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Crossing the Mashup Chasm. Enterprise Mashup Requirements
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Crossing the Mashup Chasm. Enterprise Mashup Requirements

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Discussion at the Mashup Camp VI on March 20th, 2008. What´s the present and future of data mashups in an enterprise environment?

Discussion at the Mashup Camp VI on March 20th, 2008. What´s the present and future of data mashups in an enterprise environment?

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  • Full Name Full Name Comment goes here.
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  • Hi! So sorry I missed your question!!!! In a very basic way, ETL is more focused on batch-based processes, where the number of sources is typically low, and the amount of data to transfer is huge. Data Virtualization, on the other side, gets the most value when you need real-time data access and transformation, and/or the creation of a 'data access layer' which your business logic uses it to access data, therefore simplfying how corporate apps are done.
    You can find other presentations here or at denodo.com, and I am currently preparing a generic presentation called 'Why data virtualization' that I hope you find it interesting.

    And again, sorry for the delay!!!

    Justo
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  • how's your platform different from ETL?
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Crossing the Mashup Chasm. Enterprise Mashup Requirements Crossing the Mashup Chasm. Enterprise Mashup Requirements Presentation Transcript

  • Crossing the Mashup Chasm Requirements for Enterprise Mashups March 20th 2008 Justo Hidalgo VP Technology [email_address] Juan Lozano Principal Software Engineer [email_address] ashupCamp.com
  • What am I looking for in this discussion?
  • Agenda
    • Background
    • Web mashups and Enterprise Data Mashups
    • Requirement areas
    • Further thoughts
  • Background
    • Justo N. Hidalgo
    • VP Technology Denodo Technologies, Palo Alto
    • Ph.D. Computer Science in Query Optimization in Mediators of Web Sources
    • Interest in how Enterprise Data Mashups evolve.
    • Juan Lozano
    • Principal Software Engineer Denodo Technologies, Palo Alto
    • Our “web wrapper” guru 
    • Product (Denodo Platform 4.1)
    • Enterprise Data Mashups & Web Automation
      • Data Integration of Structured, Web and Unstructured Information
    • Technology Focus: Data Integration, Web 2.0 & Mashup Enabler
    • Targeted Solutions: Intelligence, Single View, Web Automation
    • Tenths of projects in the last 12 months to learn from
  • Web Mashups vs. Data Mashups
    • Web Mashups
    • Visual centric
    • Data relationships are simple
    • End-user driven enabled by APIs / tools
    • No QoS guarantees – as is
    • Simple Web security
    • Data Mashups
    • Data & Information centric
    • Deep transformations & semantic relationships
    • Interact with Enterprise ecosystem
    • Business analyst driven w/ user control options
    • Enterprise QoS- Query Optimization, Reliable, Failover, etc.
    • Policy-based security
    ENTERPRISE ECOSYSTEM Enterprise Infrastructure / Web Platform Enterprise & Web Applications RIA, Social Software, GUI, Portals
  • Mutual Benefits Web Mashups Data Mashups Enterprise Data Integration BPM ESB EAI ETL Data Warehouses Enterprise Infrastructure / Web Platform Enterprise & Web Applications RIA, Social Software, GUI, Portals ENTERPRISE ECOSYSTEM WEB MASHUPS DATA MASHUPS User experience / global access Enterprise-class features ?
  • Customer Uses of Enterprise Data Mashups 1. Aggregate Data 3. Exchange Data Enterprise Suppliers Customers 2. Enrich Data Apps Data Services
  • Requirement Areas: Mashups for Business Users / For Power Users and IT
    • User experience
    • Heterogeneous granularity for different levels
  • Requirement Areas: Enterprise-class features…
    • Security
      • LDAP access, user and role permission levels,
      • But also: Secure VPNs, secure communications, encryption
      • EXAMPLE: Financial Aggregation
    • HA/Scalability
      • Load balancing
      • Federated and distributed architectures
      • EXAMPLE: TELE-2/Vodafone Call Center
    • Performance
      • Asynchronous access
      • Parallelism
      • Execution Plan Optimization techniques
  • Requirement Areas: …Enterprise-class features
    • Data Transformations
      • Biopharma example: contextual summaries, patterns and regular expressions
      • LinkedForce example: textual similarity
    • Maintenance
      • Sources change… specially Web Sites!!!
    • Data Management… don´t get me started 
      • What are the boundaries between mashups and MDM, Data Quality tools?
  • Requirement Areas: Ecosystem SOURCES: WEB, PACKAGED APPS, MAINFRAME, … DATA QUALITY, METADATA, STANDARDS REST, SOAP, MQSERIES, ESB, JDBC, JSR-168
  • Further thoughts
    • “ Semantic mashups”:
      • Automatic schema matching
      • Automatic Browsing and Information Structuring
    • “ Real Time” mashups
      • I mean… HARD REAL TIME.
  • Thank You! Justo Hidalgo, VP Technology Email [email_address] Juan Lozano, Principal Software Engineer Email [email_address] http://www.denodo.com