Oracle's BigData solutions

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Oracle's BigData solutions consist of a number of new products and solutions to support customers looking to gain maximum business value from data sets such as weblogs, social media feeds, smart meters, sensors and other devices that generate massive volumes of data (commonly defined as ‘Big Data’) that isn’t readily accessible in enterprise data warehouses and business intelligence applications today.

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  • While it could never be described as a sleepy business since there have been several profound changes in the course of its evolution, it doesn’t really take an industry pundit to observe that the current Analytics market is marked by an accelerating pace of change. Comparable changes taking place now in a matter of a few years took decades to play out in the early days of BI and EPM. So, in the 80s we saw database reporting tools rule the roost. And most applications shipped with some sort of hardwired reporting capabilities built in, providing visibility but no subsequent interactivity. You could get your first question answered really well. But if you had a follow-on question, you were out of luckCome the 90s and most BI platforms evolved to 3 tier architectures, supporting more users and subject area specific data marts and BI environments for functional areas such as marketing, sales and supply chain.The broad-based adoption of the internet saw BI tools in the 2000s increase their footprint to be true analytical platforms deployed on enterprise data warehouses. These data warehouses supported the decision support needs of all users of an extended enterprise with capabilities that spanned production reporting to highly interactive ad hoc analysisBig changes, no doubt, but played out over a 20+ year time horizon. In the last 2-3 years, though, we are seeing technology disruptions opening up new possibilities in Analytics at a pace that is nothing short of breathtaking:-There is an explosion of business relevant data now on the internet. It is incredibly varied, generated at great velocity and already enormous in volume. How will it be analyzed?-Apple and others have revolutionized the tablet as an internet and general content consumption device that is now well ensconced with corporations, certainly at the highest echelons. What will analytics on these smaller and intensely personal devices come to mean?-The real cost of in-memory technology has declined dramatically. What transformative power could this hold for companies looking to live – and win – “in the moment”?-The maturity and consequent acceptance of the cloud has introduced a low friction delivery model for software delivered as a service to enterprises. How will Analytics be transformed, or how might it transform, the Cloud?These dramatic changes are sweeping through the enterprise computing landscape now. They each come with their own set of challenges but for those who view them, instead , as opportunities, we believe that tremendous competitive advantage can be unlocked. And we believe that Oracle Business Analytics provides you with the tools to do just that.
  • While it could never be described as a sleepy business since there have been several profound changes in the course of its evolution, it doesn’t really take an industry pundit to observe that the current Analytics market is marked by an accelerating pace of change. Comparable changes taking place now in a matter of a few years took decades to play out in the early days of BI and EPM. So, in the 80s we saw database reporting tools rule the roost. And most applications shipped with some sort of hardwired reporting capabilities built in, providing visibility but no subsequent interactivity. You could get your first question answered really well. But if you had a follow-on question, you were out of luckCome the 90s and most BI platforms evolved to 3 tier architectures, supporting more users and subject area specific data marts and BI environments for functional areas such as marketing, sales and supply chain.The broad-based adoption of the internet saw BI tools in the 2000s increase their footprint to be true analytical platforms deployed on enterprise data warehouses. These data warehouses supported the decision support needs of all users of an extended enterprise with capabilities that spanned production reporting to highly interactive ad hoc analysisBig changes, no doubt, but played out over a 20+ year time horizon. In the last 2-3 years, though, we are seeing technology disruptions opening up new possibilities in Analytics at a pace that is nothing short of breathtaking:-There is an explosion of business relevant data now on the internet. It is incredibly varied, generated at great velocity and already enormous in volume. How will it be analyzed?-Apple and others have revolutionized the tablet as an internet and general content consumption device that is now well ensconced with corporations, certainly at the highest echelons. What will analytics on these smaller and intensely personal devices come to mean?-The real cost of in-memory technology has declined dramatically. What transformative power could this hold for companies looking to live – and win – “in the moment”?-The maturity and consequent acceptance of the cloud has introduced a low friction delivery model for software delivered as a service to enterprises. How will Analytics be transformed, or how might it transform, the Cloud?These dramatic changes are sweeping through the enterprise computing landscape now. They each come with their own set of challenges but for those who view them, instead , as opportunities, we believe that tremendous competitive advantage can be unlocked. And we believe that Oracle Business Analytics provides you with the tools to do just that.
  • Enables Map-Reduce style R calculations with the Big Data Appliance and HDFSSupports compute-intensive parallelism for simulationsORCH provides optimized R algorithms that are robust, numerically accurate and linearly scalable on Hadoop and the Big Data Appliance. More cores achieve a proportional decrease in run times and matches R user experience.Linear Models and Logistic ModelsGeneral feed-forward Neural Networks Regression ModelsMatrix Factorization (algorithms for large-scale Matrix problems)K-Means ClusteringPCA (Principal Component Analysis)Correlations
  • Oracle's BigData solutions

    1. 1. Big Data – The New Information Luis Campos Big Data Solutions Director, Oracle EMEA @luigicampos1 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    2. 2. Big Data – The New Information Challenges and Technology Advances AGENDA - Where’s the new information? - Where could it be? - If in the right place, what could be achieved? - New Technologies and the role of Oracle Corp. - Challenges of the main industries. - The Role of Switzerland in the Big Data space3 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    3. 3. Where’s the New Information? New Sources New Analytics New Integrations New from Any Data on All Data of Data Orchestrations Any Computing model4 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    4. 4. What does “New Data” really means? Absorb Any Data, Any Source All Dimensions of Data = 360º5 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    5. 5. What does “All Data” really means? Stop Throwing Any Data, Data Away Any Source = Know More About What’s Going On in your Business6 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    6. 6. What does “Any Data” really means? Any Data, Any Source Tap Any Data = New Revenue Streams7 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    7. 7. Next Step: Reduced It Any Data, Full Range of All Data Any Source Analytics Filtered Cleaned8 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    8. 8. Oracle Big Data Reference Architecture Source Data Layer Information Access Enterprise Data Warehouse Performance Processes Management Staging Data Layer BI Abstraction & Query Federation COTS/ERP Strongly Typed Foundation Layer Alerts, Data Dashboards, Performance Layer Reporting External Enterprise Data Data with full history Quality Embedded Services Data Marts Social/Text Weakly Typed Data Information Discovery Sensors Knowledge Discovery Layer Advanced Streaming Analysis & Data Mining Sandbox Rapid Dev Sandbox Data ScienceSecurity and Metadata Data Integration 9 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    9. 9. Translated into Oracle Product Architecture Source Data Layer Information Access Enterprise Data Warehouse Performance Processes Management Staging Data Layer BI Abstraction & Query Federation COTS/ERP Strongly Typed Foundation Layer Data Oracle Oracle BI Dashboards, Alerts, External Database Enterprise Data Performance Layer Reporting Foundation Data -Advanced Analytics & OLAP with full history Quality Embedded - Spatial and Graph Data Marts Endeca Services Social/Text - Industry Models Oracle Weakly Typed Information Information Data Sensors Big Data Discovery Discovery Appliance Endeca Information Knowledge Discovery Layer Advanced Discovery Analysis & Streaming Data Mining Sandbox Rapid Dev Sandbox Data ScienceSecurity and Metadata Data Integration Oracle Big Data Connectors 10 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    10. 10. Big Data Appliance Hadoop Ecosystem for the Enterprises Oracle Big Data Appliance 18 Nodes 648TB, 288 CPUs 12 Nodes (U)Cloudera Dist. Hadoop 6 Nodes Oracle NoSQL 216TB, 96 CPUs BD Connectors11 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    11. 11. Oracle’s Big Data Connectors Unlock the power of Hadoop integration Hadoop Oracle Database Oracle Big Data Connectors12 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
    12. 12. (1) Oracle Data Integrator Application Adapters for Hadoop Transforms Via MapReduce(HIVE) Benefits  Consistent tooling across BI/DW, SOA, Integration and Big Data Oracle Data Integrator Activates  Reduce complexities : graphical tooling Oracle Loads Loader for Hadoop  Improves productivity Oracle Database Improving Productivity and Efficiency for Big Data13 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
    13. 13. (2) Oracle SQL Connector for Hadoop Accessing HDFS Data from Oracle Database Access or load into theFeatures database in parallel using external table mechanism HDFS Oracle DatabaseAccess and analyze data in SQL Queryplace on HDFSQuery and join data on ODCH ExternalHDFS with database ODCH Table ODCHresident data HDFSLoad into the database Clientusing SQL if requiredAutomatic load balancing tomaximize performance14 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
    14. 14. (3) Oracle R Connector for Hadoop R Analytics leveraging Hadoop and HDFS Oracle R Client Linearly Scale a Robust Set of R Algorithms MAP MAP MAP MAP Hadoop Leverage MapReduce for R Calculations REDUCE REDUCE Compute Intensive HDFS Parallelism for Simulations15 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
    15. 15. What is ?• Brings R’s statistical functionality to the Oracle Database• Eliminates R’s memory constraints • Allows R to run on very large data sets• Oracle R is architected for enterprise production infrastructure • Automatically exploits database parallelism without requiring parallel R programming• Oracle R leverages the latest R algorithms and packages • R is an embedded component of the DBMS server• Part of Oracle Advanced Analytics (+ODM)
    16. 16. Oracle R ArchitectureR workspace console Function push-down Oracle statistics engine OBIEE, Web – data transformation & Services statistics Development Production Consumption• Leverages SQL for data prep, analysis and enhanced statistics engine• R engine runs on database nodes for production enablement of R models• Leverages Exadata—Oracle R workloads run in-database and can be bound to database nodes for workload isolation• Enriches OBIEE dashboards with Oracle R statistics and analytics
    17. 17. Oracle Data Mining (ODM) Data mining can answer questions that cannot be addressed through simple query and reporting techniques.• Data Mining: Insight from discovering relationships • Knowledge about what happened in the past • Characterization, segmentation, comparisons, discrimination • Descriptive models of patterns• Predictive Analytics: Making better decisions and forecasts • Knowledge about what is happening right now and in the future • Classification and prediction of patterns • Rule-and-model driven
    18. 18. Data Mining – Some Definitions Supervised Learning Problem Classification Sample ProblemClassification Predict customer response to an affinity card programRegression Predict customer’s ageAttribute Importance Find the most significant predictors, data preparation A1 A2 A3 A4 A5 A6 A7
    19. 19. Data Mining – Some Definitions Unsupervised Learning Problem Classification Sample ProblemAnomaly Identify customer purchasing behavior that isDetection significantly different from the normAssociation Find the items that tend to be purchasedRules together and specify their relationship – market basket analysisClustering Segment demographic data into clusters and rank the probability that an individual will belong to a given clusterFeature Group the attributes into generalExtraction characteristics of the customers F1 F2 F3 F4
    20. 20. Endeca Information Discovery Sandbox and Production mode Endeca Information Discovery Studio Endeca MDEX Server Intergration Suite21 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    21. 21. Information Discovery on Big Data To Be Released Complements Hadoop Soon Oracle Endeca Information Discovery  Data Variety - structured and unstructured data  Massive scalability  No model required  In-memory analytics  Batch execution  Interactive  Business users Filters Deep Large-Scale Fast Self-service Processing Persists Information Discovery22 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    22. 22. What is the world doing today Large Spanish Clothes Manufacturer • Automation • Sensory Event Processing • Quality Assurance23 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    23. 23. What is the world doing today Second Largest Bank in United States of America • Analysis of data xLoB: Loans, Insurance, on-line banking, card products • Market assessment • Risk Analysis • Revenue lift for new & existing products24 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    24. 24. Telco Industry Deep, Big and Fast Deep • SNA*, Find Influencers, RA** Big • Network Optimization, • CDR Analysis Fast • Sentiment Analysis • Location Based Services • Click stream Analysis© 2012 Oracle Corporation – Proprietary and Confidential * Social Network Analysis (Rate plan optimization) ** Revenue Assurance
    25. 25. Retail Industry Marketing, Merchandising and Supply Chain Marketing • In-store behaviour analysis • Sentiment Analysis + Microsegmentation Merchandising • Assortment optimization Supply Chain • Distribution and logistics optimization • Informing supplier negotiations© 2012 Oracle Corporation – Proprietary and Confidential
    26. 26. Oil and Gas Use Cases Hadoop and Seismic Data Processing27 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.
    27. 27. Life Sciences / Pharmaceutical Life Sciences • DNA Sequencing, Diseases Correlation Pharmaceutical • Clinical Trial – meds simulation© 2012 Oracle Corporation – Proprietary and Confidential
    28. 28. The Role of Switzerland in the Big Data space  Scientific Research  Data Science  Financial Industry  Telco  Public Sector© 2012 Oracle Corporation – Proprietary and Confidential
    29. 29. Danke / Merci / Grazie30 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    30. 30. 31 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

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