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2 Daniel Sánchez Fernández Big Client Technical Professional 16 de Junio de 2014 Big Arquitectura de Referencia

3 Cambios de paradigma habilitados por Big data y Analytics BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH All available information All Analyzed available information analyzed Small amount of carefully organized Large information amount of messy information Analyze Analyze small subsets of all information Aprovechar más los datos capturados Carefully Analyze cleanse information information as is, before cleanse any as analysis needed Reducir el esfuerzo necesario BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH Hypothesis Exploration Question Analysis Repository Analysis Insight Answer Insight Correlation Insight 3 Start Explore with hypothesis all data and and test identify against correlations selected data Los datos lideran el proceso y algunas correlaciones serán suficientes Analyze Analyze data data afterin it s motion been processed as it s and landed generated, in a warehouse in real-time or mart Aprovechar los datos en tiempo real

should why you do Predecir y decidir la siguiente mejor acción the embedded realm of the in everything

DATA TRADITIONAL & ANALYTICS APPROACH APPROACH Learn Pre-programmed to sense and predict analysis using all

4 Cambios de paradigma habilitados por Big data y Analytics BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH What What will happen has happened and what and should why you do Predecir y decidir la siguiente mejor acción the embedded realm of the in everything specialist Analítica intuitiva para todo el mundo BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH BIG DATA TRADITIONAL & ANALYTICS APPROACH APPROACH Learn Pre-programmed to sense and predict analysis using all on types structured of information data Computación Cognitiva Scheduled Real-time Analítica cómo y cuándo lo necesite 4

5 Traditional Landscape Recap Focus on delivering application independent, trusted insight to the business 3 rd Party Applications Information Ingestion Analytics Sources Information Interaction Shared Operational Information Governing Systems Security and Business Continuity Management 5

6 Traditional Landscape Recap Analytical Sources (Zones View) Focus on delivering application independent, trusted insight to the business Traditional Sources Information Interaction 3 rd Party Information Ingestion Analytics Sources Warehouse Zone Visualization, Mining & Exploration Applications Landing Area Zones Exploration Zone (Discovery Area) Integrated Warehouse & Marts Zone (Guided Analytical Area) User Reports & Dashboards Accelerators & Application Frameworks Shared Operational Information Master Reference Hubs Hubs Content Repository User Guided Applications & Advanced Analytics Governing Systems Security and Business Continuity Management 6

7 Information zones in the new information landscape Sensors producing readings Internet Social Media Services Customer Conversations Unstructured Public and Internal Sources New Sources Information Ingestion 1 Landing Area Zone Information Interaction Solutions Information Access Analytics Sources Shared Analytics Information Zone Deep Zone Integrated Warehouse & Marts Zone Exploration Zone Information Delivery Zone Interactive Analytics, Exploration and Discovery Planning, Optimization, Alerting and Reporting Visualization, Collaboration and Storytelling Real-time Decision Management Systems of Engagement Back office Applications Shared Operational Information Zone 6 Accelerators and Development Frameworks Event Detection and Action Information Governance, Risk and Compliance 7 Security and Business Continuity Management

8 Systems supporting the new information landscape Sensors producing readings Internet Social Media Services Customer Conversations Unstructured Public and Internal Sources New Sources Information Ingestion 1 Streams, Analytics and other Provisioning Engines Information Interaction Solutions Map-Reduce Processing (Hadoop) Information Access Analytics Sources 2 Warehouse Files 3 4 base 5 Marts and Cubes Files Provisioning Engines Cache Interactive Analytics, Exploration and Discovery Planning, Optimization, Alerting and Reporting Visualization, Collaboration and Storytelling Real-time Decision Management Systems of Engagement Back office Applications Master Hub 6 Reference Hub 7 Content Hub Activity Hub Analytics Hub bas e 15 Accelerators and Development Frameworks Event Detection and Action Information Governance, Risk and Compliance 8 Security and Business Continuity Management

9 Big Analytical Zones Schema Areas Exploration Zone Landing Area Sandbox Areas Exploration Visualization, Mining & Exploration Scientists (Raw, Years) Self-Provisioning (Mixture: Raw & Modeled) Prediction (More Refined ) Analytical or Predictive Models Landing Zone (Modeled, Years) System of Record (Y) (ELT) Delta Deep Zone (Months) System of Record (M) (Years) Aggregates (Years) Summary Aggregates (Years) Dimensional Integrated Warehouse & Marts Zone User Reports & Dashboards User Guided & Advanced Analytics Subject Users 9

10 Flows across the Big Reference Architecture Sandbox Areas Exploration Visualization, Mining & Exploration Scientists Self Provisioning Analytical or Predictive Models Prediction Subject Users User Reports & Dashboards User Guided & Advanced Analytics Landing Area System of Record Delta System of Record Aggregates Summary Aggregates Dimensional Legend Hadoop store RDBMS Store Hybrid Store Patterns discovered by the Scientists guide the design of the overall flow to the Subject Users Raw somewhat Modelled Modelled Aggregate Calculated 10

11 Overall Flows from all sources Experimental Sources Sandbox Areas Exploration Visualization, Mining & Exploration Scientists Self Provisioning Analytical or Predictive Models Prediction New Sources User Reports & Dashboards User Guided & Advanced Analytics Subject Users Landing Area System of Record Delta System of Record Aggregates Summary Aggregates Dimensional Legend Hadoop store RDBMS Store Hybrid Store Legacy Sources 11 New Sources may be supplemented with data from Legacy sources as well as with data from Experimental Sources Raw somewhat Modelled Modelled Aggregate Calculated

12 Federated Search and Exploration Sandbox Areas Exploration Visualization, Mining & Exploration Scientists Self Provisioning Analytical or Predictive Models Prediction Subject Users User Reports & Dashboards User Guided & Advanced Analytics Landing Area System of Record Delta System of Record Aggregates Summary Aggregates Dimensional Both Scientists and Subject Users can carry out Federated search across the landscape to supplement the pre-defined data flows 12

13 Information Architecture for a New Era of Computing A high level description of the Big Reference Architecture 13

14 DEMO: Micro-Segmentación y Correlación en Tiempo Real para redes sociales Social Media InfoSphere Streams Ingest & prep. Text Analytics: Timely Insights Online Flow: -in-motion analysis Entity Predictive Analytics: Analytics: Profile Action Resolution Determination Timely Decisions Social Media Text Analytics InfoSphere BigInsights Entity Analytics and Integration Comprehensive Social Media Customer profiles Predictive Analytics Customer Models Offline Flow: -at-rest analysis 14

15 IBM Software España IBM España Encuentro de Software IBM Software España 15

16 Legal Disclaimer IBM Corporation All Rights Reserved. The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. If the text contains performance statistics or references to benchmarks, insert the following language; otherwise delete: Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. 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Extend the Value of Your Data Warehouse with Big Data

Rick Clements Director, Marketing, Big Data, IBM Software Group, Information Management Extend the Value of Your Data Warehouse with Big Data You are likely familiar with the traditional warehouse infrastructure