This document summarizes a presentation on data integration given by Shawn D'Souza in October 2012. It discusses the definition of data integration, the need for it due to multiple data sources, and challenges in integrating data. Approaches to data integration include manual integration, common user interfaces, integration by applications/middleware, uniform data access, and common data storage. The presentation also provides examples of data integration patterns and architectures. It concludes by discussing the importance of data integration and ways to improve it.

1. TORQUE
IT SOLUTIONS
BTECH 451
Empowering Automotive Finance Data Integration
Final Presentation
Shawn D’souza
Oct2012

2. DEFINITION NEED FOR DI CHALLENGES FOR DI APPROACHES
PREVIOUSLY TECHNICAL DETAILS DEMO SOLUTION ANALYSIS
FUTURE WORK CONCLUSION EXPERIENCE GAINED THANK YOU

4. Data integration involves combining
data residing in different sources and
providing users with a unified view of
these data.[1]
Maurizio Lenzerini (2002). "Data Integration: A Theoretical Perspective". PODS 2002. pp. 233–246

5. Data warehouse Live Reporting
Pros: Pros:
• Reports run against the Data Warehouse rather than • Less costly
your production database so your production • Less complicated
database can be dedicated to transactional • “IT Lite” with much less reliance on IT resources
processing rather than reporting
• Reports run against live production data rather
• Reporting can be faster than a Data Warehouse so you know all data
• Static Metadata is provided in the Data Warehouse returned in reports is guaranteed to be the most
recent data in DPMS environment
• Reports may run up to 10 to 30 times faster
with Live Data reporting than with existing
Cons: DPMS
• Building or buying pre-built Data Warehouses is more
expensive than a Live Data strategy
• “IT intensive” with heavy reliance on IT support Cons:
• Resources intensive to manage, maintain, and provide • If POS tables are purged then tables often you will have to
additional content on an ongoing basis be copied first if you want to report historical information
• The frequency of data being refreshed in the Data with a Live Data strategy
Warehouse may impact reporting • Report processing is shared with transactional processing
• Requires additional database software to store data and ETL on DPMS database
software to populate your Data Warehouse

6. • Querying on business activities, for statistical
analysis, online analytical processing (OLAP), and
data mining in order to en-able forecasting, decision
making, enterprise-wide planning, and, in the end,
• To gain sustainable competitive advantages.
• Requirements for improved customer service or self-
service

7. • Data quality
• The data integration team must promote data quality to a first-class citizen.
• Transparency and auditability
• Even high-quality results will be questioned by business consumers.
Providing complete transparency into how the data results were produced
will be necessary to relieve business consumers’ concerns around data
quality.
• Tracking history
• The ability to correctly report results at a particular period in time is an on-
going challenge, particularly when there are adjustments to historical data.
• Reducing processing times
• Efficiently processing very large volumes of data within ever shortening
processing windows is an on-going challenge for the data integration team

8. [Dittrich and Jonscher, 1999], All Together Now — Towards Integrating the World’s Information Systems

9. • Manual Integration
• users directly interact with all relevant information systems and manually integrate
selected data
• Common User Interface
• the user is supplied with a common user interface (e.g., a web browser) that provides a
uniform look and feel.
• Integration by Applications
• Applications that access various data sources and return integrated results to the user
• Integration by Middleware
• reusable functionality that is generally used to solve dedicated aspects of the integration
problem
• Uniform Data Access
• a logical integration of data is accomplished at the data access level
• Common Data Storage
• physical data integration is performed by transferring data to a new data storage
[Dittrich and Jonscher, 1999], All Together Now — Towards Integrating the World’s Information Systems

10. • Enterprise Information Integration (EII) – This pattern loosely couples multiple data stores by creating
a semantic layer above the data stores and using industry-standard APIs such as ODBC, OLE-DB, and
JDBC to access the data in real time.
• Enterprise Application Integration (EAI) – This pattern supports business processes and workflows
that span multiple application systems. It typically works on a message-/event-based model and is not
data-centric (i.e., it is parameter-based and does not pass more than one “record” at a time).
• Extract, Transform, and Load (ETL) – This pattern extracts data from
sources, transforms the data in memory and then loads it into a destination.
• Extract, Load, and Transform (ELT) – This pattern first extracts data from sources and loads it into a
relational database. The transformation is then performed within the relational database and not in
memory.
• Replication – This is a relational database feature that detects changed records in a source and
pushes the changed records to a destination or destinations. The destination is typically a mirror of the
source, meaning that the data is not transformed on the way from source to destination.

12. Torque IT Solutions
• Provides I.T Solutions For Automotive
Finance Companies And Car Dealerships
The •
•
Start-up
Dealer Performance Management System
•
Company Show Profit Potential

13. The Goal
Implement an Interface that will allow users to
Import Data from external databases

14. Dealer’s Vehicle
Database
P.O.S
The
Ad-hoc Pull
Architecture
Database Dump
Overnight
D.P.M.S
Pull
Export File
Reference Tables
Customised import process
Standardised
D.P.M.S Database

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22. • Limitations
• Pros
• Flexibility – allows new external data sources to be easily
configured
• Cons
• Exact match
• Bulk Import
• Edge server caching

23. • database caching at edge servers enables dynamic
content to be replicated at the edge of the
network, thereby improving the scalability and the
response time of Web applications.
• Integrates data service technology and edge server
data replication architecture, in order to improve Web
services‟ data performance and address a variety of
data issues in the SOA network.

24. • Provide data services with edge
server data replication to clients
• Increase data service
performance
• Reduce client-perceived
response time
• Ensure data consistency is more
easily achieved

25. • Importance of DI
• Issues for DI
• How You can improve DI
• Scalability considerations for DI

26. • NHibernate
• MVC .NET
• jQuery
• SQL Server

27. Xinfeg Ye
Academic Advisor
Frederik Dinkelaker
Industrial Mentor