Soumettre la recherche
Mettre en ligne
Key note big data analytics ecosystem strategy
•
1 j'aime
•
3,223 vues
IBM Sverige
Suivre
Keynote: Big Data and Data Warehouse Modernization – Trends & Directions" Les King
Lire moins
Lire la suite
Présentations et discours publics
Technologie
Business
Signaler
Partager
Signaler
Partager
1 sur 46
Télécharger maintenant
Télécharger pour lire hors ligne
Recommandé
Webinar: The Anatomy of the Cloudant Data Layer
Webinar: The Anatomy of the Cloudant Data Layer
IBM Cloud Data Services
Data persistence using pouchdb and couchdb
Data persistence using pouchdb and couchdb
Dimgba Kalu
CouchDB : More Couch
CouchDB : More Couch
delagoya
DAC4B 2015 - Polybase
DAC4B 2015 - Polybase
Łukasz Grala
Analyze and visualize non-relational data with DocumentDB + Power BI
Analyze and visualize non-relational data with DocumentDB + Power BI
Sriram Hariharan
MongoDB on Azure
MongoDB on Azure
Norberto Leite
MongoDB & Hadoop - Understanding Your Big Data
MongoDB & Hadoop - Understanding Your Big Data
MongoDB
Blazing Fast Analytics with MongoDB & Spark
Blazing Fast Analytics with MongoDB & Spark
MongoDB
Contenu connexe
Tendances
Implementing and Visualizing Clickstream data with MongoDB
Implementing and Visualizing Clickstream data with MongoDB
MongoDB
Azure DocumentDB for Healthcare Integration
Azure DocumentDB for Healthcare Integration
BizTalk360
Design Patterns for Building 360-degree Views with HBase and Kiji
Design Patterns for Building 360-degree Views with HBase and Kiji
HBaseCon
Big Data Analytics with Hadoop, MongoDB and SQL Server
Big Data Analytics with Hadoop, MongoDB and SQL Server
Mark Kromer
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
Chris Schalk
MongoDB et Hadoop
MongoDB et Hadoop
MongoDB
MongoDB Evenings Dallas: What's the Scoop on MongoDB & Hadoop
MongoDB Evenings Dallas: What's the Scoop on MongoDB & Hadoop
MongoDB
MongoDB in the Big Data Landscape
MongoDB in the Big Data Landscape
MongoDB
Big Data in the Real World
Big Data in the Real World
Mark Kromer
Benefits of Using MongoDB Over RDBMSs
Benefits of Using MongoDB Over RDBMSs
MongoDB
2013 march 26_thug_etl_cdc_talking_points
2013 march 26_thug_etl_cdc_talking_points
Adam Muise
MongoDB Evenings DC: Get MEAN and Lean with Docker and Kubernetes
MongoDB Evenings DC: Get MEAN and Lean with Docker and Kubernetes
MongoDB
Introduction to Microsoft Azure HD Insight by Dattatrey Sindhol
Introduction to Microsoft Azure HD Insight by Dattatrey Sindhol
HARMAN Services
Big Data Analytics Projects - Real World with Pentaho
Big Data Analytics Projects - Real World with Pentaho
Mark Kromer
Webinar: Enterprise Data Management in the Era of MongoDB and Data Lakes
Webinar: Enterprise Data Management in the Era of MongoDB and Data Lakes
MongoDB
MongoDB + Spring
MongoDB + Spring
Norberto Leite
Hadoop Reporting and Analysis - Jaspersoft
Hadoop Reporting and Analysis - Jaspersoft
Hortonworks
Integrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle Database
Gwen (Chen) Shapira
Big Data, Bigger Brains
Big Data, Bigger Brains
Denny Lee
Hands-On: Managing Slowly Changing Dimensions Using TD Workflow
Hands-On: Managing Slowly Changing Dimensions Using TD Workflow
Treasure Data, Inc.
Tendances
(20)
Implementing and Visualizing Clickstream data with MongoDB
Implementing and Visualizing Clickstream data with MongoDB
Azure DocumentDB for Healthcare Integration
Azure DocumentDB for Healthcare Integration
Design Patterns for Building 360-degree Views with HBase and Kiji
Design Patterns for Building 360-degree Views with HBase and Kiji
Big Data Analytics with Hadoop, MongoDB and SQL Server
Big Data Analytics with Hadoop, MongoDB and SQL Server
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQuery
MongoDB et Hadoop
MongoDB et Hadoop
MongoDB Evenings Dallas: What's the Scoop on MongoDB & Hadoop
MongoDB Evenings Dallas: What's the Scoop on MongoDB & Hadoop
MongoDB in the Big Data Landscape
MongoDB in the Big Data Landscape
Big Data in the Real World
Big Data in the Real World
Benefits of Using MongoDB Over RDBMSs
Benefits of Using MongoDB Over RDBMSs
2013 march 26_thug_etl_cdc_talking_points
2013 march 26_thug_etl_cdc_talking_points
MongoDB Evenings DC: Get MEAN and Lean with Docker and Kubernetes
MongoDB Evenings DC: Get MEAN and Lean with Docker and Kubernetes
Introduction to Microsoft Azure HD Insight by Dattatrey Sindhol
Introduction to Microsoft Azure HD Insight by Dattatrey Sindhol
Big Data Analytics Projects - Real World with Pentaho
Big Data Analytics Projects - Real World with Pentaho
Webinar: Enterprise Data Management in the Era of MongoDB and Data Lakes
Webinar: Enterprise Data Management in the Era of MongoDB and Data Lakes
MongoDB + Spring
MongoDB + Spring
Hadoop Reporting and Analysis - Jaspersoft
Hadoop Reporting and Analysis - Jaspersoft
Integrated Data Warehouse with Hadoop and Oracle Database
Integrated Data Warehouse with Hadoop and Oracle Database
Big Data, Bigger Brains
Big Data, Bigger Brains
Hands-On: Managing Slowly Changing Dimensions Using TD Workflow
Hands-On: Managing Slowly Changing Dimensions Using TD Workflow
En vedette
Cloudant
Cloudant
Mansura Habiba
The Big Data Analytics Ecosystem at LinkedIn
The Big Data Analytics Ecosystem at LinkedIn
rajappaiyer
Teradata Aster: Big Data Discovery Made Easy
Teradata Aster: Big Data Discovery Made Easy
TIBCO Spotfire
Unified big data architecture
Unified big data architecture
DataWorks Summit
Teradata Unity
Teradata Unity
Teradata
Business Partner Product Enablement Roadmap, IBM Predictive Analytics
Business Partner Product Enablement Roadmap, IBM Predictive Analytics
Arrow ECS UK
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...
Data Con LA
Teradata - Presentation at Hortonworks Booth - Strata 2014
Teradata - Presentation at Hortonworks Booth - Strata 2014
Hortonworks
Teradata introduction - A basic introduction for Taradate system Architecture
Teradata introduction - A basic introduction for Taradate system Architecture
Mohammad Tahoon
Teradata - Architecture of Teradata
Teradata - Architecture of Teradata
Vibrant Technologies & Computers
Types of ecosystem
Types of ecosystem
bhanu_
En vedette
(11)
Cloudant
Cloudant
The Big Data Analytics Ecosystem at LinkedIn
The Big Data Analytics Ecosystem at LinkedIn
Teradata Aster: Big Data Discovery Made Easy
Teradata Aster: Big Data Discovery Made Easy
Unified big data architecture
Unified big data architecture
Teradata Unity
Teradata Unity
Business Partner Product Enablement Roadmap, IBM Predictive Analytics
Business Partner Product Enablement Roadmap, IBM Predictive Analytics
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...
Teradata - Presentation at Hortonworks Booth - Strata 2014
Teradata - Presentation at Hortonworks Booth - Strata 2014
Teradata introduction - A basic introduction for Taradate system Architecture
Teradata introduction - A basic introduction for Taradate system Architecture
Teradata - Architecture of Teradata
Teradata - Architecture of Teradata
Types of ecosystem
Types of ecosystem
Similaire à Key note big data analytics ecosystem strategy
Overview - IBM Big Data Platform
Overview - IBM Big Data Platform
Vikas Manoria
Building Confidence in Big Data - IBM Smarter Business 2013
Building Confidence in Big Data - IBM Smarter Business 2013
IBM Sverige
The ABCs of Big Data
The ABCs of Big Data
The Marketing Distillery
Big data
Big data
Mahmudul Alam
Ab cs of big data
Ab cs of big data
Digimark
Presentation on Big Data
Presentation on Big Data
Md. Salman Ahmed
Data mining with big data
Data mining with big data
Sandip Tipayle Patil
Future of Power: Big Data - Søren Ravn
Future of Power: Big Data - Søren Ravn
IBM Danmark
Sgcp14dunlea
Sgcp14dunlea
Justin Hayward
Big data by Mithlesh sadh
Big data by Mithlesh sadh
Mithlesh Sadh
Big data
Big data
Pooja Shah
Big data ppt
Big data ppt
Nasrin Hussain
big-data-8722-m8RQ3h1.pptx
big-data-8722-m8RQ3h1.pptx
VaishnavGhadge1
Big data ppt
Big data ppt
OECLIB Odisha Electronics Control Library
Kartikey tripathi
Kartikey tripathi
KARTIKEY TRIPATHI
Bigdatappt 140225061440-phpapp01
Bigdatappt 140225061440-phpapp01
nayanbhatia2
Big data
Big data
Mithilesh Joshi - SEO & Digital Marketing Consultant
02 a holistic approach to big data
02 a holistic approach to big data
Raul Chong
Special issues on big data
Special issues on big data
Vedanand Singh
ppt final.pptx
ppt final.pptx
kalai75
Similaire à Key note big data analytics ecosystem strategy
(20)
Overview - IBM Big Data Platform
Overview - IBM Big Data Platform
Building Confidence in Big Data - IBM Smarter Business 2013
Building Confidence in Big Data - IBM Smarter Business 2013
The ABCs of Big Data
The ABCs of Big Data
Big data
Big data
Ab cs of big data
Ab cs of big data
Presentation on Big Data
Presentation on Big Data
Data mining with big data
Data mining with big data
Future of Power: Big Data - Søren Ravn
Future of Power: Big Data - Søren Ravn
Sgcp14dunlea
Sgcp14dunlea
Big data by Mithlesh sadh
Big data by Mithlesh sadh
Big data
Big data
Big data ppt
Big data ppt
big-data-8722-m8RQ3h1.pptx
big-data-8722-m8RQ3h1.pptx
Big data ppt
Big data ppt
Kartikey tripathi
Kartikey tripathi
Bigdatappt 140225061440-phpapp01
Bigdatappt 140225061440-phpapp01
Big data
Big data
02 a holistic approach to big data
02 a holistic approach to big data
Special issues on big data
Special issues on big data
ppt final.pptx
ppt final.pptx
Plus de IBM Sverige
Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18
Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18
IBM Sverige
AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18
AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18
IBM Sverige
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar
IBM Sverige
#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion
#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion
IBM Sverige
#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM
#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM
IBM Sverige
Multiresursplanering - Karolinska Universitetssjukhuset
Multiresursplanering - Karolinska Universitetssjukhuset
IBM Sverige
Solving Challenges With 'Huge Data'
Solving Challenges With 'Huge Data'
IBM Sverige
Blockchain explored
Blockchain explored
IBM Sverige
Blockchain architected
Blockchain architected
IBM Sverige
Blockchain explained
Blockchain explained
IBM Sverige
Grow smarter project kista watson summit 2018_tommy auoja-1
Grow smarter project kista watson summit 2018_tommy auoja-1
IBM Sverige
Bemanningsplanering axfood och houston final
Bemanningsplanering axfood och houston final
IBM Sverige
Power ai nordics dcm
Power ai nordics dcm
IBM Sverige
Nvidia and ibm presentation feb18
Nvidia and ibm presentation feb18
IBM Sverige
Hwx introduction to_ibm_ai
Hwx introduction to_ibm_ai
IBM Sverige
Ac922 watson 180208 v1
Ac922 watson 180208 v1
IBM Sverige
Watson kista summit 2018 box
Watson kista summit 2018 box
IBM Sverige
Watson kista summit 2018 en bättre arbetsdag för de många människorna
Watson kista summit 2018 en bättre arbetsdag för de många människorna
IBM Sverige
Iwcs and cisco watson kista summit 2018 v2
Iwcs and cisco watson kista summit 2018 v2
IBM Sverige
Ibm intro (watson summit) bkacke
Ibm intro (watson summit) bkacke
IBM Sverige
Plus de IBM Sverige
(20)
Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18
Trender, inspirationer och visioner - Mikael Haglund #ibmbpsse18
AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18
AI – hur långt har vi kommit? – Oskar Malmström, IBM #ibmbpsse18
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar
#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion
#ibmbpsse18 - Koppla säkert & redundant till IBM Cloud - Magnus Huss, Interexion
#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM
#ibmbpsse18 - Den svenska marknaden, Andreas Lundgren, CMO, IBM
Multiresursplanering - Karolinska Universitetssjukhuset
Multiresursplanering - Karolinska Universitetssjukhuset
Solving Challenges With 'Huge Data'
Solving Challenges With 'Huge Data'
Blockchain explored
Blockchain explored
Blockchain architected
Blockchain architected
Blockchain explained
Blockchain explained
Grow smarter project kista watson summit 2018_tommy auoja-1
Grow smarter project kista watson summit 2018_tommy auoja-1
Bemanningsplanering axfood och houston final
Bemanningsplanering axfood och houston final
Power ai nordics dcm
Power ai nordics dcm
Nvidia and ibm presentation feb18
Nvidia and ibm presentation feb18
Hwx introduction to_ibm_ai
Hwx introduction to_ibm_ai
Ac922 watson 180208 v1
Ac922 watson 180208 v1
Watson kista summit 2018 box
Watson kista summit 2018 box
Watson kista summit 2018 en bättre arbetsdag för de många människorna
Watson kista summit 2018 en bättre arbetsdag för de många människorna
Iwcs and cisco watson kista summit 2018 v2
Iwcs and cisco watson kista summit 2018 v2
Ibm intro (watson summit) bkacke
Ibm intro (watson summit) bkacke
Dernier
Communication Accommodation Theory Kaylyn Benton.pptx
Communication Accommodation Theory Kaylyn Benton.pptx
kb31670
Machine learning workshop, CZU Prague 2024
Machine learning workshop, CZU Prague 2024
Gokulks007
Juan Pablo Sugiura - eCommerce Day Bolivia 2024
Juan Pablo Sugiura - eCommerce Day Bolivia 2024
eCommerce Institute
ISO 25964-1Working Group ISO/TC 46/SC 9/WG 8
ISO 25964-1Working Group ISO/TC 46/SC 9/WG 8
Access Innovations, Inc.
Burning Issue presentation of Zhazgul N. , Cycle 54
Burning Issue presentation of Zhazgul N. , Cycle 54
ZhazgulNurdinova
The Real Story Of Project Manager/Scrum Master From Where It Came?!
The Real Story Of Project Manager/Scrum Master From Where It Came?!
Loay Mohamed Ibrahim Aly
Communication Accommodation Theory Kaylyn Benton.pptx
Communication Accommodation Theory Kaylyn Benton.pptx
kb31670
Dynamics of Professional Presentationpdf
Dynamics of Professional Presentationpdf
ravleel42
Dernier
(8)
Communication Accommodation Theory Kaylyn Benton.pptx
Communication Accommodation Theory Kaylyn Benton.pptx
Machine learning workshop, CZU Prague 2024
Machine learning workshop, CZU Prague 2024
Juan Pablo Sugiura - eCommerce Day Bolivia 2024
Juan Pablo Sugiura - eCommerce Day Bolivia 2024
ISO 25964-1Working Group ISO/TC 46/SC 9/WG 8
ISO 25964-1Working Group ISO/TC 46/SC 9/WG 8
Burning Issue presentation of Zhazgul N. , Cycle 54
Burning Issue presentation of Zhazgul N. , Cycle 54
The Real Story Of Project Manager/Scrum Master From Where It Came?!
The Real Story Of Project Manager/Scrum Master From Where It Came?!
Communication Accommodation Theory Kaylyn Benton.pptx
Communication Accommodation Theory Kaylyn Benton.pptx
Dynamics of Professional Presentationpdf
Dynamics of Professional Presentationpdf
Key note big data analytics ecosystem strategy
1.
© 2013 IBM
Corporation Data Server Day – Big Data and DW Modernization Big Data Analytics Ecosystem Les King Director, Database, Analytics, Big Data Solutions May, 2014 lking@ca.ibm.com
2.
2 © 2013
IBM Corporation Agenda The Impact of Big Data in the Market Place IBM’s Analytics Portfolio Today IBM’s Analytics Vision Current Steps Towards that Vision Roadmap – A peak ahead
3.
3 © 2013
IBM Corporation 3 The Future of Analytics - Cognitive Tabulating Systems Era 1900 Cognitive Systems Era 2011 Programmable Systems Era 1950
4.
4 © 2013
IBM Corporation 2 years ago, Watson’s advanced analytic capabilities could sort through the equivalent of 200 million pages of data to uncover an answer in 3 SECONDS and would fill up this entire room. Today…Watson is now 24x faster and has gone from the size of a master bedroom to three stacked pizza boxes.
5.
5 © 2013
IBM Corporation 2 years ago, Watson’s advanced analytic capabilities could sort through the equivalent of 200 million pages of data to uncover an answer in 3 SECONDS and would fill up this entire room. Today…Watson is now 24x faster and has gone from the size of a master bedroom to three stacked pizza boxes. Watson refers to a set of solutions for the era of Cognitive Analytics
6.
6 © 2013
IBM Corporation Real time analytics is not only about reducing the latency between what flows through your transaction systems and when it lands in your data warehouse so you can perform analytics …. It is about real time activities performed by people ( your customers – and – potential customers ) through non-traditional sources ( facebook, tweets ) and being able to react to that immediately to capture an opportunity.
7.
7 © 2013
IBM Corporation Today’s organizations are facing many disruptive forces The ability to exploit big data Creating the need for organizations to understand and anticipate customer behavior and needs based on customer insights across all channels Creating new opportunities to capture meaningful information from new varieties of data and content coming at organizations in huge volumes and at accelerated velocity Creating the need for all parts of the organization to optimize all of their processes to create new opportunities, to mitigate risk, and to increase efficiency 3The shift of power to the consumer1 Accelerating pressure to do more with less 2
8.
8 © 2013
IBM Corporation Data AVAILABLE to an organization Data an organization can PROCESS The Big Data Conundrum The percentage of available data an enterprise can analyze is decreasing proportionately to the available to that enterprise Quite simply, this means as enterprises, we are getting “more naive” about our business over time
9.
9 © 2013
IBM Corporation Transactional & Application Data Sensor Data Social Data Enterprise Content • Volume • Structured • Throughput • Variety • Unstructured • Volume • Variety • Unstructured • Veracity • Velocity • Structured • Ingestion Big Data is all data and all paradigms for extracting value
10.
10 © 2013
IBM Corporation Breakthrough Analytics for All Data IBM’s capabilities span all dimensions of Big Data VelocityVolume • Build fast, accurate models on petabytes of data • New automated discovery techniques to understand what’s important in large volumes of data • Perform analytics where the data is for fast performance 10 Velocity Variety Veracity •Score models for immediate impact using streaming data •React in real time by embedding predictive models into apps •Establish alerts with visual context to understand what’s happening right now •Analyze social media to understand what’s being said about your business •Use natural language processing and sentiment analysis to process text data and extract key concepts •Analyze sensor data (Internet of Things) to improve business process & reduce costs •Uncover relationships among diverse entities to get a more accurate view of your entities •Discover relationships among social networks and predict their behavior •Prepare data for accurate models with sophisticated techniques Data in Many FormsData in MotionData at Scale Data in Doubt
11.
11 © 2013
IBM Corporation Does the Era of Big Data Signify the End of the Data Warehouse? NO! “Instead they [organizations] are moving towards multiple systems, including content management, data warehouses, data marts and specialized file systems tied together with data services and metadata, which will become the "logical" enterprise data warehouse.” Andrew Foo, Senior IT Architect Smarter Planet Solutions Team - “Big data brings new life to the data warehouse by enriching it and introducing new insights taken from non-traditional sources, as well as unexplored data sources. The integration of big data and traditional data warehousing can produce results that are the best of both worlds.” Top 10 Strategic Technology Trends for 2013
12.
12 © 2013
IBM Corporation In the era of Big Data… Different data workloads require different data systems Real Time Fraud Detection Sales AnalysisE-commerce Demand Analysis Transaction Processing Reporting and Analytics Operational Analytics Sensor Data Analysis Analytics Data Warehouse Transactional Database Operational Data Warehouse Mobile Data Serving JSON Database Mobile Storefront Time Series Database Data series 2Meter 2 Data series 1Meter 1 JSON doc 2Key 2 JSON doc 1Key 1 Key 2
13.
3 © 2013
IBM Corporation 3 The Future of Analytics - Cognitive Tabulating Systems Era 1900 Cognitive Systems Era 2011 Programmable Systems Era 1950
14.
3 © 2013
IBM Corporation 3 The Future of Analytics - Cognitive Tabulating Systems Era 1900 Cognitive Systems Era 2011 Programmable Systems Era 1950
15.
15 © 2013
IBM Corporation So …. What’s the current challenge ? 1. Era of Cognitive Analytics 2. Quantum leap in ability to store and work with mass amounts of information 3. Real-time analytics which includes ANY data source and ANY type of data 4. Infusion of new volumes, veracity, velocity and variety of data 5. “Power” moving to the consumer 6. “Fit for Purpose” solutions are required 7. Companies have “history” – an established ecosystem – which cannot be ignored And to top it off …… In order to stay competitive, companies need the ability to exploit this while dealing with reduced expense budgets
16.
16 © 2013
IBM Corporation Committed to Client Success IBM understands all kinds of data • Game-Changing Innovation – such as Watson, BLU acceleration, streaming analytics and expert integrated systems; 20 years of patent leadership • Business-Ready Capabilities – big data and analytics capabilities, integrated and hardened for serious use, with flexible deployment options IBM knows how to turn data into value • Client Expertise – deep industry know-how and solutions with global reach • Strong Ecosystem – growing investment with 360+ business partners & 100+ universities • Build on Current Investments – enhance existing analytics and information infrastructure with unparalleled breadth and depth of new capabilities IBM has invested in big data and analytics • $17B+ in Acquisitions – coupled with game-changing innovation since 2005 • Analytics Solution Centers – visited by 4000+ organizations accessing global expertise
17.
17 © 2013
IBM Corporation IBM’s POV on Big Data & Analytics Build a culture that infuses analytics everywhere. Be proactive about privacy, security and governance. Invest in a Big Data & Analytics platform. 1. 2. 3.
18.
18 © 2013
IBM Corporation IBM’s Key Platform Capabilities Accelerators Information Integration & Governance Data Warehouse Stream Computing Hadoop System DiscoveryApplication Development Systems Management BIG DATA PLATFORM PureData for Analytics & DB2 with BLU Acceleration Delivers deep insight with advanced database analytics & operational analytics Information Integration and Governance Govern data quality and manage the information lifecycle Accelerators Speed time to value with analytic and application accelerators InfoSphere Data Explorer Find, navigate, visualize all data InfoSphere BigInsights Bringing Hadoop to the enterprise InfoSphere Streams Analytics for data in-motion exploration
19.
19 © 2013
IBM Corporation IBM’s Key Platform Capabilities Accelerators Information Integration & Governance Data Warehouse Stream Computing Hadoop System DiscoveryApplication Development Systems Management BIG DATA PLATFORM “IBM has the deepest Hadoop platform and application portfolio.” –The Forrester Wave™: 1Q12 “IBM InfoSphere BigInsights is a core capability of the most comprehensive Big Data analytics platforms out there right now…” – Krishna RoyLars “Mark Leader IBM offers by far the largest product and services portfolio by both breadth and depth most…” – Jeff Kelly, IBM is The Undisputed Leader in Big Data Market
20.
20 © 2013
IBM Corporation IBM Netezza’s Market-Leading Evolution World’s First Data Warehouse Appliance World’s First 100 TB Data Warehouse Appliance World’s First Petabyte Data Warehouse Appliance World’s First Analytic Data Warehouse Appliance NPS® 8000 Series TwinFin™ with i- Class™ Advanced Analytics NPS® 10000 Series TwinFin™ 2003 2006 2009 2010 2011 2013 World’s fastest and “greenest” analytical platform Striper Simplicity Time to Value Extreme Performance Built-in analytic capabilities
21.
21 © 2013
IBM Corporation IBM DB2’s Market-Leading Evolution Software Innovations in Warehousing Prescriptive Best Practices for WH environments Broader Software capabilities and tighter h/w integration Integrated purchase process; bundled support & services Data Partitioning Feature ( DPF ), Optimization for mixed workloads IBM Smart Analytic System ( ISAS ) InfoSphere Balanced Warehouse ( IBW ) 2003 2006 2009 2010 2011 2013 PureSystems branding; single admin; single PID PureData for Operational Analytics ( PDOA ) MDC, Autonomics, Simplified Admin, pureXML, Cubing Services, Mining BLU Acceleration leveraging columnar and “in-memory”, NOSQL, Big Data Multi-temperature storage; Real-time Warehousing, WLM, Temporal Analytics Range Partitioning, Active Warehousing, Compression, Cognos, ETL Balanced Configuration Unit ( BCU ) Mixed Workloads Operational Analytics Extreme Performance Oracle Application Compatibility NOSQL
22.
22 © 2013
IBM Corporation BLU Acceleration for Cloud - >90% of OLTP systems have reporting running on them >50% of OLTP systems have analytics running on them Address the demands of these “mixed workload” environments DB2 for z/OS Informix Informix Informix Warehouse Accelerator Appliances PureData for Analytics powered by Netezza PureData for Operational Analytics Leveraging DB2 Software DB2 with BLU Acceleration Customized Software Multi-tenancy Virtual Environments Accelerators Cloud Analytics Platforms and Analytic Accelerators
23.
23 © 2013
IBM Corporation© 2013 IBM Corporation BigInsights Enterprise Edition Components IBMOpen Source Visualization & Discovery Integration Workload Optimization Streams Netezza Flume DB2 DataStage IBM InfoSphere BigInsights Runtime Advanced Analytic Engines File System MapReduce HDFS Data Store HBase Text Processing Engine & Extractor Library (AQL+HIL) BigSheets JDBC Applications & Development Text Analytics MapReduce Pig & Jaql Hive Administration Index Splittable Text Compression Enhanced Security Flexible SchedulerJaql Pig ZooKeeper Lucene Oozie Adaptive MapReduce Hive Integrated Installer Admin Console Sqoop Adaptive Algorithms Dashboard & Visualization Apps Workflow Monitoring Management HCatalog Security Audit & History Lineage R Guardium Platform Computing Cognos GPFS
24.
24 © 2013
IBM Corporation Enterprise Integration With Multiple Products Brings the Power of the Big Data Platform to BigInsights © 2013 IBM Corporation IBM InfoSphere Data Explorer Indexing and “on the glass” integration InfoSphere Streams Enables real-time, continuous analysis of data on the fly InfoSphere Guardium Auditing + Governance BigSQL Standard SQL query to data in Hadoop, Hive, or HBase Cognos Business Intelligence Support for Hive; Business Intelligence capabilities InfoSphere BigInsights Administration & Security Workload Optimization Connectors Advanced Engines Visualization & Exploration Development Tools Open source Hadoop components InfoSphere DataStage ETL Directly Into Hadoop without Map Reduce Platform Computing High performance, low- latency platform computing grid – Min 3X Perf Increase R (BigR in 2014) Application that allows users to execute R jobs directly from BigInsights web console DB2 and JDBC High speed parallel read-write for DB2 and JDBC connectivity WebSphere WAS 8.5 Liberty Profile – high performance secure REST access Rational & Data Studio RAD, Rational Team Concert & Data Studio collaborative development integration
25.
4 © 2013
IBM Corporation 2 years ago, Watson’s advanced analytic capabilities could sort through the equivalent of 200 million pages of data to uncover an answer in 3 SECONDS and would fill up this entire room. Today…Watson is now 24x faster and has gone from the size of a master bedroom to three stacked pizza boxes.
26.
26 © 2013
IBM Corporation Automobile and Manufacturing Quality Control and Customer Satisfaction In-flexibility and scalability limitations of existing IT solutions has been a inhibitor to competitive advantage. A new solution is needed to improve customer insights, quality and operational efficiency • Inventory control of parts • Manufacturing equipment and assembly line data •Warranty and services data from dealers •Telemetry data from vehicles •Customer services and social media data Next generation of Enterprise Data Warehouse: •Data landing zone and analytic zone for 5- 10 years of data •Warehouse reporting zone for high performance reports
27.
27 © 2013
IBM Corporation Constant Contact Transforming Email Marketing Campaign Effectiveness with IBM Big Data Capabilities • InfoSphere BigInsights, IBM PureData for Analytics – powered by Netezza technology, Cognos BI Need • Analyze 35 billion annual emails to guide customers on best dates & times to send emails for maximum response Benefits • 40 times improvement in analysis performance • 15-25% performance increase in customer email campaigns • Analysis time reduced from hours to seconds
28.
28 © 2013
IBM Corporation 28 Large European University generates own energy and uses analytics to monitor and manage consumption Need • After years of 8-digit electric bills, the university deployed an independent on- campus power generation system. But they lacked a solution to monitor, analyze, and manage production and consumption, Benefits • Anticipate lower energy consumption levels and costs • Ability to identify energy inefficient areas of campus and take corrective action • Improved understanding of how changes in power grid model affect energy efficiency Capabilities Utilized: Cognos BI, SPSS InfoSphere BigInsights InfoSphere Warehouse Tivoli Energy Management
29.
29 © 2013
IBM Corporation What’s the Vision ?
30.
30 © 2013
IBM Corporation The Next Generation Architecture for Big Data Where do we go next? The next generation architecture vision includes: Intelligent data provisioning across the ecosystem Seamless access to all data for applications Metadata asset catalog management Applications and analytics portability In-memory systems with BLU Acceleration Customer deployment options: cloud, software, and appliance Dynamic all data governance Enterprise security for all data Intelligent life-cycle management
31.
31 © 2013
IBM Corporation Information Integration & Governance Logical Data WarehouseLogical Data Warehouse Exploration, landing and archive Trusted data Reporting & interactive analysis Deep analytics & modeling Data types Real-time processing & analytics Transaction and application data Machine and sensor data Enterprise content Social data Image and video Third-party data Operational systems Actionable insight Decision management Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration The Logical Data Warehouse Leverage fit for purpose components and zones Advanced Application Capabilities Vertical Industry Accelerators
32.
32 © 2013
IBM Corporation What steps have already been taken ?
33.
33 © 2013
IBM Corporation Delivered as a cloud service, Cloudant eliminates complexity by enabling developers of fast-growing web and mobile apps to focus on developing their applications without the need to manage database infrastructure or growth Delivered as a cloud service, Cloudant eliminates complexity by enabling developers of fast-growing web and mobile apps to focus on developing their applications without the need to manage database infrastructure or growth Provides a NoSQL data layer delivered as a managed service Stores data of any structure as self-describing JSON documents Unique clustering framework that achieves elastic scalability that can span multiple racks, data centers, cloud providers or devices Provides multi-master replication that allows read and write to any replica and offline mobile app usage plus mobile replication & sync for occasionally connected apps Global data distribution and geo-load balancing provide high availability and enhanced performance for applications that require data to be located close to the user Provides full-text search, geo-location services, and flexible, real-time indexing Integrates via a RESTful API Monitored and managed 24x7 by the big data experts at Cloudant Based on open standards including– Apache CouchDB, Apache Lucene, GeoJSON and others Provides a NoSQL data layer delivered as a managed service Stores data of any structure as self-describing JSON documents Unique clustering framework that achieves elastic scalability that can span multiple racks, data centers, cloud providers or devices Provides multi-master replication that allows read and write to any replica and offline mobile app usage plus mobile replication & sync for occasionally connected apps Global data distribution and geo-load balancing provide high availability and enhanced performance for applications that require data to be located close to the user Provides full-text search, geo-location services, and flexible, real-time indexing Integrates via a RESTful API Monitored and managed 24x7 by the big data experts at Cloudant Based on open standards including– Apache CouchDB, Apache Lucene, GeoJSON and others Summary
34.
34 © 2013
IBM Corporation DB2 with BLU Acceleration DB2 with BLU Acceleration – In-memory columnar data store – Orders of magnitude improvement for • Consumability • Speed • Storage savings BLU Acceleration is breakthrough technology – Combines and extends proven relational technology with in-memory – Over 25 patents filed and pending – Leveraging years of IBM R&D spanning 10 laboratories in 7 countries worldwide Typical experience – Simple to implement and use – Average of 37X performance gains – Greater than 10X compression gains DB210.5 Super analytics Super easy DB2WITH BLU ACCELERATION DB2WITH BLU ACCELERATION
35.
35 © 2013
IBM Corporation Super Fast, Super Easy — Create, Load and Go! No Indexes, No Aggregates, No Tuning, No SQL changes, No schema changes IBM Research & Development Lab InnovationsIBM Research & Development Lab Innovations BLU Acceleration
36.
36 © 2013
IBM Corporation Offerings and Deployment Models Pure Systems Cloud Software Pure Application System IBM Business Intelligence Pattern with BLU Acceleration IBM DB2 Data Mart with BLU Acceleration BLU Acceleration for the Cloud Pay by the hour for 1TB or 10TB Use your credit card Bring your own license DB2 10.5 Advanced Workgroup Advanced Enterprise Cognos BI 10.2 DB2 10.5 Advanced Editions include 5 user licenses of Cognos Application Platform Delivering Platform Services
37.
37 © 2013
IBM Corporation “The BLU Acceleration technology has some obvious benefits: It makes our analytical queries run 4-15x faster and decreases the size of our tables by a factor of 10x. But it’s when I think about all the things I don't have to do with BLU, it made me appreciate the technology even more: no tuning, no partitioning, no indexes, no aggregates.” —Tom DeJuneas, IT Team Manager, Coca- Cola Bottling Co. Consolidated “ ”
38.
38 © 2013
IBM Corporation NOSQL - Ready for Big Data Curt Cotner 2012 FerrariownsCar Curt Cotner 123 Maple Ave, ChicagoownsHouse Curt Cotner 2001 ThunderjetownsBoat DB 2 J S O N Big Data Analytics SocialMobileCloud137.343 38.825 0 20 40 60 80 100 120 140 160 Jena TDB DB2 Graph Store Seconds Emergence of a growing number of non-relational, distributed data stores for massive scale data { "firstName": "John", "lastName" : "Smith", "age" : 25, "address" : { "streetAddress": "21 2nd Street", "city" : "New York", "state" : "NY", "postalCode" : "10021" }, "phoneNumber": [ { "type" : "home", "number": "212 555-1234" }, { "type" : "fax", "number": "646 555-4567" } ] }
39.
39 © 2013
IBM Corporation NOSQL – Why does it matter ? Combine data from systems of engagement with traditional data in same DB2 database – Best of both worlds – Simplicity and agility of XML, RDF, JSON + enterprise strengths of DB2 Store data from web/mobile apps in it's native form – Developers don’t have to learn anything new – XQuery, SPARQL, Mongo API, …. No new business processes to worry about – Security, Audit – Data Life Cycle Management – Backup and Recoverability – Resilience – High Availability and Disaster Recovery DB 2 J S O N Big Data Analytics SocialMobileCloud
40.
40 © 2013
IBM Corporation What is BigInsights BigSQL Using rich standard SQL – Comprehensive SQL '92+ support (datatypes) SQL access to all data stored in BigInsights – Multiple Sources Via JDBC/ODBC Leveraging Map/Reduce for Parallelism OR Direct for Low- Latency Queries – Big SQL utilizes direct access or MapReduce: In direct access, users can run smaller, point queries, like HBase queries for example, that will execute quickly. For bigger complex queries on larger data sets, the parallelism of MapReduce is used to process the data. Scalable server architecture Data Sources Hive Tables HBase Tables CSV Files BigSQL Engine BigInsights Application SQL Language JDBC / ODBC Driver JDBC / ODBC Server
41.
41 © 2013
IBM Corporation Big SQL 3.0– Features at a Glance Available for POWER Linux (Redhat) and Intel x64 Linux (Redhat/SUSE)
42.
5 © 2013
IBM Corporation 2 years ago, Watson’s advanced analytic capabilities could sort through the equivalent of 200 million pages of data to uncover an answer in 3 SECONDS and would fill up this entire room. Today…Watson is now 24x faster and has gone from the size of a master bedroom to three stacked pizza boxes. Watson refers to a set of solutions for the era of Cognitive Analytics
43.
43 © 2013
IBM Corporation Total respondents n = 1061 Big data objectives Top functional objectives identified by organizations with active big data pilots or implementations. Responses have been weighted and aggregated. Customer-centric outcomes Operational optimization Risk / financial management New business model Employee collaboration Big Data Requires Ability to Match Customer Information Trends More than 50% of Big Data analytics projects are “customer-centric” Integrating data increases the ability to create a complete picture of today’s ‘empowered consumer’ However Clients today struggle to link this customer information, hand-coding & repeatedly tweaking algorithms Solution IBM BigMatch for BigInsights
44.
44 © 2013
IBM Corporation C. Johnson 123 Main Street 512-545-1234 CRM Supply Chain Fulfillment Support Ticketing External Sources 3rd Party Chris Johnston 123 Main Street 512-554-1234 Shipping: 456 Pine Ave Christine. Johnson 123 Main Street Call length Semi-structured notes Satisfaction C. Johnson Main Street 512-554-1234 C. Johnson 125 Main Street 512-554-1234 ChrisJohnson65 “Likes” Clothes, Camping Gear @ChristyJohnson65 Christy65 Circle / Network data Order Mgmt. Internal / Structured External / Unstructured Web Chris.johnson@cj.net BigMatch provides The Ultimate Customer Dimension for Analytics at Hadoop Scale Big Match matches all these records Big Match combines the MDM probabilistic matching engine & pre-built algorithms & BigInsights for customer matching natively within Hadoop Increased Value of Customer only if… Christine Johnson Married 1 child 4/15/74 Christy65 Mail Order responder Specialty Apparel Partner Sales data VIP: Gold Customer Sat: 80% Influence Score: 8/10
45.
45 © 2013
IBM Corporation Match and Search Differentiators – Fuzzy Matching IBM’s library of fuzzy matching techniques is the most comprehensive. Fuzzy matches are then scored against probabilistic weights based on value frequencies in your data Nov 6, Phonetics Mohammed vs. Mahmoud Synonyms Andrew = Andy George = Jorge 1st = First Abbreviations AIG = American International Group Road = Rd Concatenation Van de Velde = Vandevelde Misalignment Kim Jung-il = Kim il Jung Edit Distance 867-5309 ~ 876- 5309 Transliteration Toyota = トヨダ Date Similarity 01/01/1973 ~ 01/02/1973 Proximity Geocodes and great-circle distance Noise Words Initiate Inc. = Initiate Typographical Errors John Smith vs. John Snith
46.
© 2013 IBM
Corporation Thank You Les King Director, Database, Analytics, Big Data Solutions May, 2014 lking@ca.ibm.com
Télécharger maintenant