI aes + gzip w/o AES-NI
zlib + hdfs io aes + zlib w/ AES-NI aes + zlib w/o AES-NI
Up to 1.5X faster with Intel® AES-NI
Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance tests, such as
SYSmark* and MobileMark*, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors
may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including
the performance of that product when combined with other products. For more information go to http://www.intel.com/performance.
21 aes = Intel® Advanced Encryption Standard New Instructions, HDFS = Hadoop* Distributed File System
22. Combining Decryption with Decompression
(HDFS-to-Memory File Transfer)
1400 Higher is better
1287
1231
1199
1200
1104
1072 1048
1000
Throughput (MB/s)
800
661 677 661
611 635 624
600 565 566 557
466
456 476
461 471
455
410 409 417
400 365 369 367
322 324 325
299 300 299
200
57 56 56
0
64k 4k 1k
hdfs io read aes w/ AES-NI aes w/o AES-NI
snappy + hdfs io aes + snappy w/ AES-NI aes + snappy w/o AES-NI
gzip + hdfs io aes + gzip w/ AES-NI aes + gzip w/o AES-NI
zlib + hdfs io aes + zlib w/ AES-NI aes + zlib w/o AES-NI
Up to 3.3X faster with Intel® AES-NI
Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors. Performance tests, such as SYSmark*
and MobileMark*, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the
results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance
of that product when combined with other products. For more information go to http://www.intel.com/performance.
22 aes = Intel® Advanced Encryption Standard New Instructions, HDFS = Hadoop* Distributed File System
23. Where to Find the Source Code…
• Patch and design document already submit to
HADOOP-9331
• A working fork of Hadoop* with encryption
framework can be found in GitHub project
23
24. Agenda
• Big Data Security Trend
• Intel® Distribution for Apache Hadoop*
• Intel Distribution for Apache Hadoop Encryption
• Intel Distribution for Apache Hadoop Role Based
Access Control
• Summary/Call to Action
24
25. Role Based Access Control (RBAC):
Overview
Intel Manager
HDFS
Permissions
HBase*
Users
Permissions
Role
Hive*
Groups Permissions
MapReduce
Permissions
Active Directory
• User/Group & Roles will
be translated into
configuration files
• ACL configurations will
be pushed into every
required node
HDFS = Hadoop* Distributed File System
25
26. RBAC: Role Definition
• Role is a collection of permissions
• Permission can have resource parameters
• Role can be associate to users/groups
• One user/group can have multiple roles
• Currently we do not support role nesting
26
29. Beyond This…Project Rhino!
• A common authorization framework for the Hadoop*
ecosystem
• Token based authentication and single sign on
• Extend Hbase* support for ACLs to the cell level
• Improve audit logging
Please visit:
https://github.com/intel-hadoop/project-rhino/
29
30. Agenda
• Big Data Security Trend
• Intel® Distribution for Apache Hadoop*
• Intel Distribution for Apache Hadoop Encryption
• Intel Distribution for Apache Hadoop Role Based
Access Control
• Summary/Call to Action
30
31. Summary/Call to Action
• Intel® Xeon® processor based servers
provide a strong foundation for big data
workloads
• Intel® Distribution for Apache Hadoop* with
Intel Xeon processors provides breakthrough
data security and access control for big data
analytics
• Develop applications to leverage Intel
Distribution for Apache Hadoop Security
capabilities
• Deploy big data solutions with Intel
Distribution for Apache Hadoop on Intel
Xeon processor-based servers
31
32. Additional Resources
• Intel® AES-NI Website
• Intel® Distribution for Apache Hadoop* Website
• Intel AES-NI animation
• Secure Cloud with High Performing Intel® Data
Protection Technologies animation
• “The Rijndael Cipher” - an AES tutorial animation
• Shay Gueron, “Advanced Encryption Standard (AES)
Instruction Set rev 2”, Intel whitepaper, June 2009.
• Shay Gueron, Michael Kounavis, “Carry-less
multiplication and its usage for computing the GCM
Mode”, Intel whitepaper, August 2009
• Intel AES-NI use with IBM DB2 database white paper
32 Intel® Advanced Encryption Standard New Instructions (Intel® AES-NI)
34. Legal Disclaimer
• Intel® AES-NI requires a computer system with an AES-NI enabled processor, as well as non-Intel software to execute
the instructions in the correct sequence. AES-NI is available on select Intel® processors. For availability, consult your
reseller or system manufacturer. For more information, see Intel® Advanced Encryption Standard Instructions (AES-NI)
• Intel® Trusted Execution Technology (Intel® TXT): No computer system can provide absolute security under all
conditions. Intel® TXT requires a computer with Intel® Virtualization Technology, an Intel TXT enabled processor,
chipset, BIOS, Authenticated Code Modules and an Intel TXT compatible measured launched environment (MLE). Intel
TXT also requires the system to contain a TPM v1.s. For more information, visit
http://www.intel.com/technology/security.
• Intel® Virtualization Technology (Intel® VT) requires a computer system with an enabled Intel® processor, BIOS, and
virtual machine monitor (VMM). Functionality, performance or other benefits will vary depending on hardware and
software configurations. Software applications may not be compatible with all operating systems. Consult your PC
manufacturer. For more information, visit http://www.intel.com/go/virtualization.
• Software and workloads used in performance tests may have been optimized for performance only on Intel
microprocessors. Performance tests, such as SYSmark* and MobileMark*, are measured using specific computer
systems, components, software, operations and functions. Any change to any of those factors may cause the results to
vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated
purchases, including the performance of that product when combined with other products. For more information go to
http://www.intel.com/performance.
• Any software source code reprinted in this document is furnished under a software license and may only be used or
copied in accordance with the terms of that license.
• Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to
whom the Software is furnished to do so, subject to the following conditions:
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
34
35. Risk Factors
The above statements and any others in this document that refer to plans and expectations for the first quarter, the year and the
future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,”
“intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking
statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking
statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors
could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the
following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand
could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance
of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns
including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial
conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could
negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by
a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult
to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and
market acceptance of Intel's products; actions taken by Intel's competitors, including product offerings and introductions, marketing
programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological
developments and to incorporate new features into its products. The gross margin percentage could vary significantly from
expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying
products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and
associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials
or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and
intangible assets. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in
countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters,
infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and
compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intel's
products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures.
Intel’s current chief executive officer plans to retire in May 2013 and the Board of Directors is working to choose a successor. The
succession and transition process may have a direct and/or indirect effect on the business and operations of the company. In
connection with the appointment of the new CEO, the company will seek to retain our executive management team (some of whom are
being considered for the CEO position), and keep employees focused on achieving the company’s strategic goals and objectives. Intel's
results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and
by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as
the litigation and regulatory matters described in Intel's SEC reports. An unfavorable ruling could include monetary damages or an
injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting
Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed
discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most
recent Form 10-Q, report on Form 10-K and earnings release.
Rev. 1/17/13
35
37. Pillars & Challenges of Big Data
Massive scale and growth of unstructured data
80%~90% of total data
Volume Growing 10x~50x faster than structured (relational) data
10x~100x of traditional data warehousing
Heterogeneity and variable nature of Big Data
Many different forms (text, document, image, video...)
Variety No schema or weak schema
Inconsistent syntax and semantics
Real-time rather than batch-style analysis
Velocity Data streamed in, tortured, and discarded
Making impact on the spot rather than
after-the-fact
Predictive analytics for future trends and patterns
Value Deep, complex analysis (machine learning, statistic modeling,
graph algorithms…) versus
Traditional business intelligence (querying, reporting…)
37
38. HDFS File Encryption: Architecture
Overview
Key Management
Input Data Stream Output Data Stream
Encrypt/Decrypt
Encryption Codec
Buffer
Native Crypto Lib
HDFS = Hadoop* Distributed File System
38