SlideShare ist ein Scribd-Unternehmen logo
1 von 32
Downloaden Sie, um offline zu lesen
DataPortal - Database Sharing In the Cloud
     A simpler way to transfer complex data



                              Connection Concepts, Inc.
                           2141 Industrial Parkway, Suite 202
                          Silver Spring, MD, 20904-7824, USA
                                      860 729-3117
                                      con2inc.com
                            data-portal.biz ; directtodb.com
                           Contact: Gary Whitten, Pres., CIO
                                whitteng@con2inc.com
Data Sharing Today
Sharing structured, complex data                        (e.g. Relation Database Data)
 is too burdensome

  Current Options:
     Simple: Convert to flat files Difficult: Legacy EDI or similar
       Structure, Information and                         (Electronic Data Interchange)
       Value is lost                             • Requires development
                                                 • Rigid, Inflexible
                                                 • Requires Conversion for data Provider
                                                   AND Consumer
Ease of
Transfer
                                                 • Requires Firewall Modification
                           Practical
                           Limit




           Data Complexity/ Value      Goal: “YouTube for data”
                                        ●
                                          Push-button sharing of complex data over the Web
                                        ●
                                          Agile - Instant, Effortless Publishing, Retrieval

                                                                                           2
Database Sharing Needs

Experts call for sharing data in its original form:
  (from CNET January 27, 2009 http://news.cnet.com/8301-13739_3-10150699-46.html)
        CNE

 David Robinson
  Associate Director of the Center for Information Technology Policy at Princeton University:
 "(no) one person or organization could possibly anticipate all the ways that Americans will want to analyze,
 reuse, or cross-reference the information that Recovery.gov will offer. And no one person or organization
 needs to do so, as long as the data itself is readily available."

 In 2008, Robinson and his colleagues at Princeton published a paper calling for the government to provide open
 access to the raw data used by all federal Web sites. The highly influential paper has been widely circulated
 among technology policy circles in recent months.


 Jim Harper
  Director of Information Policy Studies at the Cato Institute:
  ...the entire back-end database should be made available.
 "This is a little tricky, because people have to settle on a format, and then require submissions in that format from
 contractors and state and local entities, etc.,"




                                                                                                                     3
Database Sharing Needs
Experts call for easier, more agile data sharing:
  From Information Week, June 21, 2010, cover story (page 26) “Share”,
   “Why IT Needs To Push Data Sharing Efforts”
   http://www.informationweek.com/news/services/integration/showArticle.jhtml?articleID=225700546



... one of the single best things (IT departments) can do for their businesses is enable effective data sharing. … Yet
data sharing, particularly automated systems that give your external business partners access to your data when they
want it, are not ubiquitous or easy, and the level of data sharing of any kind is surprisingly low.


                                                *******************************


When it comes to what frustrates data sharing efforts, the classic culprit, budget limitations, tops the list of survey
respondents, followed by complaints about the multiple sets of tools and the care and feeding required by legacy
connections.


                                                *******************************


A bigger problem is if IT simply doesn't have the ability to respond quickly to new requests. Too many companies
can't move fast enough.



                                                                                                                          4
Database Sharing Today

Need for more agile Supply Chain management

Real world example - Sikorsky Aircraft Company:
  Resistance from potential new suppliers to burdensome
  EDI process to share order data




                                                          5
DataPortal
                     Database Sharing In the Cloud

DataPortal transfers business data
in database form over the Web
 • No development                            • SSL encryption
 • Works across database vendors             • Multiple layers of password protection
 • Minimal setup and configuration           • No firewall modification
   (no manual installation for Web client)
 • Serves a wide audience                    • Data NOT exposed directly to network
 • Instant database migration                • No unnecessary conversion at either end
                                             • Maintains database value and complexity

Appropriate when source and destination data reside in a
relational database system


             DataPortal - “Push-button” solution for instantly
                sharing complex database data in the Cloud


                                                                                    6
DataPortal Operation


                                       Client
                  DataPortal Client             DataPortal Server
                                      Request
   Database
                       Host                          Host           Database
    System                                                           System

                      DataPortal       Web          DataPortal
                        Client                       Server
                                      HTTP




Client requests a DataSource
            (published database)




                                                                               7
DataPortal Operation


           DataPortal Client    Server          DataPortal Server
                Host           Response              Host
Database                                                                       Database
 System                                                                         System

               DataPortal       Web                   DataPortal
                 Client                                Server
                                HTTP




                                          Snapshot of requested DataSource
                                          is transferred over the Web, through
                                          unmodified firewall, to user's preferred
                                          database system
                                          available for immediate processing
                                          using existing infrastructure




                                                                                          8
DataPortal Write Data Modes


   Original    Data to be          Original     Data to be   Original   Data to be
    Data        Written             Data         Written      Data       Written




       Replace                                Edit                Append
       Database




         Resulting                       Resulting                 Resulting
           Data                            Data                      Data


Creates and populates                Writes new data         Appends new data
new database – replaces              over old                After old
Any pre-existing database
with same name



                                                                                     9
Shared Data Selection Options

Shared Data Selection Options

 • Share Full Database Structure and Content

 • Share Only Selected Tables

 • Share Data Filtered by Authenticated ID




                                                   10
Data Sharing Usage Example

Broadcast – central database is distributed
            to many partners (e.g. - Replace Database mode)


                DataPortal   DataPortal    DataPortal
                  Client       Client        Client




                DataPortal   DataPortal    DataPortal
                  Client      Server         Client




                DataPortal   DataPortal    DataPortal
                  Client       Client        Client



                                                              11
Data Sharing Usage Example

Merge and Integrate – many partners add data
                      to central database (e.g. - Append mode)


               DataPortal    DataPortal   DataPortal
                Server        Server       Server




               DataPortal    DataPortal   DataPortal
                Server         Client      Server




               DataPortal    DataPortal   DataPortal
                Server        Server       Server



                                                                 12
DataPortal Security

DataPortal Security
• Uses Web standard SSL (Secure Socket Layer) for
    • Encryption
    • Authentication
    • Other Web security standards can be applied when available

• Mulitple levels of password authentication
    • Web level
    • Application level
    • Database level

• Data filtering based on authenticated ID

• Database never exposed to network (stays behind firewall)




                                                                   13
DataPortal Applet Client

 DataPortal Client
                      ●   No installation required
within Web browser
                      ●   Uses standard Java-enabled
                          Web browser


                          Usage:
                           ●   URL selects DataPortal Server
                           ●   Selection Fields
                                  DataSource
                                  Data Destination
                                  DB Vendor
                                  Host/Port
                                  User/Password
                                  Initial DB (if required)
                                  New DB Name
                                  Info/Status Display




                      ** standalone client also available **
                                                               14
DataPortal Standalone
                            Client Application
   DataPortal Client          ●   Includes all functionality of Web client
Standalone Application
  Transfer Control View
                              ●   Defines and saves data transfers
                              ●   Web listener transfers data based on authenticated
                                  Web requests from programs or browsers

                                                DataPortal Client
                                             Standalone Application
                                                  Listener View




                                                                                   15
DataPortal Server Management
               Publishing a DataSource


Add DataSource Web Form   DataSource Form Parameters
                          DB Vendor Type

                          Host/Port

                          DB Name
                          DataSource Name
                           (as seen by client)

                          Database User/Password

                          Min/Max Number of Connections

                          Require Client User/Password
                           for DataSource access



                                                         16
DataPortal Security Design

DataPortal Security Diagram
                 Manual
               Submission



                                                                       Standard                 SSL
                 Applet
 RDBMS            GUI                                                  Data Path              Data Path



               DataPortal                                                                             RDBMS
                 Client

                                             Programatic
                                                                          DataPortal Server
            DataPortal Client                   URL
                                               Request                         Host
                Applet                 Manual               Web Form
                                      (Browser)               URL             DataPortal
                    Manual           Submission              Request           Server
                  Submission




                  Application                HTTP/SSL
                     GUI                     Listerner(s)
 RDBMS




                                DataPortal
                                  Client


                       DataPortal Client
                         Application
                                                                                                              17
DataPortal Data Transfer Integrity:
                            Implicit Verification

    DataPortal Transfer Integrity – Implicit Verfication
●   DataPortal transfer requires many Java component actions
●   Each action is monitored for material exceptions
●   Data corruption without exception is highly unlikely
    (supported by testing)

●   Transfer is treated atomically - If any action throws any material
    exception, the entire transfer is aborted
    – Transfer results reported
    –   Transfer can be repeated, in its entirety, until successful



                                                                      18
DataPortal Data Transfer Integrity:
                          Explicit Verification

    DataPortal Transfer Integrity – Explicit Verification

●   Element Size at Destination is compared to Size at Source – can be
    done inline, not currently included in standard transfer (overhead not
    justified)
●   DataPortal infrastructure is used to compare structure and data of two
    DataBases, across DB vendors – Compare 2 DB separate utility




                                                                             19
DataPortal Data Transfer Integrity:
                    Database Compare Utility
DataPortal infrastructure is used to compare structure and data of
  two DataBases, across DB vendors


           Example:
           In a DB of ~150 MBs, an improper handling of the “ ' ”
             character by the database was discovered




                                                                    20
DataPortal Deployment


                         DataPortal Deployed by loading
DataPortal Server
     Host
                         WAR (Web ARchive) File into
                         Servlet Engine
   Web Server
                         Client Applet JAR (Java Archive) File
  Servlet Engine
                         is contained within WAR File
    WAR File
    (Server)             Client is automatically downloaded and
                         installed into Web browser
     JAR File
      (Client)




                                                                 21
DataPortal Demo




                  22
Patented DataPortal Technology

DataPortal IP Status:
Connection Concepts awarded
U.S. Patent 7,346,635 for DataPortal technology
Connection Concepts owns patent




                                                  23
DataPortal Advantage

 • With DataPortal, data is read/written directly from/to
      database without conversion to intermediate formats

 • More complex, high value data can be transferred with the
   ease of a simple flat file
                                                                                                  Ease of
                   Traditional Database Data Transfer Over the Web                                Transfer
                                                                                                                             Practical
                                                                                                                             Limit
              Export                                                 Setup
              Convert                                                Convert
              Dilute                                                 Import
Database                 CSV,                              CSV,                Database
Data Source              XML...                            XML...              Data Destination
                                                                                                             Data Complexity/ Value
                                         Web

         Direct Database Data Transfer Over the Web with DataPortal
                                                                                                                    DataPortal
                                      DataPortal                                                  Ease of
                                                                                                  Transfer
                                                                                                                             Practical
                                                                                                                             Limit


Database                                                                       Database
Data Source                                                                    Data Destination

                                         Web
                                                                                                             Data Complexity/ Value


                                                                                                                                  24
DataPortal Value

Users can share complex database
 data over the Web...
●   directly from/to their databases (provider and consumer) without
    conversion to intermediate forms
●   immediately
●   effortlessly (“push-button” ease)
●   without development effort or need for programmers
●   directly from the browser so consumer does not need to install
    software
●   without limit on data complexity (value)
     e.g. tables, primary/foreign keys, images, docs...
●   while serving a wide audience
●   no firewall modification

    Complex data can be shared among millions of users within minutes
                                                                        25
Standards-Based Solution

DataPortal uses standards based technologies
●   Relational Database Systems – standard for storing data
      (e.g. Oracle, DB2, Access, MySQL, SQL Server...)
●   Web
●   SQL (Structured Query Language)
●   Standard port – no firewall modification
●   Client runs in browser – no software installation
●   SSL (Secure Socket Layer) for authentication and encryption
●   Java
●   Server deployed as Java Servlet within Web server
●   Java Applet runs in Web browser
●   JDBC – Java standard for database connectivity

                                                                  26
DataPortal Applications

DataPortal applications
    DataPortal is a potential solution whenever parties want to
    quickly and easily share database data
●   Many existing or new EDI applications (e.g. Supply-chain management,
    Order Processing or Status reporting...)
●   Putting catalog, product and pricing info on the Web
●   Supplying sales force with current data in the field
●   Government data sharing
        ●   Providing users original data for data-centric public facing Web sites (data.gov,
              usaspending.gov, recovery.gov..)
        ●   Public data reporting to government (e.g Recovery funds usage reporting)
        ●   Inter-agency/department data sharing



            DataPortal Potential:
             Standard for Sharing Business and Government Data


                                                                                                27
DataPortal Design

                                                                                              DB      Database
                  Abstract DB Structure, Data                                                Vendor
                                                                                              “A”
                Representation Serialized Objects            DataPortal Server
                          (DB Vendor Neutral)                     Host
   To
                                                                                 DB Structure, Data
DataPortal                                                       DataPortal          Requests
  Client                                                          Server
                                                                                 DB Structure, Data
                                                                                        Info




                       DB Vendor “B”

    Database

                                       DataPortal Client
               Create DB, Tables            Host

                                                                                                            from
                                           DataPortal
                                             Client                                                       DataPortal
                Populate Tables                                                                            Server




                                                                                                                  28
Competitive Advantages


Non-Web based competition:
EDI (Legacy), Database Development Tools
●   Difficult to use – intended for programmers
●   Restrictive – must conform to specific formats or database
    vendors/versions
●   Requires firewall modification,
      network programing
●   Client must install software
●   Must add in security and authentication
●   Persistent connections use network resources
●   Intended for narrow audience


                                                                 29
Competitive Advantages

Web based competition
●   CSV, Excel files
                                                  Ease of
     – Requires conversion                        Transfer


     – Limits complexity/value of data                            DataPortal

                                            CSV

●   XML                                     XML
                                                                               Practical
                                                                               Limit


     – Requires conversion
                                            API

     – Requires programming
     – Limits complexity/value of data
                                                             Data Complexity/ Value

     – Inefficient – high data overhead

●   API
     – Requires programming, expertise, time, effort

                                                                                           30
Where Can DataPortal Be Used?

Provide Standard Tool for Sharing Complex, Structured Data
 Within or Across:
● Departments
● Offices
● Partners
● Suppliers
● Customers
● Sales Force
● Government (Federal, State, City, Local)
● …..




                                                             31
Database Sharing In the Cloud
         A simpler way to transfer complex data




Contact Information:

Connection Concepts, Inc.
2141 Industrial Parkway, Suite 202
Silver Spring, MD, 20904-7824, USA

http://data-portal.biz ; http://directtodb.com
             http://con2inc.com

Gary Whitten, President., CIO
whitteng@con2inc.com
860 729-3117

Weitere ähnliche Inhalte

Was ist angesagt?

Empower Splunk and other SIEMs with the Databricks Lakehouse for Cybersecurity
Empower Splunk and other SIEMs with the Databricks Lakehouse for CybersecurityEmpower Splunk and other SIEMs with the Databricks Lakehouse for Cybersecurity
Empower Splunk and other SIEMs with the Databricks Lakehouse for CybersecurityDatabricks
 
Scalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4j
Scalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4jScalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4j
Scalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4jNeo4j
 
Big Data on Public Cloud
Big Data on Public CloudBig Data on Public Cloud
Big Data on Public CloudIMC Institute
 
Big Data Analytics MIS presentation
Big Data Analytics MIS presentationBig Data Analytics MIS presentation
Big Data Analytics MIS presentationAASTHA PANDEY
 
Big Data Analytics for Real Time Systems
Big Data Analytics for Real Time SystemsBig Data Analytics for Real Time Systems
Big Data Analytics for Real Time SystemsKamalika Dutta
 
Analysis of big data in pandemic case
Analysis of big data in pandemic case Analysis of big data in pandemic case
Analysis of big data in pandemic case Muh Saleh
 
MULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKS
MULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKSMULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKS
MULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKSI3E Technologies
 
Applying Noisy Knowledge Graphs to Real Problems
Applying Noisy Knowledge Graphs to Real ProblemsApplying Noisy Knowledge Graphs to Real Problems
Applying Noisy Knowledge Graphs to Real ProblemsDataWorks Summit
 
Big Data Use Cases
Big Data Use CasesBig Data Use Cases
Big Data Use Casesboorad
 
Big Tools for Big Data
Big Tools for Big DataBig Tools for Big Data
Big Tools for Big DataLewis Crawford
 
Leveraging a big data model in the IT domain
Leveraging a big data model in the IT domainLeveraging a big data model in the IT domain
Leveraging a big data model in the IT domainVSS Monitoring
 
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Geoffrey Fox
 
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...LeMeniz Infotech
 
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...ieeepondy
 
Integrating scientific laboratories into the cloud
Integrating scientific laboratories into the cloudIntegrating scientific laboratories into the cloud
Integrating scientific laboratories into the cloudData Finder
 

Was ist angesagt? (20)

Empower Splunk and other SIEMs with the Databricks Lakehouse for Cybersecurity
Empower Splunk and other SIEMs with the Databricks Lakehouse for CybersecurityEmpower Splunk and other SIEMs with the Databricks Lakehouse for Cybersecurity
Empower Splunk and other SIEMs with the Databricks Lakehouse for Cybersecurity
 
Bigdata " new level"
Bigdata " new level"Bigdata " new level"
Bigdata " new level"
 
Scalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4j
Scalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4jScalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4j
Scalability and Graph Analytics with Neo4j - Stefan Kolmar, Neo4j
 
Big Data on Public Cloud
Big Data on Public CloudBig Data on Public Cloud
Big Data on Public Cloud
 
Machine Data Analytics
Machine Data AnalyticsMachine Data Analytics
Machine Data Analytics
 
Big Data Analytics MIS presentation
Big Data Analytics MIS presentationBig Data Analytics MIS presentation
Big Data Analytics MIS presentation
 
Big Data Analytics for Real Time Systems
Big Data Analytics for Real Time SystemsBig Data Analytics for Real Time Systems
Big Data Analytics for Real Time Systems
 
Big data tools
Big data toolsBig data tools
Big data tools
 
Analysis of big data in pandemic case
Analysis of big data in pandemic case Analysis of big data in pandemic case
Analysis of big data in pandemic case
 
MULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKS
MULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKSMULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKS
MULTILAYER BIG DATA ARCHITECTURE FOR REMOTE SENSING IN EOLIC PARKS
 
Applying Noisy Knowledge Graphs to Real Problems
Applying Noisy Knowledge Graphs to Real ProblemsApplying Noisy Knowledge Graphs to Real Problems
Applying Noisy Knowledge Graphs to Real Problems
 
View on big data technologies
View on big data technologiesView on big data technologies
View on big data technologies
 
Big Data Use Cases
Big Data Use CasesBig Data Use Cases
Big Data Use Cases
 
Big Tools for Big Data
Big Tools for Big DataBig Tools for Big Data
Big Tools for Big Data
 
Leveraging a big data model in the IT domain
Leveraging a big data model in the IT domainLeveraging a big data model in the IT domain
Leveraging a big data model in the IT domain
 
Real time bi solution architecture
Real time bi solution architectureReal time bi solution architecture
Real time bi solution architecture
 
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...
 
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
 
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...
 
Integrating scientific laboratories into the cloud
Integrating scientific laboratories into the cloudIntegrating scientific laboratories into the cloud
Integrating scientific laboratories into the cloud
 

Ähnlich wie DataPortal Presentation

Modern Data Management for Federal Modernization
Modern Data Management for Federal ModernizationModern Data Management for Federal Modernization
Modern Data Management for Federal ModernizationDenodo
 
Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4Shawn D'souza
 
Apache Kafka and the Data Mesh | Ben Stopford and Michael Noll, Confluent
Apache Kafka and the Data Mesh | Ben Stopford and Michael Noll, ConfluentApache Kafka and the Data Mesh | Ben Stopford and Michael Noll, Confluent
Apache Kafka and the Data Mesh | Ben Stopford and Michael Noll, ConfluentHostedbyConfluent
 
Evolution from EDA to Data Mesh: Data in Motion
Evolution from EDA to Data Mesh: Data in MotionEvolution from EDA to Data Mesh: Data in Motion
Evolution from EDA to Data Mesh: Data in Motionconfluent
 
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization Denodo
 
Apache Kafka and the Data Mesh | Michael Noll, Confluent
Apache Kafka and the Data Mesh | Michael Noll, ConfluentApache Kafka and the Data Mesh | Michael Noll, Confluent
Apache Kafka and the Data Mesh | Michael Noll, ConfluentHostedbyConfluent
 
Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案Etu Solution
 
Big Data Session Presentations
Big Data Session PresentationsBig Data Session Presentations
Big Data Session PresentationsePSI Platform
 
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data Virtualization
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data VirtualizationMyth Busters III: I’m Building a Data Lake, So I Don’t Need Data Virtualization
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data VirtualizationDenodo
 
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)Denodo
 
Data Virtualization: Introduction and Business Value (UK)
Data Virtualization: Introduction and Business Value (UK)Data Virtualization: Introduction and Business Value (UK)
Data Virtualization: Introduction and Business Value (UK)Denodo
 
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)Denodo
 
Best Practices in the Cloud for Data Management (US)
Best Practices in the Cloud for Data Management (US)Best Practices in the Cloud for Data Management (US)
Best Practices in the Cloud for Data Management (US)Denodo
 
Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...
Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...
Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...Denodo
 
Apache Kafka® and the Data Mesh
Apache Kafka® and the Data MeshApache Kafka® and the Data Mesh
Apache Kafka® and the Data MeshConfluentInc1
 
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)Denodo
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An IntroductionDenodo
 
Data Virtualization: An Essential Component of a Cloud Data Lake
Data Virtualization: An Essential Component of a Cloud Data LakeData Virtualization: An Essential Component of a Cloud Data Lake
Data Virtualization: An Essential Component of a Cloud Data LakeDenodo
 
Denodo DataFest 2016: The Role of Data Virtualization in IoT Integration
Denodo DataFest 2016: The Role of Data Virtualization in IoT IntegrationDenodo DataFest 2016: The Role of Data Virtualization in IoT Integration
Denodo DataFest 2016: The Role of Data Virtualization in IoT IntegrationDenodo
 

Ähnlich wie DataPortal Presentation (20)

Modern Data Management for Federal Modernization
Modern Data Management for Federal ModernizationModern Data Management for Federal Modernization
Modern Data Management for Federal Modernization
 
Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4
 
Apache Kafka and the Data Mesh | Ben Stopford and Michael Noll, Confluent
Apache Kafka and the Data Mesh | Ben Stopford and Michael Noll, ConfluentApache Kafka and the Data Mesh | Ben Stopford and Michael Noll, Confluent
Apache Kafka and the Data Mesh | Ben Stopford and Michael Noll, Confluent
 
Evolution from EDA to Data Mesh: Data in Motion
Evolution from EDA to Data Mesh: Data in MotionEvolution from EDA to Data Mesh: Data in Motion
Evolution from EDA to Data Mesh: Data in Motion
 
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization
 
Apache Kafka and the Data Mesh | Michael Noll, Confluent
Apache Kafka and the Data Mesh | Michael Noll, ConfluentApache Kafka and the Data Mesh | Michael Noll, Confluent
Apache Kafka and the Data Mesh | Michael Noll, Confluent
 
Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案
 
Big Data Session Presentations
Big Data Session PresentationsBig Data Session Presentations
Big Data Session Presentations
 
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data Virtualization
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data VirtualizationMyth Busters III: I’m Building a Data Lake, So I Don’t Need Data Virtualization
Myth Busters III: I’m Building a Data Lake, So I Don’t Need Data Virtualization
 
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
Simplifying Your Cloud Architecture with a Logical Data Fabric (APAC)
 
Data Virtualization: Introduction and Business Value (UK)
Data Virtualization: Introduction and Business Value (UK)Data Virtualization: Introduction and Business Value (UK)
Data Virtualization: Introduction and Business Value (UK)
 
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
 
Best Practices in the Cloud for Data Management (US)
Best Practices in the Cloud for Data Management (US)Best Practices in the Cloud for Data Management (US)
Best Practices in the Cloud for Data Management (US)
 
Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...
Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...
Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...
 
Apache Kafka® and the Data Mesh
Apache Kafka® and the Data MeshApache Kafka® and the Data Mesh
Apache Kafka® and the Data Mesh
 
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
Logical Data Lakes: From Single Purpose to Multipurpose Data Lakes (APAC)
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An Introduction
 
Data Virtualization: An Essential Component of a Cloud Data Lake
Data Virtualization: An Essential Component of a Cloud Data LakeData Virtualization: An Essential Component of a Cloud Data Lake
Data Virtualization: An Essential Component of a Cloud Data Lake
 
Kurukshetra - Big Data
Kurukshetra - Big DataKurukshetra - Big Data
Kurukshetra - Big Data
 
Denodo DataFest 2016: The Role of Data Virtualization in IoT Integration
Denodo DataFest 2016: The Role of Data Virtualization in IoT IntegrationDenodo DataFest 2016: The Role of Data Virtualization in IoT Integration
Denodo DataFest 2016: The Role of Data Virtualization in IoT Integration
 

Kürzlich hochgeladen

Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...
Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...
Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...aamir
 
Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...
Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...
Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...Riya Pathan
 
👙 Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Service
👙  Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Service👙  Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Service
👙 Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Serviceanamikaraghav4
 
Call Girls Agency In Goa 💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...
Call Girls  Agency In Goa  💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...Call Girls  Agency In Goa  💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...
Call Girls Agency In Goa 💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...russian goa call girl and escorts service
 
👙 Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Service
👙  Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Service👙  Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Service
👙 Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Serviceanamikaraghav4
 
Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...
Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...
Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...Riya Pathan
 
Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...
Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...
Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...ritikasharma
 
Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...
Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...
Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...rahim quresi
 
Top Rated Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...
Top Rated  Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...Top Rated  Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...
Top Rated Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...Call Girls in Nagpur High Profile
 
Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034 Independent Chenna...
Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034  Independent Chenna...Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034  Independent Chenna...
Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034 Independent Chenna... Shivani Pandey
 
Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...ritikasharma
 
Call Girl Nagpur Roshni Call 7001035870 Meet With Nagpur Escorts
Call Girl Nagpur Roshni Call 7001035870 Meet With Nagpur EscortsCall Girl Nagpur Roshni Call 7001035870 Meet With Nagpur Escorts
Call Girl Nagpur Roshni Call 7001035870 Meet With Nagpur EscortsCall Girls in Nagpur High Profile
 
Call Girls In Goa 9316020077 Goa Call Girl By Indian Call Girls Goa
Call Girls In Goa  9316020077 Goa  Call Girl By Indian Call Girls GoaCall Girls In Goa  9316020077 Goa  Call Girl By Indian Call Girls Goa
Call Girls In Goa 9316020077 Goa Call Girl By Indian Call Girls Goasexy call girls service in goa
 
Call Girls in Barasat | 7001035870 At Low Cost Cash Payment Booking
Call Girls in Barasat | 7001035870 At Low Cost Cash Payment BookingCall Girls in Barasat | 7001035870 At Low Cost Cash Payment Booking
Call Girls in Barasat | 7001035870 At Low Cost Cash Payment Bookingnoor ahmed
 
↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...
↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...
↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...noor ahmed
 

Kürzlich hochgeladen (20)

Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...
Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...
Nayabad Call Girls ✔ 8005736733 ✔ Hot Model With Sexy Bhabi Ready For Sex At ...
 
Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...
Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...
Independent Sonagachi Escorts ✔ 9332606886✔ Full Night With Room Online Booki...
 
👙 Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Service
👙  Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Service👙  Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Service
👙 Kolkata Call Girls Shyam Bazar 💫💫7001035870 Model escorts Service
 
Call Girls Agency In Goa 💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...
Call Girls  Agency In Goa  💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...Call Girls  Agency In Goa  💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...
Call Girls Agency In Goa 💚 9316020077 💚 Call Girl Goa By Russian Call Girl ...
 
👙 Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Service
👙  Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Service👙  Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Service
👙 Kolkata Call Girls Sonagachi 💫💫7001035870 Model escorts Service
 
Russian ℂall gIRLS In Goa 9316020077 ℂall gIRLS Service In Goa
Russian ℂall gIRLS In Goa 9316020077  ℂall gIRLS Service  In GoaRussian ℂall gIRLS In Goa 9316020077  ℂall gIRLS Service  In Goa
Russian ℂall gIRLS In Goa 9316020077 ℂall gIRLS Service In Goa
 
CHEAP Call Girls in Malviya Nagar, (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in  Malviya Nagar, (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICECHEAP Call Girls in  Malviya Nagar, (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Malviya Nagar, (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
 
Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...
Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...
Independent Hatiara Escorts ✔ 9332606886✔ Full Night With Room Online Booking...
 
Goa Call Girls 9316020077 Call Girls In Goa By Russian Call Girl in goa
Goa Call Girls 9316020077 Call Girls  In Goa By Russian Call Girl in goaGoa Call Girls 9316020077 Call Girls  In Goa By Russian Call Girl in goa
Goa Call Girls 9316020077 Call Girls In Goa By Russian Call Girl in goa
 
Call Girls South Avenue Delhi WhatsApp Number 9711199171
Call Girls South Avenue Delhi WhatsApp Number 9711199171Call Girls South Avenue Delhi WhatsApp Number 9711199171
Call Girls South Avenue Delhi WhatsApp Number 9711199171
 
Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...
Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...
Book Sex Workers Available Kolkata Call Girls Service Airport Kolkata ✔ 62971...
 
Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...
Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...
Science City Kolkata ( Call Girls ) Kolkata ✔ 6297143586 ✔ Hot Model With Sex...
 
Top Rated Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...
Top Rated  Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...Top Rated  Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...
Top Rated Pune Call Girls Pimpri Chinchwad ⟟ 6297143586 ⟟ Call Me For Genuin...
 
Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034 Independent Chenna...
Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034  Independent Chenna...Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034  Independent Chenna...
Verified Trusted Call Girls Tambaram Chennai ✔✔7427069034 Independent Chenna...
 
Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
Top Rated Kolkata Call Girls Khardah ⟟ 6297143586 ⟟ Call Me For Genuine Sex S...
 
Call Girl Nagpur Roshni Call 7001035870 Meet With Nagpur Escorts
Call Girl Nagpur Roshni Call 7001035870 Meet With Nagpur EscortsCall Girl Nagpur Roshni Call 7001035870 Meet With Nagpur Escorts
Call Girl Nagpur Roshni Call 7001035870 Meet With Nagpur Escorts
 
Call Girls In Goa 9316020077 Goa Call Girl By Indian Call Girls Goa
Call Girls In Goa  9316020077 Goa  Call Girl By Indian Call Girls GoaCall Girls In Goa  9316020077 Goa  Call Girl By Indian Call Girls Goa
Call Girls In Goa 9316020077 Goa Call Girl By Indian Call Girls Goa
 
Call Girls in Barasat | 7001035870 At Low Cost Cash Payment Booking
Call Girls in Barasat | 7001035870 At Low Cost Cash Payment BookingCall Girls in Barasat | 7001035870 At Low Cost Cash Payment Booking
Call Girls in Barasat | 7001035870 At Low Cost Cash Payment Booking
 
Desi Bhabhi Call Girls In Goa 💃 730 02 72 001💃desi Bhabhi Escort Goa
Desi Bhabhi Call Girls  In Goa  💃 730 02 72 001💃desi Bhabhi Escort GoaDesi Bhabhi Call Girls  In Goa  💃 730 02 72 001💃desi Bhabhi Escort Goa
Desi Bhabhi Call Girls In Goa 💃 730 02 72 001💃desi Bhabhi Escort Goa
 
↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...
↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...
↑Top Model (Kolkata) Call Girls Rajpur ⟟ 8250192130 ⟟ High Class Call Girl In...
 

DataPortal Presentation

  • 1. DataPortal - Database Sharing In the Cloud A simpler way to transfer complex data Connection Concepts, Inc. 2141 Industrial Parkway, Suite 202 Silver Spring, MD, 20904-7824, USA 860 729-3117 con2inc.com data-portal.biz ; directtodb.com Contact: Gary Whitten, Pres., CIO whitteng@con2inc.com
  • 2. Data Sharing Today Sharing structured, complex data (e.g. Relation Database Data) is too burdensome Current Options: Simple: Convert to flat files Difficult: Legacy EDI or similar Structure, Information and (Electronic Data Interchange) Value is lost • Requires development • Rigid, Inflexible • Requires Conversion for data Provider AND Consumer Ease of Transfer • Requires Firewall Modification Practical Limit Data Complexity/ Value Goal: “YouTube for data” ● Push-button sharing of complex data over the Web ● Agile - Instant, Effortless Publishing, Retrieval 2
  • 3. Database Sharing Needs Experts call for sharing data in its original form: (from CNET January 27, 2009 http://news.cnet.com/8301-13739_3-10150699-46.html) CNE David Robinson Associate Director of the Center for Information Technology Policy at Princeton University: "(no) one person or organization could possibly anticipate all the ways that Americans will want to analyze, reuse, or cross-reference the information that Recovery.gov will offer. And no one person or organization needs to do so, as long as the data itself is readily available." In 2008, Robinson and his colleagues at Princeton published a paper calling for the government to provide open access to the raw data used by all federal Web sites. The highly influential paper has been widely circulated among technology policy circles in recent months. Jim Harper Director of Information Policy Studies at the Cato Institute: ...the entire back-end database should be made available. "This is a little tricky, because people have to settle on a format, and then require submissions in that format from contractors and state and local entities, etc.," 3
  • 4. Database Sharing Needs Experts call for easier, more agile data sharing: From Information Week, June 21, 2010, cover story (page 26) “Share”, “Why IT Needs To Push Data Sharing Efforts” http://www.informationweek.com/news/services/integration/showArticle.jhtml?articleID=225700546 ... one of the single best things (IT departments) can do for their businesses is enable effective data sharing. … Yet data sharing, particularly automated systems that give your external business partners access to your data when they want it, are not ubiquitous or easy, and the level of data sharing of any kind is surprisingly low. ******************************* When it comes to what frustrates data sharing efforts, the classic culprit, budget limitations, tops the list of survey respondents, followed by complaints about the multiple sets of tools and the care and feeding required by legacy connections. ******************************* A bigger problem is if IT simply doesn't have the ability to respond quickly to new requests. Too many companies can't move fast enough. 4
  • 5. Database Sharing Today Need for more agile Supply Chain management Real world example - Sikorsky Aircraft Company: Resistance from potential new suppliers to burdensome EDI process to share order data 5
  • 6. DataPortal Database Sharing In the Cloud DataPortal transfers business data in database form over the Web • No development • SSL encryption • Works across database vendors • Multiple layers of password protection • Minimal setup and configuration • No firewall modification (no manual installation for Web client) • Serves a wide audience • Data NOT exposed directly to network • Instant database migration • No unnecessary conversion at either end • Maintains database value and complexity Appropriate when source and destination data reside in a relational database system DataPortal - “Push-button” solution for instantly sharing complex database data in the Cloud 6
  • 7. DataPortal Operation Client DataPortal Client DataPortal Server Request Database Host Host Database System System DataPortal Web DataPortal Client Server HTTP Client requests a DataSource (published database) 7
  • 8. DataPortal Operation DataPortal Client Server DataPortal Server Host Response Host Database Database System System DataPortal Web DataPortal Client Server HTTP Snapshot of requested DataSource is transferred over the Web, through unmodified firewall, to user's preferred database system available for immediate processing using existing infrastructure 8
  • 9. DataPortal Write Data Modes Original Data to be Original Data to be Original Data to be Data Written Data Written Data Written Replace Edit Append Database Resulting Resulting Resulting Data Data Data Creates and populates Writes new data Appends new data new database – replaces over old After old Any pre-existing database with same name 9
  • 10. Shared Data Selection Options Shared Data Selection Options • Share Full Database Structure and Content • Share Only Selected Tables • Share Data Filtered by Authenticated ID 10
  • 11. Data Sharing Usage Example Broadcast – central database is distributed to many partners (e.g. - Replace Database mode) DataPortal DataPortal DataPortal Client Client Client DataPortal DataPortal DataPortal Client Server Client DataPortal DataPortal DataPortal Client Client Client 11
  • 12. Data Sharing Usage Example Merge and Integrate – many partners add data to central database (e.g. - Append mode) DataPortal DataPortal DataPortal Server Server Server DataPortal DataPortal DataPortal Server Client Server DataPortal DataPortal DataPortal Server Server Server 12
  • 13. DataPortal Security DataPortal Security • Uses Web standard SSL (Secure Socket Layer) for • Encryption • Authentication • Other Web security standards can be applied when available • Mulitple levels of password authentication • Web level • Application level • Database level • Data filtering based on authenticated ID • Database never exposed to network (stays behind firewall) 13
  • 14. DataPortal Applet Client DataPortal Client ● No installation required within Web browser ● Uses standard Java-enabled Web browser Usage: ● URL selects DataPortal Server ● Selection Fields  DataSource  Data Destination  DB Vendor  Host/Port  User/Password  Initial DB (if required)  New DB Name  Info/Status Display ** standalone client also available ** 14
  • 15. DataPortal Standalone Client Application DataPortal Client ● Includes all functionality of Web client Standalone Application Transfer Control View ● Defines and saves data transfers ● Web listener transfers data based on authenticated Web requests from programs or browsers DataPortal Client Standalone Application Listener View 15
  • 16. DataPortal Server Management Publishing a DataSource Add DataSource Web Form DataSource Form Parameters DB Vendor Type Host/Port DB Name DataSource Name (as seen by client) Database User/Password Min/Max Number of Connections Require Client User/Password for DataSource access 16
  • 17. DataPortal Security Design DataPortal Security Diagram Manual Submission Standard SSL Applet RDBMS GUI Data Path Data Path DataPortal RDBMS Client Programatic DataPortal Server DataPortal Client URL Request Host Applet Manual Web Form (Browser) URL DataPortal Manual Submission Request Server Submission Application HTTP/SSL GUI Listerner(s) RDBMS DataPortal Client DataPortal Client Application 17
  • 18. DataPortal Data Transfer Integrity: Implicit Verification DataPortal Transfer Integrity – Implicit Verfication ● DataPortal transfer requires many Java component actions ● Each action is monitored for material exceptions ● Data corruption without exception is highly unlikely (supported by testing) ● Transfer is treated atomically - If any action throws any material exception, the entire transfer is aborted – Transfer results reported – Transfer can be repeated, in its entirety, until successful 18
  • 19. DataPortal Data Transfer Integrity: Explicit Verification DataPortal Transfer Integrity – Explicit Verification ● Element Size at Destination is compared to Size at Source – can be done inline, not currently included in standard transfer (overhead not justified) ● DataPortal infrastructure is used to compare structure and data of two DataBases, across DB vendors – Compare 2 DB separate utility 19
  • 20. DataPortal Data Transfer Integrity: Database Compare Utility DataPortal infrastructure is used to compare structure and data of two DataBases, across DB vendors Example: In a DB of ~150 MBs, an improper handling of the “ ' ” character by the database was discovered 20
  • 21. DataPortal Deployment DataPortal Deployed by loading DataPortal Server Host WAR (Web ARchive) File into Servlet Engine Web Server Client Applet JAR (Java Archive) File Servlet Engine is contained within WAR File WAR File (Server) Client is automatically downloaded and installed into Web browser JAR File (Client) 21
  • 23. Patented DataPortal Technology DataPortal IP Status: Connection Concepts awarded U.S. Patent 7,346,635 for DataPortal technology Connection Concepts owns patent 23
  • 24. DataPortal Advantage • With DataPortal, data is read/written directly from/to database without conversion to intermediate formats • More complex, high value data can be transferred with the ease of a simple flat file Ease of Traditional Database Data Transfer Over the Web Transfer Practical Limit Export Setup Convert Convert Dilute Import Database CSV, CSV, Database Data Source XML... XML... Data Destination Data Complexity/ Value Web Direct Database Data Transfer Over the Web with DataPortal DataPortal DataPortal Ease of Transfer Practical Limit Database Database Data Source Data Destination Web Data Complexity/ Value 24
  • 25. DataPortal Value Users can share complex database data over the Web... ● directly from/to their databases (provider and consumer) without conversion to intermediate forms ● immediately ● effortlessly (“push-button” ease) ● without development effort or need for programmers ● directly from the browser so consumer does not need to install software ● without limit on data complexity (value) e.g. tables, primary/foreign keys, images, docs... ● while serving a wide audience ● no firewall modification Complex data can be shared among millions of users within minutes 25
  • 26. Standards-Based Solution DataPortal uses standards based technologies ● Relational Database Systems – standard for storing data (e.g. Oracle, DB2, Access, MySQL, SQL Server...) ● Web ● SQL (Structured Query Language) ● Standard port – no firewall modification ● Client runs in browser – no software installation ● SSL (Secure Socket Layer) for authentication and encryption ● Java ● Server deployed as Java Servlet within Web server ● Java Applet runs in Web browser ● JDBC – Java standard for database connectivity 26
  • 27. DataPortal Applications DataPortal applications DataPortal is a potential solution whenever parties want to quickly and easily share database data ● Many existing or new EDI applications (e.g. Supply-chain management, Order Processing or Status reporting...) ● Putting catalog, product and pricing info on the Web ● Supplying sales force with current data in the field ● Government data sharing ● Providing users original data for data-centric public facing Web sites (data.gov, usaspending.gov, recovery.gov..) ● Public data reporting to government (e.g Recovery funds usage reporting) ● Inter-agency/department data sharing DataPortal Potential: Standard for Sharing Business and Government Data 27
  • 28. DataPortal Design DB Database Abstract DB Structure, Data Vendor “A” Representation Serialized Objects DataPortal Server (DB Vendor Neutral) Host To DB Structure, Data DataPortal DataPortal Requests Client Server DB Structure, Data Info DB Vendor “B” Database DataPortal Client Create DB, Tables Host from DataPortal Client DataPortal Populate Tables Server 28
  • 29. Competitive Advantages Non-Web based competition: EDI (Legacy), Database Development Tools ● Difficult to use – intended for programmers ● Restrictive – must conform to specific formats or database vendors/versions ● Requires firewall modification, network programing ● Client must install software ● Must add in security and authentication ● Persistent connections use network resources ● Intended for narrow audience 29
  • 30. Competitive Advantages Web based competition ● CSV, Excel files Ease of – Requires conversion Transfer – Limits complexity/value of data DataPortal CSV ● XML XML Practical Limit – Requires conversion API – Requires programming – Limits complexity/value of data Data Complexity/ Value – Inefficient – high data overhead ● API – Requires programming, expertise, time, effort 30
  • 31. Where Can DataPortal Be Used? Provide Standard Tool for Sharing Complex, Structured Data Within or Across: ● Departments ● Offices ● Partners ● Suppliers ● Customers ● Sales Force ● Government (Federal, State, City, Local) ● ….. 31
  • 32. Database Sharing In the Cloud A simpler way to transfer complex data Contact Information: Connection Concepts, Inc. 2141 Industrial Parkway, Suite 202 Silver Spring, MD, 20904-7824, USA http://data-portal.biz ; http://directtodb.com http://con2inc.com Gary Whitten, President., CIO whitteng@con2inc.com 860 729-3117