Oracle, שרון עוזיאל
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  • 1. 1 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 2. The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remain at the sole discretion of Oracle.2 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 3. Oracle Big Data Next Generation Data Management Sharon Uziel, Oracle Consulting Infrastructure Manager3 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 4. 4 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 5. Explosive Data Growth Harnessing insight from Big Data provides an opportunity to gain competitive advantageGIGABYTES OF DATA) CREATED 10,000 1.8 trillion gigabytes of data Requires capability was created in 2011… for rapid:  More than 90% is (IN BILLIONS) 5,000 unstructured data  Assimilation  Approx. 500 quadrillion files  Quantity doubles  Interpretation every 2 years  Response/Action 0 2005 2010 2015 5 Copyright © 2011, Oracle and/or its affiliates. All rights Content Provided By Cloudera. Policy Classification from Slide 8 Insert Information Protection STRUCTURED DATA UNSTRUCTURED DATA reserved.
  • 6. Understanding the Scope of Big Data Big Data enables including all types of data in decision making models Successful • Structured & Unstructured companies: • Internal &  Leverage existing External frameworks • Transactional & Data  Develop new Warehouse models  Move quickly and adapt6 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 7. Oracle’s Vision for Analysis on ALL Data Provide a complete solution allowing you to manage your business, not complex information technology configurations Organize Visualize Stream Acquire Analyze /Discover /Decide7 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 8. Oracle Big Data Platform Accelerate time to market and reduce risk with end-to-end solution Organize Visualize Stream Acquire Analyze /Discover /Decide Endeca Information Discovery Oracle is the industry leader in database and information management. With largest global customer base, the power of Oracle provides all the components you need to get results from your Big Data initiatives broadly and quickly8 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 9. Sample of Big Data Use Cases Today Companies across industries are using Big Data insights to grow their businessIndustry Big Data Use Cases Potential Benefits • Analysis of data sets across lines of business (loans, insurance, • Increased share of customerBanking & on-line banking, card products) for market assessment • Increased customer loyalty • Risk analysis & revenue lift for new & existing products • Increased overall revenue Finance • Analysis of stock portfolio trends & risk • Decreased financial risk • Analysis of unexpected health condition associations using • Improved quality of careHealthcare electronic health records and visualization • Reduced cost of care • Advertising performance / optimization • On-line service loyaltyOn-Line • Feature popularity & consumer ratings • Better social community experience • People & career matching • More secure and predictable services Services & • Search optimizationSocial Media • Security threat analysis • Troubleshooting • Analysis of auto sensors reporting location, parts and • Increased customer safety & loyaltyAutomotive component problems •Minimize warranty claims • Optimize manufacturing processes9 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 10. Unstructured data in An example in Banking Server Logs is VALUABLE!! Server Logs showRegistered Customer using • Not a Credit Card Customer • Frequent visits to CC pagesInternet banking • Reviewing Features/benefits • Considerable time spent BI/DWH identifies … • this Customer as an HNI • Seek info on preferred channel • credit worthiness for offer • Make an offer 10 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 11. Cross-Industry Collaboration“Impulsive” Buying Behavior • Coupon used Retail + Telco • Increased spend Customer Profile: Coupon: Send Revenue share with Telco, a Win-Win!Customer enters 30-35 Female Proximity to store < 200meters 2 kids < 5yrsshopping mall (Telco 10% discount if used within next Singed up for coupons 15 minutescaptures “high volume” 112 113location data from Cell Phone) 114 115 116 117 118 119 120 121 126 125 124 127 123 122 Layout of a Shopping Mall 11 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 12. Big Data augments traditional data .. Video and Images Documents TappingBig Data: into Finding &Decisions Social diverse monetizingbased on all Datayour data Machine- data hidden Generated Data sets relationshipsInformationArchitectures Today:Decisions based on Transactions Driving data-baseddatabase data business decisions 12 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 13. How New, Big Data adds Value? “I found it” Stores Looking back “PAST” Catalog/ Call Web Center Retail Decisions Social Search Looking ahead Networks “FUTURE” “I think” “I want”13 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 14. Big Data Opportunity Through 2015, more than 85 percent of Fortune 500 organizations will fail to effectively exploit big data for competitive advantage. Source: Gartner BI Summit, “Extreme Data: Challenges and Opportunities for Large-Scale Data Warehousing, BI and Analytics” (May 2011)14 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 15. Oracle Approach15 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 16. Oracle Integrated Solution Stack for Big Data BigBig Appliance Data Data Exadata Exalytics Exadata Exalytics Appliance Hadoop HDFS (MapReduce) Oracle Database + In-Database Options Analytics Oracle NoSQL Oracle Big data (Oracle R Analytic Database Connectors Enterprise, Applications, OLAP, Spatial, OBIEE, Partitioning, Hyperion Enterprise RAC, etc.) Oracle Data Applications Integrator ACQUIRE ORGANIZE ANALYZE DECIDE16 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 17. Information Management Optimization ELT Platform DatabaseTraditional (ODI for bulk Consolidation Sources data loads + Potentially Platform 1 / 10 GbE Goldengate for (Any application on 11.2 CDC) databases) Big Data Integration Platform Comprehensive• Big Data Connectors Analytics & Visualisation Platform (Retail Analytics, ADI, Exalytics ,OBIEE, BI Apps) Oracle InfiniBand Oracle InfiniBand Oracle Big Data Appliance Exadata Exalytics 17 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 18. Oracle Big Data Appliance BigBig Appliance Data Data Exadata Exalytics Exadata Exalytics Appliance Hadoop HDFS (MapReduce) Oracle Database + In-Database Options Analytics Oracle NoSQL Oracle Big data (Oracle R Analytic Database Connectors Enterprise, Applications, OLAP, Spatial, OBIEE, Partitioning, Hyperion RAC, etc.) Oracle Data Integrator ACQUIRE ORGANIZE ANALYZE DECIDE18 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 19. Big Data Appliance What is Oracle Approach? • 18 Sun X4270 M2 Servers per Rack – 864 GB memory – 216 cores – 648 TB storage • 40 Gb/s InfiniBand Fabric – Inter-rack Connectivity – Inter-node Connectivity • 10 Gb/s Ethernet Connectivity – Data center connectivity19 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 20. Oracle Integrated Solution Stack for Big Data BigBig Appliance Data Data Exadata Exalytics Exadata Exalytics Appliance Hadoop HDFS (MapReduce) Oracle Database + In-Database Options Analytics Oracle NoSQL Oracle Big data (Oracle R Analytic Database Connectors Enterprise, Applications, OLAP, Spatial, OBIEE, Partitioning, Hyperion Enterprise RAC, etc.) Oracle Data Applications Integrator ACQUIRE ORGANIZE ANALYZE DECIDE20 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 21. What is Hadoop? • Scalable fault-tolerant distributed system for data storage and processing – Open source under Apache license • Enables analysis of Big Data – Can store huge volumes of unstructured data, e.g.,weblogs, transaction data, social media data – Enables massive data aggregation – Highly scalable and robust – Problems move from processor bound (small data, complex computations) to data bound (huge data, often simple computations) • Consists of two key services 1. Hadoop Distributed File System (HDFS) 2. Map-Reduce • Other Projects based on core Hadoop – Hive, Pig, HBase, Flume, Sqoop, and others • Originally sponsored by Yahoo!  Apache project  Cloudera • Based on Googles GFS and Big Table whitepaper21 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 22. Hadoop in action INPUT 1 MAP MAP MAP REDUCE REDUCE MAP REDUCE MAP MAP REDUCE REDUCE SHUFFLE MAP /SORT MAP OUTPUT 1 MAP MAP REDUCE MAP REDUCE MAP REDUCE MAP SHUFFLE MAP /SORT SHUFFLE /SORT INPUT 222 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 23. Data & Processing Flow ORACLE BIG DATA APPLIANCE ORACLE LOADER FOR HADOOP EXADATAINPUT 1 MAP MAP MAP REDUCE REDUCE MAP MAP REDUCE MAP REDUCE MAP MAP REDUCE REDUCE SHUFFLE REDUCE SHUFFLE MAP /SORT MAP /SORT MAP MAP MAP REDUCE MAP REDUCE MAP REDUCE MAP REDUCE MAP MAP REDUCE SHUFFLE SHUFFLE MAP /SORT MAP /SORT REDUCE SHUFFLE /SORTINPUT 223 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 24. Cloudera Hadoop Distribution What is Oracle Approach? Apache Hadoop Apache Sqoop Apache Hive Apache Mahout Apache Pig Apache Whirr Apache HBase Apache Oozie Apache Zookeeper Fuse-DFS Apache Flume Hue Latest details at: http://www.cloudera.com/hadoop-details/24 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 25. Cloudera Manager25 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved. http://www.cloudera.com/wp-content/uploads/2011/12/Cloudera-Manager-DS-3.7-FNL2.pdf
  • 26. Oracle Integrated Solution Stack for Big Data BigBig Appliance Data Data Exadata Exalytics Exadata Exalytics Appliance Hadoop HDFS (MapReduce) Oracle Database + In-Database Options Analytics Oracle NoSQL Oracle Big data (Oracle R Analytic Database Connectors Enterprise, Applications, OLAP, Spatial, OBIEE, Partitioning, Hyperion Enterprise RAC, etc.) Oracle Data Applications Integrator ACQUIRE ORGANIZE ANALYZE DECIDE26 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 27. Oracle NoSQL What is the functional need? • Simple data storage, typically non-SQL or Not-only-SQL for Solution categories such as • Online interactive processing • Social Networks • Email • Shopping Cart • Large data repositories without a fixed schema • Extract Transform Load batch processing (Hadoop) • Distributed (Cloud) storage • Large amounts of data (Terabyte – Petabyte range)27 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 28. Oracle NoSQL What is Oracle Approach? • Simple Data Model – Key-value pair with major+minor-key paradigm Application Application – Read/insert/update/delete NoSQL DB Driver NoSQL DB Driver • Scalability – Dynamic data partitioning and distribution – Optimized data access via intelligent driver • High availability – One or more replicas – Resilient to partition master failures – No single point of failure – Disaster recovery through location of replicas • Transparent load balancing Storage Nodes Storage Nodes – Reads from master or replicas Data Center A Data Center B – Driver is network topology & latency aware28 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 29. Selection Option for Use Case Hadoop Distributed File System Oracle NoSQL Database (HDFS) File System Database Parallel scanning Indexed storage No inherent structure Simple data structure High volume writes High volume random reads and writes29 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 30. Oracle Integrated Solution Stack for Big Data BigBig Appliance Data Data Exadata Exalytics Exadata Exalytics Appliance Hadoop HDFS (MapReduce) Oracle Database + In-Database Options Analytics Oracle NoSQL Oracle Big data (Oracle R Analytic Database Connectors Enterprise, Applications, OLAP, Spatial, OBIEE, Partitioning, Hyperion Enterprise RAC, etc.) Oracle Data Applications Integrator ACQUIRE ORGANIZE ANALYZE DECIDE30 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 31. Oracle Big Data Connectors What is the functional need? 1. Connect HDFS to traditional RDBMS 2. Provide ability to access HDFS directly from RDBMS 3. Provide ability to integrate from source file to Hadoop Cluster to Oracle database visually using a wizard based approach 4. Allow advanced analytics users to leverage a Hadoop Cluster with HDFS and MapReduce from the R environment31 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 32. Oracle Big Data Connectors What is Oracle Approach? 1. Oracle Loader for Hadoop (OLH) – A map/reduce utility for optimized load of data into Oracle Database – Pre-partition, sort, and transform data into an Oracle ready format on Hadoop and load into Oracle Database 2. Oracle Direct Connector for Hadoop Distributed File System – Directly access to data files on HDFS • Create an external table pointing to file location on HDFS • Query data from database using SQL • Load data into database when required32 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 33. Oracle Big Data Connectors What is Oracle Approach? 3. Oracle Data Integrator Application Adapter for Hadoop – The knowledge modules simplify processing of unstructured and structured data on Hadoop – Data Validation and transformation in Hadoop. – Exporting Hadoop data-sets to Oracle. 4. Oracle R Connector for Hadoop – Allows R users to leverage a Hadoop Cluster with HDFS and MapReduce from the R environment – Provides transparent access to Hadoop Cluster: MapReduce and HDFS-resident data33 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 34. Oracle Loader for Hadoop: Offline/Online Option Read target table metadata from the database Write from reducer nodes to Copy files from HDFS to a Oracle Data Pump files location where database Perform partitioning, can access them sorting, and data MAP conversion REDUCE DATA Import into the database in MAP parallel using external table REDUCE DATA mechanism SHUFFLE MAP /SORT ORACLE LOADER FOR HADOOP MAP REDUCE DATA MAP REDUCE DATA SHUFFLE MAP /SORT REDUCE DATA34 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 35. Oracle Direct Connector for HDFS (ODCH) Directly access data files on HDFS from external tables Directly access data files on HDFS from external tables MAP REDUCE DATA MAP DATA DATA REDUCE DATA SQL QUERY SHUFFLE MAP /SORT DATA DATA ANY MAPREDUCE JOB DATA External ODCH Table ODCH DATA DATA DATA DATA MAP REDUCE DATA MAP REDUCE DATA SHUFFLE • Raw data in delimited text file format MAP /SORT REDUCE DATA • Data Pump files created by Oracle Loader for Hadoop (OLH)35 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 36. Selection Option for Use Case Oracle Loader for Hadoop Output Option Use Case Characteristics Online load with JDBC The simplest use case for non partitioned tables Online load with Direct Path Fast online load for partitioned tables Offline load with datapump files Fastest load method for external tables Direct HDFS Oracle Direct Connector for HDFS Leave data on HDFS Load into database when needed36 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 37. Oracle R Enterprise What is the functional need? Open source language and environment Used for statistical computing and graphics Strength in easily producing publication-quality plots Highly extensible with open source community R packages37 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 38. What Are ’s Challenges? 1. R is memory constrained – R processing is single threaded - does not exploit available compute infrastructure – R lacks industrial strength for enterprise use cases 2. R has lacked mindshare in Enterprise market – R is still met with caution by the long established SAS and IBM/SPSS statistical community • However, major university (e.g. Yale ) Statistics courses now taught in R • The FDA has recently shown indications for approval of new drugs for which the submission’s data analysis was performed using R38 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 39. Oracle R Connector for Hadoop Architecture Client Host Oracle Big Data Oracle Exadata Appliance R Engine R Engine ORE* ORE* ORHC ORHC Hadoop MapReduce R Engine Cluster Nodes ORE* Software HDFS*optional 39 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 40. Oracle Integrated Solution Stack for Big Data BigBig Appliance Data Data Exadata Exalytics Exadata Exalytics Appliance Hadoop HDFS (MapReduce) Oracle Database + In-Database Options Analytics Oracle NoSQL Oracle Big data (Oracle R Analytic Database Connectors Enterprise, Applications, OLAP, Spatial, OBIEE, Partitioning, Hyperion Enterprise RAC, etc.) Oracle Data Applications Integrator ACQUIRE ORGANIZE ANALYZE DECIDE40 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 41. Oracle Data Integrator Knowledge module for HadoopCan we do the integration from source file toHadoop Cluster to Oracle database visually usinga wizard based approach? ( Not too keen towrite the map reduce code) 41 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 © 2011 Oracle Corporation – Proprietary and Confidential reserved.
  • 42. Oracle Data Integration for Big Data Oracle Approach: Improving Productivity and Efficiency for Big Data Transforms Via MapReduce ODI Hadoop Integration • New ODI Technology for Hive Oracle Data Integrator Activates • New ODI KM’s for Hive • Reverse from Hive Tables Oracle Loads • File to Hive Loader for Hadoop • Hive Control Append • Hive Transform • Hive to Oracle (OLH) • Hive is used within KM’s to generate Oracle SQL like calls which are transformedBig Data Appliance Oracle Exadata into Map Reduce statements 42 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 43. Oracle Big Data Analysis Approach43 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 44. Approach 1: Discovery led Analytics Hadoop, Oracle Connectors and R Data Analysis 1. Un-Modeled Data 1. Fast exploration of new and 2. External Data (Low control un anticipated questions. on format and access, Low 2. Non structured navigation Quality) paths 3. Non-Structured Data 3. Analysis on all possible 4. ..and structured , internal dimensions (nothing is left data out)44 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 45. Approach 2: Discovery led Analytics Endeca Information Discovery Automatically unified and Interactive search,Diverse and changing Drag-and-drop application enriched in Endeca Server navigation and analytics information composition – no predefined model for exploration andStructured required analysisSemi-StructuredUnstructured Endeca Information Discovery 45 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 46. 1. Auto Indexing 2. All Dimensions across all data Endeca Dashboard 3. Intuitive summaries – refinement counts 4. Tag Clouds (Image and text) 5. No need for pre specified navigation paths + Typical Search … + + 1. Search across structured and Non-STRUCTURED data 2. With minimum effort required for Schema design 3. No summarization .. data up to atomic level of detail is available for analysis. 4. Sentiment Analysis46 Copyright © 2011, Oracle and/or its affiliates. All rights 5. Intuitive business use focused dashboard Insert Information Protection Policy Classification from Slide 8 reserved. 6. Visualize anything on a map
  • 47. Familiar Methodology, Enhanced Results Traditional Delivery Endeca Agility Benefits1. Gather 1. Incorporate More Requirements, Sources, Satisfy Define Scope Portal Layer More Users3. Define Semantics, 3. Streamlined Create Reports, Report Layer Application Build Portal Development Presentation Semantic Layer4. Administer and 4. Low maintenance Manage System and overhead Data Warehouse2. Model, Create, Data Marts Endeca Server 2. More Flexible Data Load, and Repository Configure Data ETL ITL Repository ERP Merch Supply ERP Merch Prod Web Intra andise Chain net 47 Copyright © 2011, Oracle and/or its affiliates. All rights Store Insert Information Protection Policy Classification from Slide 8 SCM Roster CMS WCM reserved.
  • 48. Next Gen Information Management Platform Pre Built Oracle Analytical Applications, ADIInformation Endeca Information Delivery Traditional BI Memory times ten & Essbase) Exalytics (BI Foundation, In Reports, Charts Visualization MicroStrategy Multi-Delivery & Reports DimensionalDecision-Making Analysis EXADATA +Data Warehousing Oracle R Enterprise Data MartsAnd Data Marts Endeca Server OLAP Cubes Data WarehouseInformation ODI Goldengate Non-structured Data Access/Transformation ETL Systems (Data Stage / OWB)Integration Big Data Connectors Data Quality + MDM (Site,Product,Customer,Supplier)Enterprise Systems Big Data Appliance& Data Sources Content Mgt Stores, Merchandise, supply chain Custom Web Log files File System Systems Fly Buy Applications 48 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 49. Oracle Integrated Solution Stack Engineered Systems ACQUIRE ORGANIZE ANALYZE DECIDE49 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 50. Q&A50 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 51. 51 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.
  • 52. 52 Copyright © 2011, Oracle and/or its affiliates. All rights Insert Information Protection Policy Classification from Slide 8 reserved.