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Ipedo Company Overview
 

Ipedo Company Overview

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Ipedo Company Overview Ipedo Company Overview Presentation Transcript

  • Ipedo XIP Overview for {Customer Name} July 2006 {Name}
  • Agenda
    • Introductions
    • EII Opportunities for <customer>
    • Ipedo XIP Overview
  • {Customer Name} {Issues/Fits/Needs}
    • Summarize issues or needs of the prospect
  • Ipedo Background
    • Focus on Enterprise Information Integration (EII) software
    • Customers and partners worldwide
    • Based in Redwood City, CA
    • Founded in 2000; Venture backed by Draper Fisher Jurvetson and AsiaTech Management
    50+ Customers Quick Facts Influential Partners
  • EII Market Opportunity Growing Batch Realtime Process Data EAI EII ETL Sources: * Forrester Research;  TDWI/BI Research 10/05
    • Target is a user
    • Supports ad hoc applications
    • Results moved in realtime at query time
    • Supports relational + XML
    • Data warehouse can be source
    EII EII
    • Target is a database
    • Supports scheduled applications
    • Batch; data moved ahead of time
    • Supports relational + multidimensional
    • Data warehouse is the target
    ETL EII Use Plans 
  • EII Brings Advantages to New Projects Solution Key Advantages of EII Dashboards
    • Intelligently combine company-wide information
    • Provide view customization per context or user
    • Maintain or enhance enterprise security model
    Risk Management BI / Reporting
    • Enable better reports with up-to-date information
    • - Access diverse data sources via queries, not code
    • - Mix real-time data with historical information
    • Extract key information across company data silos
    • Reduce cost of compliance
    • Manage complex XML schemas such as FpML
  • EII Enables Integration Infrastructure Solution Key Advantages of EII Metadata Management
    • Consolidate metadata across the enterprise
    • Exchange data models with other modeling tools
    • Append business definitions to integrated data
    Data Service Layer for Service-Oriented Architectures (SOA) Master Data Management (MDM)
    • Maintain consistent view of core business entities
    • Avoid political issues around data ownership
    • Reduce data replication
    • Create data services layer that simplifies SOA
    • Allow universal info exchange as Web Services
    • Manage both HTTP and SOAP Web Services
  • Use Case: Real-Time Reporting Reporting Trade Data Market Data Risk Data Oracle OLTP Sybase OLTP Web Service EII Server Virtual DB
  • Use Case: Data Warehouse Prototyping Relational Databases Sales Ops Applications/Marts EII Server Virtual DB Data Warehouse Reporting & BI Tools Migrate ETL Prototype Permanent
  • Use Case: Database Migration - Insulation from Change Reporting Trade Data Trade Data Sybase DB2 EII Server Virtual DB Migrate
  • Use Case: Migrating to SOA Relational Databases Sales Ops Applications/Marts EII Server Virtual DB
    • EII and SOA:
    • Make relational models available as services
    • Protect against a performance hit
    • Maintain adequate governance
    • Let DBAs be DBAs, and developers be developers
    View 1 View 2 Registry Application 1 Application 2
  • Ipedo Creates a Virtual Data Services Layer from Multiple Remote Data Sources Ipedo XIP Sales Business Unit A Business Unit B Customer System Finance System Operations System Finance System Operations System Mfg. System Finance Marketing Subset Data Distributed Data Mixed Data Query Query Query Query
    • Cross business boundaries
    • No data replication
    • Mix data types in real time
    TIBCO SQL Server SAP Oracle SQL BAPI SOAP WSDL JMS SOAP WSDL SQL View 2 Operations View 1 Sales by Region View 3 Financials by Account View 4 Financials Quarterly
  • Ipedo Provides a Data Services Layer
  • Views Virtualize Data Access Custom Metadata Data Source Connections Data Mappings Transformations Output Schema View Caching Policy Validation Security View Data Warehouse Relational Database Web Services Packaged Apps XML Documents Message Queues Ipedo Views A View is a metadata construct. It contains no data, unless explicitly cached. When a query is executed against a View, Ipedo returns the data to fulfill the request.
  • Deploying Ipedo XIP Ipedo XIP Development/QA Ipedo XIP Monitor & Manage Production App Server Reporting Dashboards Web Apps SOA End Users
    • Views are designed, developed and tested
    • Views are then deployed to production server
    • Additional production servers can be added to support more users
    View 1 View 2 View 3 View 1 View 2 View 3 Design Time Runtime Deploy
  • Ipedo XIP Solves Integration Challenges  Faster Development $ Cheaper Deployment  Lower Risk $ Data discovery tools and wizards for RDB, XML and SOA  Data source introspection with statistic and key gathering  Metadata collection and management $ Drag and Drop View creation $ Automatic SQL and XQuery generation  Rule and aggregate tools  Automatic XML to RDB bridging $ Automatic query plan generation  Cost-based query optimization  Policy-driven caching for SQL and XML result sets $ Virtualized data access without replication  Customizable result set presentation  Adjustable configurations for scaling up, down or out  Detailed data access controls  Performance thresholds and limits  Data lineage and query forensics reporting Automated Discovery Powerful Modeling Intelligent Optimization Flexible Deployment Comprehensive Management Where does our data reside and in what format? How can I quickly combine all the data I need for my application? How can I meet my users’ performance needs and not break existing systems? How can I tailor data to suit the various and changing user needs? How can I make sure we adhere to our security and data governance policies?
  • Ipedo Speeds Discovery and Data Combination Finance Sales Ops Source Introspection Data Discovery Metadata Collection Tables, Keys, Statistics Stored Proc, Keys, Statistics Services, Methods Catalog Read Catalog Read UDDI Lookup Design Time Automated Discovery Powerful Modeling GUI View Creation Auto-Query Generation Intelligence SQL Statement XQuery Statement Metadata Lookup Metadata Addition Metadata Catalog Rules Aggregates
  • Auto-Discovery Simplifies Data Integration XIP > Automated Discovery Lists all tables Displays column names Shows data types Generates SQL DDL Ipedo automatically Introspects remote data sources.
  • Custom Metadata Annotates Data XIP > Automated Discovery Ipedo Metadata Catalog Add custom metadata to annotate: * data sources * remote tables * columns * views Retrieve metadata from Ipedo system tables using standard SQL system functions
  • GUI View Builder Simplifies Development XIP > Powerful Modeling ‘ Drag and Drop’ tables into the design pane Build logic using intuitive graphical tools: TotalPrice>$50,000 Ipedo automatically generates SQL, which can be modified
  • Ipedo Ideal for SOA
    • Consuming Web Services
    • Blending multiple web services
    • Conditional invocation of web services in a specific order
    • Combining web services with other data sources
    • Processing Web Services
    • Business rules checking on web services data
    • Caching of web services data for reuse
    • Ipedo uses a query-based approach, preserving the format of the original data
  • ‘ Web Services’ Tables Allows SQL on XML Customer Records (Oracle) Market Exposure (SOAP Web Service) Credit Record (HTTP Web Service) XML XML Rel. Rel. XMLQuery XMLQuery XML Core SQL Core BI/Reporting Client ODBC / JDBC SQL SQL SQL SQL Data Sources Remote Tables Relational View Use familiar SQL syntax with Web Services: Join, Group By, hints XIP > Powerful Modeling
  • Map Web Services to Relational Tables XIP > Powerful Modeling Select a Web Service Map results to relational columns Modify data types to fit your application’s needs
  • Ipedo Application Adapters Integrate Data Into Ipedo Framework
    • Pre-built templates and objects
      • Accessible via SQL, Web Services, XQuery
    • Visual drag and drop, API/script access
    • Broadest range of applications (Librados)
    • Simple pricing structure ($15K/CPU per adapter)
    Application View Templates Application Data Objects Oracle Integration Tables Integration Objects API BAPI SOQL Oracle Siebel SAP R/3 Salesforce.com Common Reports Common Objects
  • Ipedo Approach to SAP BW Data
    • Librados adapters for relational data
    • Simba OLAP adapters for cube data
    • Best-in-class vendors of standards-based advanced analytical data adaptors
    • Perform more complex analyses using broader range of data sources
    • Integrate SAP BW with other data sources
  • Ipedo Optimizes Data Access Data Virtualization Customizable Presentation Deployment Time Flexible Deployment Intelligent Optimization Query Optimization Query Execution Cache Policy Cost-Based Optimizer Query Plan Advanced Tuning XML View Cache SQL View Cache Schedule Refresh Policy
    • Result Set Streaming
    • Query Throttling
    • Concurrency
    • Hybrid Intermediate Result Processing
    Virtual View Virtual View Virtual View SQL/JDBC SQL/ODBC XML/WS XML/WS JSP/HTTP Order View Risk View Leads View
  • Ipedo Appears as a Database
    • External applications see tables within Ipedo
    • Each table can contain data from disparate sources (relational or Web Services)
    XIP > Flexible Deployment
    • Performance Optimization
  • Cost-Based Query Optimization
    • CBO means knowing how a query will perform; Rules-based optimization means guessing
    • Examines statistics in remote data sources
      • Size of database (cardinality)
      • Indexes - presence, type, selectivity
    • Allows user to assign cost function to remote procedure
    • Determines join order and join algorithm based on “cost” of performing query
    • Minimizes intermediate results to speed performance
    • CBO greatly enhances EII performance
    XIP > Intelligent Optimization
  • Query Plan Shows Optimization Approach
    • Automatic Query
    • Plan Generation
    • Join Order
    • Join Algorithms
    • Pushdowns
      • Joins
      • Predicates
      • Aggregates
    XIP > Intelligent Optimization
  • Advanced Tuning Provides Ultimate Control
    • Hints
      • override Ipedo’s query engine with domain-specific knowledge
    • SQL pass-through
      • Ipedo sends data source specific SQL to the data source for execution
    • Polymorphic remote function mapping
    XIP > Intelligent Optimization pass sql &quot;dbcc show statistics (customers, zipcode_stat)&quot; to mssqlsvr select a.* from properties joinOrder=fixed orders o, customers c properties joinStrategy=NESTEDLOOP where c.c_customerid = o.o_customerid
  • Query Execution
    • Result Set Streaming
      • Immediate response to user
    • Query Throttling
      • Control load on back-end systems
    • Concurrency
      • Support large numbers of users with optimal performance
    • Hybrid memory-disk intermediate result set processing
      • Manage memory to handle extremely large data sets
    • Flexible Security Model
      • Common user credential across data sources
      • Dynamic session-based user credentials for data sources
      • View-based access control
    XIP > Intelligent Optimization
  • Intelligent Caching Cuts Response Time, System Load
    • Speeds Response to Repeated Queries
    • Reduces Load on Back-End Data Sources
    • Cache relational or XML data natively
    • Sub-queries on cache
    • Scheduled or on-demand cache loading
    • Automatic cache invalidation
    XIP > Intelligent Optimization
  • Top-Down Modeling Tools Can Create Tables and Views in Ipedo 1. Build Logical Views In enterprise modeling software 2. Export Data Definitions Using standard ODBC 3. Select Options For schemas, tables, columns, indexes 4. Customize DDL for Ipedo Syntax reflects virtual table construct 5. View Model in Ipedo All metadata appears, available for integration
  • Reuse Ipedo Views in Modeling Tools 1. Build Views in Ipedo Map tables and create relationships across disparate data sources 2. Reverse Engineer Start with blank template inside ERwin 3. Select Options Choose what information to import 4. Model Appears in ERwin Tables, metadata, joins, etc., available to share and reuse
  • Comprehensive Monitoring and Reporting Simplifies System Management Partners Finance Marketing Management Dashboard
    • Users
      • Session Details
      • Session Controls
      • Query Controls
    • Data Sources
      • Source Status
      • Source Impact
    • Thresholds
      • Limits on Views
      • Limits on Users
    • Data Lineage
      • View Source Search
      • Historical Access Reports and Search
    Ipedo XIP Users Administrators Ipedo Billing View Order View Risk View Leads View
  • Detailed User and Source Management Session Status XIP > Comprehensive Management Database Availability See Query Plan Kill Session or Cancel Query
  • Ipedo Facilitates Data Governance Where is the information in this view coming from? How does change to data source affect downstream views?
  • Dynamic Failover Redirection
    • Many large customers have database replicas worldwide - want EII Views to failover to replica database
    • Ipedo XIP will failover after it reaches retry limit
    SOA Sybase 1 Oracle 2 Oracle 1 New York London New York Tokyo Sybase 2
  • Enterprise Class Product
    • Clustering and Failover - support for standard clustering environment
    • Scales to manage millions of documents and terabytes of storage
    • Industry-standard management APIs
    • Robust archival & backup capabilities
    • Automated schema migration
    • Integrated document versioning and journaling
  • Ipedo XIP Components SQL Engine • Query Optimization XQuery Engine • Query Optimization Federation Engine • Query Orchestration • Statistical Analysis Data Services Manager • Web Services • SQL Data Services • XQuery Data Services • APIs • Web Tag Library • Outbound Pipelines Rules / Intelligence Engine • Visual Rules Wizard • Rules Processing • Compound Aggregate Indexing Integration / Transformation Engine • Relational Views • XML Views • Graphical View Builder • Feeds • Inbound Pipelines Metadata Manager • Sources • Mappings Cache Manager • Relational • XML XML Store • Schema Management
  • HP - Finance Dashboard
    • Application
      • Financial Dashboard
    • Challenges
      • Accessing data from disparate sources
      • Frequently changing data needs
      • Requirement to keep interface simple
      • Granular security model
      • Scalability for 25,000 users
    • Solution
      • Use Ipedo to aggregate data from multiple sources, filter using XML, and transform for delivery into HP’s internal portal,
    • Results
      • Makes new information available for real-time business decisions
    My Dashboard Financial Databases (Oracle) Data Warehouse (Hyperion Essbase) MS Word Analysis Reports Corporate Portal Framework Ipedo XIP Product View Region View Account View Channel View
  • Assurant - Insurance Policy Management
    • Application
      • Insurance Company: next-generation policy management system
    • Challenges
      • Manage large transaction volumes of policies in ACORD XML schema
      • Integrate with complex workflow processes using a service-oriented architecture (SOA)
      • Maintain uptime in high-availability environment
      • Solution
      • Use Ipedo to manage and integrate information from underwriting, accounting, claims processing and billing systems,
    • Results
      • Streamlines operations, improves response time to inquiries
    Billing Database Ipedo XIP Profitable Accounts Claims Status Expensive Claims Arrears Sales View Workflow View Claims View Acct. View Claims System Acct. System Web Services Policies ACORD Schema
  • Sun - Customer Service
    • Application
      • Customer Service
    • Challenges
      • Combining XML messages, Web Services, databases, other data
      • Connecting with external risk engine
      • Providing rapid response to inquiries
      • Managing huge data volumes
    • Solution
      • Embed Ipedo into Sun application to deliver risk analysis as a service
    • Results
      • Improves system uptime and availability and reduces incident volume and cost
    Customer Reference Data (Oracle) Risk Engine (Fair Isaac) System Information (XML) Preventive Services Ipedo XIP Hardware View Software View Account View Risk View Risk Assessment Advanced Diagnostics System Configurations Web Services JDBC
  • The Ipedo Difference
    • High Performance
      • Cost-based Query Optimization
      • Intelligent Caching
    • Investment Protection
      • Choice of data model: Relational (SQL) or XML (XQuery)
      • Choice of SOA deployment model – WSDL/SOAP or REST
      • One cohesive product
    • Seamless Deployment
      • Fully scriptable with complete SQL DDL support
      • Integrated Data Lineage and Impact Analysis reporting
      • Comprehensive APIs: Java, Web Services, .NET
    • Highly Scalable
      • Concurrency for hundreds of simultaneous users
      • Easily accommodates additional data sources
  • Additional Resources 2005 EII Survey Guide to EII ROI Guide
    • Background Slides
  • Enterprise Information Integration is…
    • … a new kind of virtual data integration technology that makes several remote data sources appear as one local database.
  • Ipedo XIP: Integration + Intelligence Vision: Intelligence without boundaries: Give business decision makers “information on demand, from anywhere, at any time” Product: Ipedo XIP integrates and manages information from disparate and complex data sources on demand
  • EII Generates Real Financial Benefits
    • Developer Productivity
      • EII allows developers to write to one, common interface to access multiple data sources, reducing the development cost for new applications
      • What % of your IT budget is spent on data integration development?
    • Analyst Utilization
      • EII allows analysts to simplify the data collection process and spend more time producing information and less time gathering data
      • How much time do your analysts spend collecting data?
    • Increased Flexibility
      • EII allows companies to respond quickly to changes in market conditions without incurring a major IT investment
      • What effort is involved to link new data sources to existing reports or applications?
    • Data Consolidation
      • Using EII to create a virtual centralized database can cut the cost of data maintenance, and reduce the need for data replication
      • How much does it cost to deploy each new data mart?
  • Integration Platform Built for Developers/Integrators Data and Cache Management • TTL • Performance • Versioning Publishing and API Access • XML • JSP Tag Lib • JDBC/ODBC • .NET • WS/SOA Transformation & Virtualization • XML Schema • CSV • SQL <-> XML • Pipelines Connectivity • Relational Views • XML Views • SOA/WS Views • JMS Feeds • HTTP Views Query Management • XQuery • SQL • Cost-based Optimizer Rules Processor • SQL SP • XQuery Modules • Java Functions Security • ACLs • Roles Management • JMX • JSP • APIs GUI Tools • View Builder • Query Builder • Rules Builder
  • EII Brings Advantages to New Projects - Create data services layer that simplifies SOA MDM - Maintain consistent view of core business entities - Avoid political issues around data ownership SOA - Allow universal info exchange as Web Services Solution Key Advantages of EII Dashboards - Intelligently combine company-wide information - Provide view customization per context or user Risk Management - Extract key information across company data silos - Reduce cost of compliance BI / Reporting - Enable better reports with up-to-date information - Access diverse data sources via queries, not code
    • Metadata Slides
  • Views Use Metadata Extensively Ipedo XIP View Output Schema Transformations Data Mappings Data Source Connections View Definitions of Fields Business Definitions Caching Policies Security Primary / Foreign Keys Security Data Source Metadata Source Repository Ipedo
  • Metadata Flows Throughout the Framework Metadata Manager Enterprise Data Sources Introspection Ipedo XIP Metadata Repositories Metadata Mgmt/Mod Tools Databases Web Services XML Doc Collections Other Data Sources Supported by Ipedo Modeling Tools BI/Reporting Tools SOA Tools App Dev Tools JDBC/ODBC/SQL WSDL/UDDI JDBC/ODBC/SQL WSDL/UDDI XMI JDBC/ODBC/SQL JDBC/ODBC/SQL XMI/Proprietary
    • Additional Customer Case Studies
  • Anhui Electric - Fraud and Risk Management
    • Application
      • Power Utility Billing Risk Management Dashboard
    • Challenges
      • Forecasting business risk
      • No aggregated meter monitoring data to prevent rampant theft of power
      • Lack of credit information on new customers
    • Solution
      • Use Ipedo to integrate energy meter collection system, customer databases and financial systems across multiple sites
    • Results
      • Significant reduction in fraud and theft; better management reporting
    Billing Database Customer Database Energy Meter Collection System Ipedo Credit View Theft View Risk View Forecast View
    • Application
      • Ring tone, music, and mobile game digital rights management platform
    • Challenges
      • Ability to address diverse content and transaction data types
      • Adapting rapidly to changing market and customer requirements
      • Scalability for millions of buyers and millions of content assets
    • Solution
      • Use Ipedo to integrate user, payment, and ring tone/game metadata for presentation through Web interface
    • Results
      • Allows rapid response to customers and easy integration of new content libraries into client Web sites
    Navio Card Navio Merchant Navio Store ASP Applications Report Server Title Manager Content Lockbox Digital Commerce Engine Ipedo XIP Ipedo XIP Ipedo XIP Navio - Rights-Based Commerce Web Storefront P2P Net
  • HP OpenView - Customer Service Portal
    • Application
      • Customer Service Portal
    • Challenges
      • Flexible search interface
      • Frequently changing content
      • Scalability for global users
      • High Availability
    • Solution
      • Use Ipedo to manage granular content and deliver in response to Web-based user inquiries
    • Results
      • Improve customer experience by directing users to relevant software downloads or reference materials
    Customer Service Marketing Databases (RDBMS) OpenView Web Site Ipedo XIP Product Info Customer Info Marketing Info
  • Pharma R&D Data Integration Ipedo Cuts Costs, Speeds Discovery BEFORE IPEDO AFTER IPEDO
    • Too many data sources
    • Unable to consolidate genomic data
    • Data sources in proprietary formats
    • Unable to analyze all genomic data
    • Scientists manually research genes
    • Scientists’ observations are not shared
    Ipedo XIP Observations Annotations
    • Deliver one-stop shop for research data
    • Capture scientists’ annotations
    • Keep scientists’ information synchronized
    • Views Integration
    • Query Analysis
    • Pipelining Presentation
    • “ 10x researcher productivity gain.”
    Internal Data Warehouse GenBank BLAST MedLine Internal Data Warehouse GenBank BLAST MedLine Scientists Scientists Research Application Web Site Web-Based Queries Subscription Web Site ROI in Time to Market Research Portal
  • British Telecom Price Book October 2003
    • Vertical Market Unique Needs
  • Financial Services Has Unique Needs
    • 360 degree view of the customer
    • See across systems in subsidiaries/acquisitions
    • Integrate data from different databases
    • Combine live and historical data
    • Satisfy regulatory requirements
    • Cross-divisional reporting
    • Granular security / data ownership
  • Insurance Has Unique Needs
    • Integrate underwriting, policy management, billing, claims systems
    • Perform ad-hoc reports
    • Leverage industry standard schemas
    • Produce reports for regulators
    • 360 degree view of the customer
    • Cross-divisional reporting
    • Post-merger/acquisition consolidation
    • Granular security / data ownership
  • Health Care Has Unique Needs
    • HIPAA compliance
    • Protect patient privacy - granular security model
    • Mix historical and real-time information
    • Customize reports for different uses: administration, insurance, treatment
    • 360 degree view of the patient
  • Retail Has Unique Needs
    • Generate real-time sales information
    • Track inventory across multiple locations
    • Share information with supply chain
    • Spot trends as they occur
    • 360 degree view of the customer
    • Cross-divisional reporting
    • Post-merger/acquisition consolidation
    • Granular security / data ownership
    • Business Intelligence
  • Business Intelligence and Reporting
    • Use Business Objects, Cognos, MicroStrategy, or any BI tools to analyze data from:
      • Relational databases
      • Unstructured data sources
      • Enterprise applications
      • Web Services
    • Combine historical information with real-time information from message queues or Web Services
  • Ipedo Supports BOBJ Three Ways Ipedo XIP BusinessObjects Universe Business Objects BI Platform Data Sources Data Mart
  • Ipedo Bridges Data Partitions
    • Application
      • Ad-hoc reporting across separate databases using BI tools
    • Challenges
      • Overcoming size limitation on Sybase servers
      • Shielding reports from changes in underlying database partitions
      • Combining data from disparate sources into a single “Universe”
      • Maintaining high-performance for complex queries
    • Solution
      • Use Ipedo to create virtual union views
      • BI tools connect to Ipedo views
      • Ipedo optimizes and federates queries to all databases
    Business Intelligence / Enterprise Reporting Tools Ipedo XIP All Customers View All Orders View All Sellers View ... Sybase1 Sybase2 Sybase10
  • Ipedo Appears as a Database to Business Intelligence Applications In this example, Business Objects Web Intelligence sees Ipedo as a single relational database. Ipedo is really pulling data from three different relational sources.
  • Ipedo Complements BOBJ
    • Data Integration
      • Real-time, query-based access to data sources
      • Combining web services with other data sources
      • Query optimization
      • Validation of data in real-time
    • Web Services
      • Blending multiple web services
      • Conditional invocation of web services in a specific order
      • Business rules checking on web services data
      • Caching of web services data for reuse
    • Master Data Management
  • Master Data Management
    • Create a master index that leverages Ipedo’s high performance engine
      • Cost-based query optimization
      • Union join pushdown
    • Reduce data replication
    • Customize departmental subsets of customer data to avoid data politics and ownership issues
    • Perform data cleansing (through Ipedo partnership)
    • Help improve the performance of the denormalization process and boost the flexibility in handling underlying schema changes
  • Ipedo XIP for MDM BI/Reporting Client ODBC / JDBC SQL SQL SQL Data Sources Remote Tables Relational View Join data from multiple RDBMS using Master Customer Key in MDM Ipedo Customer View Join View Union View Master Customer Key SQL SQL CRM Data (DB2) Trading Data (Sybase) Master Data (DB2)
    • Data Quality
  • Data Quality Enhances EII Data Source Mappings Virtual View Mappings User Query on View EII Step: Join DQ Call: Matching Join Key EII Step: Filter DQ Call: Matching Other Columns Data Sources Live Views (seconds of latency) User Query on View EII Step: Filter, Other DQ Call: Cleanse or Match Data Cached Views (minutes to hours of latency)
    • Caching Options:
    • Scheduled Cache (Push) – Include cleanse/match option for admin
    • User-driven Cache (Pull) – Include cleanse/match option after initial view
    • Aligning data between different sources for efficient join operations
    • Reconciling differences between data from different sources
    • Cleaning up cached information for subsequent reuse
    • Risk Management
  • Risk Management
    • Present complete set of information for analysis
      • Real-time feeds from message queues (trading data, interest rates, etc.)
      • Analyze complex XML documents (such as FpML or other trading schemas) and combine seamlessly with relational data
      • Cache XML for subsequent query, analysis, and integration
      • Compound Aggregate Indexes (CAI) track key data fields and automatically compute aggregates across different data feeds
    • Maintain audit trail for compliance and SOX
  • Ipedo for Risk Analysis Financial View Ipedo XIP JDBC ODBC SQL Convert to Relational Tables Calculate Aggregates Message Queue Message Queue XBRL FpML XML Caching BI/Reporting Client Trading Data In XML Format Order View Risk View
    • Enterprise Readiness
  • Ipedo Delivers Enterprise Readiness
    • Performance
      • Cost-based Query Optimization
      • Result-set Streaming
      • Policy-based Caching
      • Advanced Tuning
    • Scalability
      • Clustering
      • Concurrency
    • Security
    • Data Governance
  • Ipedo Joins Huge Data Sets… SELECT CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS, COUNT(ORDERS.O_ORDERKEY), SUM(ORDERS.O_TOTALPRICE ) FROM CUSTOMER, ORDERS WHERE (CUSTOMER.C_CUSTKEY=ORDERS.O_CUSTKEY) AND CUSTOMER.C_NAME = 'Customer#000000250' GROUP BY CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS
      • Select all ORDERS of a given CUSTOMER
    • ORDERS and TIME_DIM tables from Oracle (Orders Management App) 750,000 rows
    • CUSTOMER and NATION tables from SQL Server (CRM) 10 0 , 0 0 0 r o w s
  • …And Delivers Sub-Second Performance
    • Query Optimization:
      • Search predicate is pushed down to remote RDBMS.
      • Join predicate will be pushed into remote inner table if nested loop join is cost-effective.
      • Number of rows of the where clause is taken into consideration in Cost-Based Optimization (CBO).
    • Performance:
      • average query time = 0.1 sec
    • Query Plan:
  • Ipedo Security Protects Data and Views
    • User and Group Level Hierarchical Access Control List
    • Integration with external LDAP directories
    • Document/Attribute-level filtering (node-level)
    • JAAS-compliant encryption algorithm flexibility for 2-way secure authentication (from client, as well as server)
  • Low Overhead in IT Environment
    • Connection Pooling allows you to protect back-end data sources from excessive load
    • Intuitive setup and administration means no DBA required to run Ipedo
    • Runs on Windows, Linux, Solaris, HP-UX, AIX - any environment with a JVM
    • Runs on Tomcat (included), WebLogic, WebSphere, JBoss, JRocket
  • Professional Services Simplifies Implementation
    • Experienced Team
      • SQL, XML, Java, Web Services
    • Flexible Engagement Model
      • Off-site development, non-invasive setup (1-5 days)
      • On-site training for your team
    • Sample Deployments
      • Portal for global computer hardware company
      • Trading application for commodity exchange
      • Claims management system for insurance company
      • Risk management system for power utility
    • Ipedo Performance Testing
  • Ipedo XIP SQL Performance Testing
    • ORDERS and TIME_DIM tables from Oracle (Orders Management App)
    • CUSTOMER and NATION tables from SQL Server (CRM)
    100,000 rows 750,000 rows
    • Testing conducted in July 2005 on dual-CPU Intel XEON 2.2GHz with 2.0Gb memory
  • SQL Query #1 SELECT CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS, COUNT(ORDERS.O_ORDERKEY), SUM(ORDERS.O_TOTALPRICE ) FROM CUSTOMER, ORDERS WHERE (CUSTOMER.C_CUSTKEY=ORDERS.O_CUSTKEY) AND CUSTOMER.C_NAME = 'Customer#000000250' GROUP BY CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS
      • Select all ORDERS of a given CUSTOMER
  • Query Plan #1 and Performance
    • Query Optimization:
      • Search predicate is pushed down to remote RDBMS.
      • Join predicate will be pushed into remote inner table if nested loop join is cost-effective.
      • Number of rows of the where clause is taken into consideration in Cost-Based Optimization (CBO).
    • Performance:
      • average query time = 0.1 sec
    • Query Plan:
  • SQL Query #2 SELECT TIME_DIM.YEAR, COUNT(ORDERS.O_ORDERKEY), SUM(ORDERS.O_TOTALPRICE ) FROM TIME_DIM, ORDERS, CUSTOMER WHERE (ORDERS.O_ORDERDATE=TIME_DIM.NATIVEDATE) AND CUSTOMER.C_CUSTKEY=ORDERS.O_CUSTKEY) AND ( ( CUSTOMER.C_CUSTKEY ) = CASE WHEN char(hour(current_timestamp)) = '24' THEN 250 ELSE 251 END )) GROUP BY TIME_DIM.YEAR
      • Conditionally select a customer’s total order count and sum
  • Query Plan #2 and Performance
    • Query Optimization
      • ANSI SQL standard case-when-else conditional expression is pushed down to remote RDBMS.
      • Column transitivity is supported to allow better query plan.
        • e.g. (t1.c1 = t2.c2 and t2.c2 = constant)  (t1.c1=constant and t2.c2 = constant)
    • Performance:
      • average query time = 0.2 sec
    • Query plan:
  • SQL Query #3 SELECT V_MERGED_CUSTOMER.C_NATIONKEY, sum(V_MERGED_ORDERS.O_TOTALPRICE) FROM V_MERGED_CUSTOMER, V_MERGED_ORDERS WHERE (V_MERGED_CUSTOMER.C_CUSTKEY=V_MERGED_ORDERS.O_CUSTKEY) AND (V_MERGED_CUSTOMER.C_MKTSEGMENT = 'MACHINERY ‘) GROUP BY V_MERGED_CUSTOMER.C_NATIONKEY
      • Query efficiently across data partitions in Oracle and SQLServer:
        • View v_merged_customer unions customer tables in both partitions.
        • View v_merged_order unions order tables in both partitions.
  • Query Plan #3 and Performance
    • Query Optimization
      • Search predicates on views are pushed down to data sources
      • Joins across union partitions are regrouped and pushed down to data sources, so that smaller joined result sets are combined in XIP
    • Performance:
      • average query time = 2 sec
    • Query plan:
  • SQL Query #4
    • Query Optimization
      • Shows stability and scalability in handling large result set with high concurrency without running out of memory.
      • The first record is returned immediately without waiting for the entire result set to be processed.
      • Ipedo support cursor fetching all the way through from client to Ipedo and to remote RDBMS.
      • Query can be cancelled without fetching the entire result set, and resources are released immediately.
    SELECT ORDERS.O_ORDERKEY, ORDERS.O_ORDERDATE, ORDERS.O_CLERK FROM ORDERS
      • Select large result set (750,000 rows)
  • Query Performance #4
    • Cursor is used throughout the fetch chain to achieve high responsiveness:
      • Cursor is used from client to Ipedo:
        • Client obtains first row : 0.174s
      • Cursor is used from Ipedo to remote RDBMS:
        • Database returns first row in 0.171 s.
        • This shows cursor is used in back end when Ipedo fetches data from remote RDBMS.
    • Ipedo XIP Reference Architectures
  • Ipedo XIP Sizing Considerations
    • Number of Concurrent Users
    • Query Load
      • Number of queries
      • Complexity of queries (number of joins)
      • Amount of information being returned (row count)
    • Additional Processing
      • Data transformations (relational - XML)
      • Rules processing (number and complexity of rules)
    • High-Availability Requirements
      • Clustering (configuration)
      • Replication (writing or reading transaction logs)
  • Basic EII Server Load Concurrent users: < 75 Queries: < 50/sec Server Configuration CPUs: 1-4 RAM: 2 G Typical Usage Enable departmental reporting across multiple sources
  • Basic EII with Failover Ipedo XIP Primary Application-aware session pooling Ipedo XIP Secondary Server Load Concurrent users: < 75 Queries: < 50/sec Server Configuration CPUs: 1-4 RAM: 2 G Typical Usage Enable reporting across multiple sources for a business unit Ipedo can also use a clustering server for failover
  • HA Clustered - “Shared-Disk” Architecture Shared Disk Ipedo XIP Primary Ipedo XIP Secondary LAN Hub Private Network Windows Advanced Server Clustering Server Load Concurrent users: < 75/node Queries: < 50/sec/node Server Configuration CPUs: 1-4 per node RAM: 2 G per node Typical Usage Enable reporting across multiple sources for an enterprise or build an SOA data services layer Ipedo also supports clustering software on Linux, Solaris, HP-UX (HA = High Availability)
  • HA Clustered - “Shared-Nothing” Architecture SCSI Disk Ipedo XIP Primary Ipedo XIP Secondary LAN Hub Private Network Windows Advanced Server Clustering SCSI Disk Server Load Concurrent users: < 75/node Queries: < 50/sec/node Server Configuration CPUs: 1-4 per node RAM: 2 G per node Typical Usage Enable reporting across multiple sources for an enterprise or region, or build an SOA data layer Ipedo also supports clustering software on Linux, Solaris, HP-UX (HA = High Availability)
  • EII Server Farm (Clustered for Load Balancing) Ipedo XIP Ipedo XIP LAN Load Balancer Ipedo XIP Server Load Concurrent users: < 75/node Queries: < 50/sec/node Server Configuration CPUs: 1-4 per node RAM: 2 G per node Typical Usage Enable reporting across multiple sources for an enterprise or build an SOA data services layer