Big Data Platform Overview

Transcription

1 Big Data Platform Overview Alex Hay Big Data CTP Meridee Lowry Big Data CTP April 30 th, 2014

2 Big Data is a Concept Big Data 2

3 IBM Big Data and Analytics Offerings Watson Foundations Data types Real-time processing & analytics STREAMS, DATA REPLICATION Actionable insight Machine and sensor data Image and video Enterprise content Transaction and application data Social data Operational systems DB2, INFORMIX PUREDATA TRANSACTIONS Exploration, landing and archive Trusted data DB2 WAREHOUSE PUREDATA OPERATIONAL ANALYTICS Deep analytics & modeling PUREDATA ANALYTICS Reporting & interactive analysis DB2 BLU PUREDATA ANALYTICS Decision management SPSS MODELER GOLD Predictive analytics and modeling SPSS MODELER Reporting, analysis, content analytics COGNOS BI COGNOS TM1 Third-party data BIGINSIGHTS PUREDATA HADOOP Discovery and exploration WATSON EXPLORER Information Integration & Governance INFORMATION SERVER, MDM, G2, GUARDIUM, OPTIM 3

4 4 Online Retailer Scenarios

5 The Big Outdoors Company Fictitious company IBM-generated data for sales Company stats 144 Products 3,500 Unstructured Data Items 5

6 6 Scenario Zero Demo

7 Scenario 1: Populate the Landing Zone Value: Process and explore data more quickly Data Sources Actionable Insights Landing, Exploration & Archive Structured Operational Explore Landed data using BigSheets BigSheets Unstructured External Social Information Movement, Matching & Transformation 7

8 8 Scenario One Demo

9 BigSheets - Spreadsheet Interface 9

10 Scenario 2: Perform Contextual Discovery Value: More quickly search all forms of data Data Sources Actionable Insights Landing, Exploration & Archive Structured Operational Unstructured Exploration & Discovery External Social 10

11 Bringing Big Data to the Point of Impact Relational Data File Systems Content Management Application/ Users Commenting Tagging Watson Explorer CRM Supply Chain ERP BIG DATA PLATFORM Hadoop System Stream Computing Rating RSS Feeds Shared Folders Cloud Social Tools Custom Sources 11 External Sources

12 Analytics Clustering 12

13 Scenario 3: Perform Text Analytics & Merge Data Value: Report and Retain Lineage from Hadoop Data Sources Actionable Insights Landing, Exploration & Archive Structured Operational Unstructured Apply Text Analytics Merge Structured w/ Unstructured Data BI & Performance Management External Social Information Movement, Matching & Transformation 13

14 14 Scenario Three Demo

15 Text Analytics Processing of Unstructured Data How it works Parses unstructured text and detects meaning with annotators Understands the context in which the text is analyzed Hundreds of pre-built annotators for names, addresses, phone numbers, and others Parts of speech support forp English, Spanish, French, German, Portuguese, Dutch, Japanese, Chinese Distills structured info from unstructured text Benefits More precise and correct answers 50% faster than manual method Run faster text analysis Unstructured text (document, , etc) Football World Cup 2010, one team distinguished themselves well, losing to the eventual champions 1-0 in the Final. Early in the second half, Netherlands striker, Arjen Robben, had a breakaway, but the keeper for Spain, Iker Casillas made the save. Winger Andres Iniesta scored for Spain for the win. Classification and Insight 15

16 Scenario 4: Enhanced Product and Consumer Data Value: 360-degree view Data Sources Actionable Insights Landing, Exploration & Archive Apply Text Analytics with SDA Enterprise Warehouse PureData for Add Operational value to Analytics existing Customer data by applying Social Profiles BI & Performance Management External Social Information Movement, Matching & Transformation 16

17 Scenario Four Demo 17

18 Scenario 5: Better decision making using Predictive Analytics Value: Predict more often, more granular using more data Data Sources Actionable Insights Predictive Analytics & Modeling Structured Operational Landing, Exploration & Archive Enterprise Warehouse PureData for Operational Analytics Unstructured External Social 18

19 Scenario 6: Real-time data mining & Text Analytics Value: Improve Customer Satisfaction & Sales Data Sources Actionable Insights Streaming Real-time (Predictions) Real-Time Analytics Real-time Text Analytics Predictive Analytics & Modeling Sensor Geospatial Time Series Structured Operational Unstructured External Social 19

20 Scenario 7: Encryption and Auditing of BigInsights Value: Improve Data Security through Governance Data Sources Actionable Insights Sensor Geospatial Time Series Structured Operational Landing, Exploration & Archive Enterprise Warehouse Analytic Appliances Data Marts BI & Performance Management Unstructured Security, Governance and Business Continuity 20

21 IBM Big Data and Analytics Offerings Watson Foundations Data types Real-time processing & analytics STREAMS, DATA REPLICATION Actionable insight Machine and sensor data Image and video Enterprise content Transaction and application data Social data Operational systems DB2, INFORMIX PUREDATA TRANSACTIONS Exploration, landing and archive Trusted data DB2 WAREHOUSE PUREDATA OPERATIONAL ANALYTICS Deep analytics & modeling PUREDATA ANALYTICS Reporting & interactive analysis DB2 BLU PUREDATA ANALYTICS Decision management SPSS MODELER GOLD Predictive analytics and modeling SPSS MODELER Reporting, analysis, content analytics COGNOS BI COGNOS TM1 Third-party data BIGINSIGHTS PUREDATA HADOOP Discovery and exploration WATSON EXPLORER Information Integration & Governance INFORMATION SERVER, MDM, G2, GUARDIUM, OPTIM 21

22 22 Thank You!