SlideShare a Scribd company logo
TORQUE
      IT SOLUTIONS
       Empowering Automotive Finance




   BTECH 451
Data Integration

    Shawn D’souza
The Company
• Torque It Solutions
• Provides I.T Solutions For Automotive
  Finance Companies And Car Dealerships
• Start-up
• Dealer Performance Management System
• Show Profit Potential
• Turn Data Into Information
The Project
• Avoid Manual Data Entry
   – How?
       • Use Existing Data
• A way to extract Data
• Automated Import of Data
• Auto-populate Form Fields
• Reconcile With Existing Data
• Flexible interface
The Plan
 Gathering The Data
      1. Ad-hoc Web Service
      2. Using Data Dump
 Displaying The Data
     3. Design & Implement The Visual
         Interface
How it Will Work

 Dealer’s Vehicle                                                       P.O.S
    Database

                             D.P.M.S
                                                    Ad-hoc Pull




                                                                Standardised
                                             Pull             D.P.M.S Database
       Overnight
     Database Dump

Export File
                                                     Reference Tables
                        Customised import process
The Benefits
• For the Customer
   – Easier to Use
   – Reduce time of Data entry
   – Increase Productivity
• For the Company
   – Compatible existing systems
   – Increase sales of the system
What’s in it for             me
Gain Experience working with:
  1. C# .NET Framework
  2. Object Mapping Technologies
  3. Developing Web Applications
  4. Creating & Consuming Web services
  5. Database Querying & Optimisation
  6. …
4512012

More Related Content

What's hot

Slides: Start Small, Grow Big with a Unified Scale-Out Infrastructure
Slides: Start Small, Grow Big with a Unified Scale-Out InfrastructureSlides: Start Small, Grow Big with a Unified Scale-Out Infrastructure
Slides: Start Small, Grow Big with a Unified Scale-Out Infrastructure
NetApp
 
Superior Cloud Economics with IBM Power Systems
Superior Cloud Economics with IBM Power SystemsSuperior Cloud Economics with IBM Power Systems
Superior Cloud Economics with IBM Power Systems
IBM Power Systems
 
IBM Spectrum Scale Slidecast
IBM Spectrum Scale SlidecastIBM Spectrum Scale Slidecast
IBM Spectrum Scale Slidecast
inside-BigData.com
 
Net App Unified Storage Architecture
Net App Unified Storage ArchitectureNet App Unified Storage Architecture
Net App Unified Storage Architecture
nburgett
 
Netapp Storage
Netapp StorageNetapp Storage
Netapp Storage
Prime Infoserv
 
Big Data: Movement, Warehousing, & Virtualization
Big Data: Movement, Warehousing, & VirtualizationBig Data: Movement, Warehousing, & Virtualization
Big Data: Movement, Warehousing, & Virtualization
tervela
 
Open-Source Lamp Stacks Fly with IBM POWER8
Open-Source Lamp Stacks Fly with IBM POWER8Open-Source Lamp Stacks Fly with IBM POWER8
Open-Source Lamp Stacks Fly with IBM POWER8
IBM Power Systems
 
O365 to share point 2016 delivering storage best practices in 3 easy steps
O365 to share point 2016   delivering storage best practices in 3 easy stepsO365 to share point 2016   delivering storage best practices in 3 easy steps
O365 to share point 2016 delivering storage best practices in 3 easy steps
Paul LaPorte
 
Superior Cloud Economics with Power Systems
Superior Cloud Economics with Power Systems Superior Cloud Economics with Power Systems
Superior Cloud Economics with Power Systems
IBM Power Systems
 
Consolidate More: High Performance Primary Deduplication in the Age of Abunda...
Consolidate More: High Performance Primary Deduplication in the Age of Abunda...Consolidate More: High Performance Primary Deduplication in the Age of Abunda...
Consolidate More: High Performance Primary Deduplication in the Age of Abunda...
Hitachi Vantara
 
How to Solve Real-Time Data Problems
How to Solve Real-Time Data ProblemsHow to Solve Real-Time Data Problems
How to Solve Real-Time Data Problems
IBM Power Systems
 
From big data to big value : Infrastructure need and Huawei best practise
From big data to big value : Infrastructure need and Huawei best practise From big data to big value : Infrastructure need and Huawei best practise
From big data to big value : Infrastructure need and Huawei best practise
BSP Media Group
 
IBRIX_908be20
IBRIX_908be20IBRIX_908be20
IBRIX_908be20
Chuck Hurst
 
IBM Power Systems: Designed for Data
IBM Power Systems: Designed for DataIBM Power Systems: Designed for Data
IBM Power Systems: Designed for Data
IBM Power Systems
 
NetApp FlashAdvantage 3-4-5
NetApp FlashAdvantage 3-4-5NetApp FlashAdvantage 3-4-5
NetApp FlashAdvantage 3-4-5
NetApp
 
Data warehouse inmon versus kimball 2
Data warehouse inmon versus kimball 2Data warehouse inmon versus kimball 2
Data warehouse inmon versus kimball 2
Mike Frampton
 
A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
A Common Database Approach for OLTP and OLAP Using an In-Memory Column DatabaseA Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
Ishara Amarasekera
 
Maximize IT for Real Business Advantage
Maximize IT for Real Business AdvantageMaximize IT for Real Business Advantage
Maximize IT for Real Business Advantage
Hitachi Vantara
 
Hadoop in the Enterprise: Legacy Rides the Elephant
Hadoop in the Enterprise: Legacy Rides the ElephantHadoop in the Enterprise: Legacy Rides the Elephant
Hadoop in the Enterprise: Legacy Rides the Elephant
DataWorks Summit
 
Advantages of Mainframe Replication With Hitachi VSP
Advantages of Mainframe Replication With Hitachi VSPAdvantages of Mainframe Replication With Hitachi VSP
Advantages of Mainframe Replication With Hitachi VSP
Hitachi Vantara
 

What's hot (20)

Slides: Start Small, Grow Big with a Unified Scale-Out Infrastructure
Slides: Start Small, Grow Big with a Unified Scale-Out InfrastructureSlides: Start Small, Grow Big with a Unified Scale-Out Infrastructure
Slides: Start Small, Grow Big with a Unified Scale-Out Infrastructure
 
Superior Cloud Economics with IBM Power Systems
Superior Cloud Economics with IBM Power SystemsSuperior Cloud Economics with IBM Power Systems
Superior Cloud Economics with IBM Power Systems
 
IBM Spectrum Scale Slidecast
IBM Spectrum Scale SlidecastIBM Spectrum Scale Slidecast
IBM Spectrum Scale Slidecast
 
Net App Unified Storage Architecture
Net App Unified Storage ArchitectureNet App Unified Storage Architecture
Net App Unified Storage Architecture
 
Netapp Storage
Netapp StorageNetapp Storage
Netapp Storage
 
Big Data: Movement, Warehousing, & Virtualization
Big Data: Movement, Warehousing, & VirtualizationBig Data: Movement, Warehousing, & Virtualization
Big Data: Movement, Warehousing, & Virtualization
 
Open-Source Lamp Stacks Fly with IBM POWER8
Open-Source Lamp Stacks Fly with IBM POWER8Open-Source Lamp Stacks Fly with IBM POWER8
Open-Source Lamp Stacks Fly with IBM POWER8
 
O365 to share point 2016 delivering storage best practices in 3 easy steps
O365 to share point 2016   delivering storage best practices in 3 easy stepsO365 to share point 2016   delivering storage best practices in 3 easy steps
O365 to share point 2016 delivering storage best practices in 3 easy steps
 
Superior Cloud Economics with Power Systems
Superior Cloud Economics with Power Systems Superior Cloud Economics with Power Systems
Superior Cloud Economics with Power Systems
 
Consolidate More: High Performance Primary Deduplication in the Age of Abunda...
Consolidate More: High Performance Primary Deduplication in the Age of Abunda...Consolidate More: High Performance Primary Deduplication in the Age of Abunda...
Consolidate More: High Performance Primary Deduplication in the Age of Abunda...
 
How to Solve Real-Time Data Problems
How to Solve Real-Time Data ProblemsHow to Solve Real-Time Data Problems
How to Solve Real-Time Data Problems
 
From big data to big value : Infrastructure need and Huawei best practise
From big data to big value : Infrastructure need and Huawei best practise From big data to big value : Infrastructure need and Huawei best practise
From big data to big value : Infrastructure need and Huawei best practise
 
IBRIX_908be20
IBRIX_908be20IBRIX_908be20
IBRIX_908be20
 
IBM Power Systems: Designed for Data
IBM Power Systems: Designed for DataIBM Power Systems: Designed for Data
IBM Power Systems: Designed for Data
 
NetApp FlashAdvantage 3-4-5
NetApp FlashAdvantage 3-4-5NetApp FlashAdvantage 3-4-5
NetApp FlashAdvantage 3-4-5
 
Data warehouse inmon versus kimball 2
Data warehouse inmon versus kimball 2Data warehouse inmon versus kimball 2
Data warehouse inmon versus kimball 2
 
A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
A Common Database Approach for OLTP and OLAP Using an In-Memory Column DatabaseA Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database
 
Maximize IT for Real Business Advantage
Maximize IT for Real Business AdvantageMaximize IT for Real Business Advantage
Maximize IT for Real Business Advantage
 
Hadoop in the Enterprise: Legacy Rides the Elephant
Hadoop in the Enterprise: Legacy Rides the ElephantHadoop in the Enterprise: Legacy Rides the Elephant
Hadoop in the Enterprise: Legacy Rides the Elephant
 
Advantages of Mainframe Replication With Hitachi VSP
Advantages of Mainframe Replication With Hitachi VSPAdvantages of Mainframe Replication With Hitachi VSP
Advantages of Mainframe Replication With Hitachi VSP
 

Viewers also liked

RPH KT
RPH KTRPH KT
RPH KT
Ken Jyubalta
 
The Four Points of Prosperity
The Four Points of ProsperityThe Four Points of Prosperity
The Four Points of Prosperity
wcaudle
 
3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...
3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...
3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...
Shawn D'souza
 
Plan de comisiones fuxionprolife
Plan de comisiones  fuxionprolife Plan de comisiones  fuxionprolife
Plan de comisiones fuxionprolife
FUXION
 
INDIA ON CUSP OF DIGITAL REVOLUTION
INDIA ON CUSP OF DIGITAL REVOLUTIONINDIA ON CUSP OF DIGITAL REVOLUTION
INDIA ON CUSP OF DIGITAL REVOLUTION
Kumar Ranjan
 
The Four Points of Prosperity
The Four Points of ProsperityThe Four Points of Prosperity
The Four Points of Prosperity
wcaudle
 

Viewers also liked (7)

RPH KT
RPH KTRPH KT
RPH KT
 
The Four Points of Prosperity
The Four Points of ProsperityThe Four Points of Prosperity
The Four Points of Prosperity
 
3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...
3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...
3d9f068c73d9f068c73aef66f694f31a049aff3f43a3d9f068c73aef66f694f31a049aff3f4ef...
 
Plan de comisiones fuxionprolife
Plan de comisiones  fuxionprolife Plan de comisiones  fuxionprolife
Plan de comisiones fuxionprolife
 
INDIA ON CUSP OF DIGITAL REVOLUTION
INDIA ON CUSP OF DIGITAL REVOLUTIONINDIA ON CUSP OF DIGITAL REVOLUTION
INDIA ON CUSP OF DIGITAL REVOLUTION
 
The Four Points of Prosperity
The Four Points of ProsperityThe Four Points of Prosperity
The Four Points of Prosperity
 
Web 2.0 Tools
Web 2.0 ToolsWeb 2.0 Tools
Web 2.0 Tools
 

Similar to 4512012

Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4
Shawn D'souza
 
A Hybrid Technology Platform for Increasing the Speed of Operational Analytics
A Hybrid Technology Platform for Increasing the Speed of Operational AnalyticsA Hybrid Technology Platform for Increasing the Speed of Operational Analytics
A Hybrid Technology Platform for Increasing the Speed of Operational Analytics
IBMGovernmentCA
 
CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]
CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]
CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]
Rhapsody Technologies, Inc.
 
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
Precisely
 
How Data Drives Business at Choice Hotels
How Data Drives Business at Choice HotelsHow Data Drives Business at Choice Hotels
How Data Drives Business at Choice Hotels
Cloudera, Inc.
 
In plant Web to Print a Solution that Fits
In plant Web to Print a Solution that FitsIn plant Web to Print a Solution that Fits
In plant Web to Print a Solution that Fits
Rochester Software Associates
 
Hortonworks kognitio webinar 10 dec 2013
Hortonworks kognitio webinar 10 dec 2013Hortonworks kognitio webinar 10 dec 2013
Hortonworks kognitio webinar 10 dec 2013
Michael Hiskey
 
Modern Data Architecture: In-Memory with Hadoop - the new BI
Modern Data Architecture: In-Memory with Hadoop - the new BIModern Data Architecture: In-Memory with Hadoop - the new BI
Modern Data Architecture: In-Memory with Hadoop - the new BI
Kognitio
 
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
DATAVERSITY
 
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...
DATAVERSITY
 
EPM Automate - Automating Enterprise Performance Management Cloud Solutions
EPM Automate - Automating Enterprise Performance Management Cloud SolutionsEPM Automate - Automating Enterprise Performance Management Cloud Solutions
EPM Automate - Automating Enterprise Performance Management Cloud Solutions
Joseph Alaimo Jr
 
Data mining & column stores
Data mining & column storesData mining & column stores
Data mining & column stores
Aung Thu Rha Hein
 
Deliver Best-in-Class HPC Cloud Solutions Without Losing Your Mind
Deliver Best-in-Class HPC Cloud Solutions Without Losing Your MindDeliver Best-in-Class HPC Cloud Solutions Without Losing Your Mind
Deliver Best-in-Class HPC Cloud Solutions Without Losing Your Mind
Avere Systems
 
The Shifting Landscape of Data Integration
The Shifting Landscape of Data IntegrationThe Shifting Landscape of Data Integration
The Shifting Landscape of Data Integration
DATAVERSITY
 
DoneDeal - AWS Data Analytics Platform
DoneDeal - AWS Data Analytics PlatformDoneDeal - AWS Data Analytics Platform
DoneDeal - AWS Data Analytics Platform
martinbpeters
 
NZS-4532 - Bringing Historical Data to Life with IBMs SMF Data Engine
NZS-4532 - Bringing Historical Data to Life with IBMs SMF Data EngineNZS-4532 - Bringing Historical Data to Life with IBMs SMF Data Engine
NZS-4532 - Bringing Historical Data to Life with IBMs SMF Data Engine
IBM z Systems Software - IT Service Management
 
Lower Cost and Complexity with Azure and StorSimple Hybrid Cloud Solutions
Lower Cost and Complexity with Azure and StorSimple Hybrid Cloud SolutionsLower Cost and Complexity with Azure and StorSimple Hybrid Cloud Solutions
Lower Cost and Complexity with Azure and StorSimple Hybrid Cloud Solutions
Perficient, Inc.
 
Cloud Computing And Soa Convergence Linthicum 02 09 10
Cloud Computing And Soa Convergence Linthicum 02 09 10Cloud Computing And Soa Convergence Linthicum 02 09 10
Cloud Computing And Soa Convergence Linthicum 02 09 10
David Linthicum
 
Big data and Analytics on AWS
Big data and Analytics on AWSBig data and Analytics on AWS
Big data and Analytics on AWS
2nd Watch
 
Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...
Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...
Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...
Seeling Cheung
 

Similar to 4512012 (20)

Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4Fi nf068c73aef66f694f31a049aff3f4
Fi nf068c73aef66f694f31a049aff3f4
 
A Hybrid Technology Platform for Increasing the Speed of Operational Analytics
A Hybrid Technology Platform for Increasing the Speed of Operational AnalyticsA Hybrid Technology Platform for Increasing the Speed of Operational Analytics
A Hybrid Technology Platform for Increasing the Speed of Operational Analytics
 
CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]
CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]
CDM SIG: Fusion MDM for Customer Highlights [2010 OAUG Collaborate]
 
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
 
How Data Drives Business at Choice Hotels
How Data Drives Business at Choice HotelsHow Data Drives Business at Choice Hotels
How Data Drives Business at Choice Hotels
 
In plant Web to Print a Solution that Fits
In plant Web to Print a Solution that FitsIn plant Web to Print a Solution that Fits
In plant Web to Print a Solution that Fits
 
Hortonworks kognitio webinar 10 dec 2013
Hortonworks kognitio webinar 10 dec 2013Hortonworks kognitio webinar 10 dec 2013
Hortonworks kognitio webinar 10 dec 2013
 
Modern Data Architecture: In-Memory with Hadoop - the new BI
Modern Data Architecture: In-Memory with Hadoop - the new BIModern Data Architecture: In-Memory with Hadoop - the new BI
Modern Data Architecture: In-Memory with Hadoop - the new BI
 
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
 
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...
 
EPM Automate - Automating Enterprise Performance Management Cloud Solutions
EPM Automate - Automating Enterprise Performance Management Cloud SolutionsEPM Automate - Automating Enterprise Performance Management Cloud Solutions
EPM Automate - Automating Enterprise Performance Management Cloud Solutions
 
Data mining & column stores
Data mining & column storesData mining & column stores
Data mining & column stores
 
Deliver Best-in-Class HPC Cloud Solutions Without Losing Your Mind
Deliver Best-in-Class HPC Cloud Solutions Without Losing Your MindDeliver Best-in-Class HPC Cloud Solutions Without Losing Your Mind
Deliver Best-in-Class HPC Cloud Solutions Without Losing Your Mind
 
The Shifting Landscape of Data Integration
The Shifting Landscape of Data IntegrationThe Shifting Landscape of Data Integration
The Shifting Landscape of Data Integration
 
DoneDeal - AWS Data Analytics Platform
DoneDeal - AWS Data Analytics PlatformDoneDeal - AWS Data Analytics Platform
DoneDeal - AWS Data Analytics Platform
 
NZS-4532 - Bringing Historical Data to Life with IBMs SMF Data Engine
NZS-4532 - Bringing Historical Data to Life with IBMs SMF Data EngineNZS-4532 - Bringing Historical Data to Life with IBMs SMF Data Engine
NZS-4532 - Bringing Historical Data to Life with IBMs SMF Data Engine
 
Lower Cost and Complexity with Azure and StorSimple Hybrid Cloud Solutions
Lower Cost and Complexity with Azure and StorSimple Hybrid Cloud SolutionsLower Cost and Complexity with Azure and StorSimple Hybrid Cloud Solutions
Lower Cost and Complexity with Azure and StorSimple Hybrid Cloud Solutions
 
Cloud Computing And Soa Convergence Linthicum 02 09 10
Cloud Computing And Soa Convergence Linthicum 02 09 10Cloud Computing And Soa Convergence Linthicum 02 09 10
Cloud Computing And Soa Convergence Linthicum 02 09 10
 
Big data and Analytics on AWS
Big data and Analytics on AWSBig data and Analytics on AWS
Big data and Analytics on AWS
 
Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...
Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...
Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...
 

Recently uploaded

Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
MichaelKnudsen27
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
Zilliz
 
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation ParametersEssentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
Safe Software
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
DianaGray10
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Zilliz
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Hiroshi SHIBATA
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
ScyllaDB
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
saastr
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Alex Pruden
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
Neo4j
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
AstuteBusiness
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Pitangent Analytics & Technology Solutions Pvt. Ltd
 
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
Fwdays
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
c5vrf27qcz
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Zilliz
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
Pablo Gómez Abajo
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
operationspcvita
 

Recently uploaded (20)

Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
 
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation ParametersEssentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
 
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
 
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk"Frontline Battles with DDoS: Best practices and Lessons Learned",  Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
 
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptxThe Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
 

4512012

  • 1. TORQUE IT SOLUTIONS Empowering Automotive Finance BTECH 451 Data Integration Shawn D’souza
  • 2. The Company • Torque It Solutions • Provides I.T Solutions For Automotive Finance Companies And Car Dealerships • Start-up • Dealer Performance Management System • Show Profit Potential • Turn Data Into Information
  • 3. The Project • Avoid Manual Data Entry – How? • Use Existing Data • A way to extract Data • Automated Import of Data • Auto-populate Form Fields • Reconcile With Existing Data • Flexible interface
  • 4. The Plan  Gathering The Data 1. Ad-hoc Web Service 2. Using Data Dump  Displaying The Data 3. Design & Implement The Visual Interface
  • 5. How it Will Work Dealer’s Vehicle P.O.S Database D.P.M.S Ad-hoc Pull Standardised Pull D.P.M.S Database Overnight Database Dump Export File Reference Tables Customised import process
  • 6. The Benefits • For the Customer – Easier to Use – Reduce time of Data entry – Increase Productivity • For the Company – Compatible existing systems – Increase sales of the system
  • 7. What’s in it for me Gain Experience working with: 1. C# .NET Framework 2. Object Mapping Technologies 3. Developing Web Applications 4. Creating & Consuming Web services 5. Database Querying & Optimisation 6. …

Editor's Notes

  1. I’ll be giving you a brief overview of the project I am going to be working on this year,I will talk to you a bit a about the company, what they doand what I will be working on with the company.
  2. Start-upStarted last year, the current system has the bare nProvides I.T Solutions For Automotive Finance Companies And Dealersby Assisting Car Dealerships, distributor/ manufacturers etc… Make The Most Profit From Car Sales, Finance And Income And After Sales I.E. Servicing And Parts.Dealer Performance Management SystemI will be working on the DPMS which stands for Dealer Performance Management System, this is the web application that will be used by the car dealers to help them make decisions.. Show Profit PotentialShow Profit Potential And Where business and operationalProcess Improvements Can Be Made, Using KPI or(key performance indicators) that will evaluate the success of deals in car sales, insurance ... ROI rate of income – how much profit u make on a saleF&I Hand over time How many deals have been convertedTurn Data Into InformationThese dealers already have records of sales, however these records are date not information. The purpose of this system is to turn this data into information. This will be done finding trends in the data for example.. Turning data into informationInformation is quite simply an understanding of the relationships between pieces of data, or between pieces of data and other information. – this is done in the system through KPIs such as improved performance, competitive advantage, innovation, the sharing of lessons learned and continuous improvement of the organization.
  3. Currently the only way to input data into DPMS is by manual data entry that is: manually fill 50 or so fields, this leaves room for user to make mistake or enter inaccurate data and also takes up a lot of time.Avoid Manual Data EntryHow?Use Existing DataSo we Need a way for users to import their existing data from external systems into the application to help auto complete fields.The system need to be automatedA way to extract Data DPMS needs a way to use vehicle details informationfrom currently operated systems i.e. Point of Sale (POS), (Vehicle database)Allow users to auto-populate vehicle detail fields Automated Import of DataAuto-populate Form Fields Design and implement an interface that will allow user to load the data.Reconcile With Existing Data The system will convert different data formats into a standardised format that will be consumed by the system Flexible interface The interface need to be as Flexible as possible so the will it be a be to interface with as much systems as possible
  4. The project will comprise of 3 main distinct areasGathering The Data1. Ad-hoc Web Service The first way of extracting data from the system will be via Ad hoc or “on the fly”, in this approach the underlying POS or Point of Sales system database will be accessed by queries. For example when the user enter a registration number or VIN number the system will automatically trigger a web-service that will send a request to the external POS system, which in turn will respond with corresponding vehicle detail, this data can then be used to populate the rest of the fields. 2. Using Data Dump The second means of gathering data is through an overnight process where the external system in our case Mototrak, which a data interface tool used by dealer to upload mass vehicle information to databases, this is same system currently being used by Trade Me. The plan is use the data dump created by the vehicle database, Mototrak to update our database. Displaying The Data The third phase is design and implement the front-end interface its-self, 1. Design & Implement The Visual Interface the interface will allow the user to specify whether they want to import or manually enter the, Depending on the user configuration the system will use one of the two ways described above will used to retrieve the vehicle details
  5. DPMS User in DPMS to have choice regarding where to pull data from (Mototrack or POS for example)P.O.S – point of sale - Presume Point of Sale System will provide Web service searchData dump - which will contain a record of the table structure and the data from a vehicle database and is usually in the form of a list of SQL statements.A new set of tables for reference dataETLExtract: I will extract data from the export, Transform: Then transform the data, e.g. combine or split columns, format the data eg. If the data in a different data format, the idea of this is to get all the data into be consistent with our database.Load: finally the data will be loaded into the reference tableCustomised import process will convert the data from data dump
  6. On completion of this interface, users will be allowed to choose how they want the fields to be populatedThey will have the option of selecting data either from the POS system or Mototrak ( vehicle data interface)For the CustomerReduce Manual data entryIncrease Data accuracyFor the CompanyThe interface will allow Make the system compatible with existing systemsThe system will dealers will be able to interface with most systems In turn this will benefit the companyIncrease the sales of the system