SlideShare une entreprise Scribd logo
1  sur  19
AN INTRO TO DATA MARTS AND DATA WAREHOUSES MARKUS BEAMER BDPA-CHARLOTTE WWW.BDPA-CHARLOTTE.ORG ALSO AVAILABLE ON  MOBEAMER.BLOGGER.COM Intelligent Data Strategies
Data Warehouse or Data mart ,[object Object],Data Warehouse Data Mart Extreme Volume Contains years of daily information at the lowest grain possible. Specific Volume Sets May only contain month to date information. Corporate wide The grouping of data elements is dictated by the corporate structure. Specific Data is grouped by needs of the team or group building the solution. Facts and Dimensions This system is typically made up of facts. Many Metrics Has many metric tables and rollup grains Serves data to Datamarts Serves data to Reports Will have data that is shared across groups. Will have data specific to only the implementation group.
Traditional Reporting Solutions Many systems performing many different business functions Analyst(s) Intelligence Many reports from the multiple sources Human intervention is needed to “makes sense” of different reports. ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Disorganized Closet ,[object Object],[object Object]
Organize Your Data Closet ,[object Object],[object Object],[object Object]
Simple Data Warehousing Many systems performing many different business functions Intelligence A centralized shared location for all the data.  Automated reporting that understands all sources. Reports specific to each system can still be delivered.
Adding Data Marts Intelligence Automated reporting that understands all sources can be delivered. Reports specific to each system can still be delivered. Data Mart Data Mart
Loading the Warehouse ,[object Object],[object Object],[object Object],[object Object],The Website A normal website where customer can come and order items from your company. Data A single data file containing All Orders made by a customer for that day. Website DB This is your standard relational database system. Tracks a lot of information. Ware House All this data is stored in a single table called “Orders”
Components of a Datawarehouse External Sources Stage Data Marts Reports
Facts ,[object Object],[object Object],Date CustomerDimID ProductDimID Price 06/25/2010 1 101 19.00 06/28/2010 2 102 10.00 Name CustomerID Product Price Date Markus 1001 Airplane 19.00 06/25/2010 John 1010 Car 10.00 06/28/2010
Dimensions ,[object Object],[object Object],CustDimID CustID Name 1 1001 Markus 2 1010 John ProductDimID Name 101 Airplane 102 Car
Time Sensitive Dimensions ,[object Object],[object Object],[object Object],2 Customers one in NC one in SC. Markus moves to SC in Feb. You can still report accurate NC sales in Jan because of the start and end dates. CustDimID CustID Name State Start End 1 1001 Markus NC 01/01/2010 01/01/2070 2 1010 John SC 01/01/2010 01/01/2070 CustDimID CustID Name State Start End 1 1001 Markus NC 01/01/2010 01/31/2010 2 1010 John SC 01/01/2010 01/01/2070 3 1001 Markus SC 02/01/2010 01/01/2070
Hierarchies ,[object Object],[object Object],For Example: The Airplane and Car both belong to the Toys product line. This hierarchy could be used to rollup and produce all Toy sales. ProductDimID Name ProductCode ParentCode 100 Toys T - 101 Airplane A1 T 102 Car C1 T
Facts and Dimensions ,[object Object],CustDimID CustID Name 1 1001 Markus 2 1010 John ProductDimID Name 101 Airplane 102 Car Date CustomerDimID ProductDimID Price 06/25/2010 1 101 19.00 06/28/2010 2 102 10.00
Metrics ,[object Object],[object Object],Product Metric Table For Example: The Product Metric table might be used to show that “Airplanes”(101) were sold 30 times on the 28 th . Customer Metric Table In this example you can see that Markus (1) bought one product, while John (2) bought 5 orders . Date ProductDimID NumOrders 06/25/2010 101 20 06/28/2010 102 30 Date CustDimID NumOrders 06/25/2010 1 1 06/28/2010 2 5
Star Schema ,[object Object],Orders Date CustomerDimID ProductDimID EmployeeDimID Price Product ProductDimID ProductName ProdcutCode ParentCode Start End Customer CustomerDimID CustID Name Age Start End Date Date Day of Week Month Name isHoliday Employee EmployeeDimID SSN Manager SSN Status Start End
Using the Data Mart ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Creating  A Metric Table Orders Date CustomerDimID ProductDimID EmployeeDimID Price Product ProductDimID ProductName ProdcutCode ParentCode Start End Customer CustomerDimID CustID Name Age Start End Date Date Day of Week Month Name isHoliday Employee EmployeeDimID SSN Manager SSN Status Start End Date CustDimID NumOrders 06/25/2010 1 1 06/28/2010 2 5
References ,[object Object],[object Object],[object Object],[object Object]

Contenu connexe

Tendances

Advanced Dimensional Modelling
Advanced Dimensional ModellingAdvanced Dimensional Modelling
Advanced Dimensional ModellingVincent Rainardi
 
Dimensional Modelling Session 2
Dimensional Modelling Session 2Dimensional Modelling Session 2
Dimensional Modelling Session 2akitda
 
Multidimensional data models
Multidimensional data  modelsMultidimensional data  models
Multidimensional data models774474
 
Dimensional data modeling
Dimensional data modelingDimensional data modeling
Dimensional data modelingAdam Hutson
 
multi dimensional data model
multi dimensional data modelmulti dimensional data model
multi dimensional data modelmoni sindhu
 
Difference between snowflake schema and fact constellation
Difference between snowflake schema and fact constellationDifference between snowflake schema and fact constellation
Difference between snowflake schema and fact constellationAsim Saif
 
Difference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional ModelingDifference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional ModelingAbdul Aslam
 
Multi dimensional model vs (1)
Multi dimensional model vs (1)Multi dimensional model vs (1)
Multi dimensional model vs (1)JamesDempsey1
 
Multidimensional schema
Multidimensional schemaMultidimensional schema
Multidimensional schemaChaand Chopra
 
Fact less fact Tables & Aggregate Tables
Fact less fact Tables & Aggregate Tables Fact less fact Tables & Aggregate Tables
Fact less fact Tables & Aggregate Tables Sunita Sahu
 
MSBI and Data WareHouse techniques by Quontra
MSBI and Data WareHouse techniques by Quontra MSBI and Data WareHouse techniques by Quontra
MSBI and Data WareHouse techniques by Quontra QUONTRASOLUTIONS
 
Agile Methodology Approach to SSRS Reporting
Agile Methodology Approach to SSRS ReportingAgile Methodology Approach to SSRS Reporting
Agile Methodology Approach to SSRS ReportingDanielson Samuel
 

Tendances (20)

Dimensional Modeling
Dimensional ModelingDimensional Modeling
Dimensional Modeling
 
Advanced Dimensional Modelling
Advanced Dimensional ModellingAdvanced Dimensional Modelling
Advanced Dimensional Modelling
 
Dimensional Modelling Session 2
Dimensional Modelling Session 2Dimensional Modelling Session 2
Dimensional Modelling Session 2
 
Business Intelligence: A Review
Business Intelligence: A ReviewBusiness Intelligence: A Review
Business Intelligence: A Review
 
Multidimensional data models
Multidimensional data  modelsMultidimensional data  models
Multidimensional data models
 
Dimensional data modeling
Dimensional data modelingDimensional data modeling
Dimensional data modeling
 
multi dimensional data model
multi dimensional data modelmulti dimensional data model
multi dimensional data model
 
Difference between snowflake schema and fact constellation
Difference between snowflake schema and fact constellationDifference between snowflake schema and fact constellation
Difference between snowflake schema and fact constellation
 
Dimensional Modeling
Dimensional ModelingDimensional Modeling
Dimensional Modeling
 
Dw concepts
Dw conceptsDw concepts
Dw concepts
 
Difference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional ModelingDifference between ER-Modeling and Dimensional Modeling
Difference between ER-Modeling and Dimensional Modeling
 
Multi dimensional model vs (1)
Multi dimensional model vs (1)Multi dimensional model vs (1)
Multi dimensional model vs (1)
 
Star schema
Star schemaStar schema
Star schema
 
Data Warehouse Designing: Dimensional Modelling and E-R Modelling
Data Warehouse Designing: Dimensional Modelling and E-R ModellingData Warehouse Designing: Dimensional Modelling and E-R Modelling
Data Warehouse Designing: Dimensional Modelling and E-R Modelling
 
Dominick’s finer foods
Dominick’s finer foodsDominick’s finer foods
Dominick’s finer foods
 
Multidimensional schema
Multidimensional schemaMultidimensional schema
Multidimensional schema
 
Fact less fact Tables & Aggregate Tables
Fact less fact Tables & Aggregate Tables Fact less fact Tables & Aggregate Tables
Fact less fact Tables & Aggregate Tables
 
MSBI and Data WareHouse techniques by Quontra
MSBI and Data WareHouse techniques by Quontra MSBI and Data WareHouse techniques by Quontra
MSBI and Data WareHouse techniques by Quontra
 
MULTIMEDIA MODELING
MULTIMEDIA MODELINGMULTIMEDIA MODELING
MULTIMEDIA MODELING
 
Agile Methodology Approach to SSRS Reporting
Agile Methodology Approach to SSRS ReportingAgile Methodology Approach to SSRS Reporting
Agile Methodology Approach to SSRS Reporting
 

Similaire à Data Warehousing

Data quality and bi
Data quality and biData quality and bi
Data quality and bijeffd00
 
Datawarehouse Overview
Datawarehouse OverviewDatawarehouse Overview
Datawarehouse Overviewashok kumar
 
(Lecture 3) Star Schema.pdf
(Lecture 3) Star Schema.pdf(Lecture 3) Star Schema.pdf
(Lecture 3) Star Schema.pdfMobeenMasoudi
 
Data Warehousing for students educationpptx
Data Warehousing for students educationpptxData Warehousing for students educationpptx
Data Warehousing for students educationpptxjainyshah20
 
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Analyst View of Data Virtualization: Conversations with Boulder Business Inte...
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
 
OLAP Cubes in Datawarehousing
OLAP Cubes in DatawarehousingOLAP Cubes in Datawarehousing
OLAP Cubes in DatawarehousingPrithwis Mukerjee
 
Become BI Architect with 1KEY Agile BI Suite - OLAP
Become BI Architect with 1KEY Agile BI Suite - OLAPBecome BI Architect with 1KEY Agile BI Suite - OLAP
Become BI Architect with 1KEY Agile BI Suite - OLAPDhiren Gala
 
Overview of business intelligence
Overview of business intelligenceOverview of business intelligence
Overview of business intelligenceAhsan Kabir
 
BI_LECTURE_4-2021.pptx
BI_LECTURE_4-2021.pptxBI_LECTURE_4-2021.pptx
BI_LECTURE_4-2021.pptxhajon27910
 
BI - Data warehousing in practice
BI - Data warehousing in practiceBI - Data warehousing in practice
BI - Data warehousing in practiceSjors Otten
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modelingvivekjv
 
Automatic Data Reconciliation, Data Quality, and Data Observability.pdf
Automatic Data Reconciliation, Data Quality, and Data Observability.pdfAutomatic Data Reconciliation, Data Quality, and Data Observability.pdf
Automatic Data Reconciliation, Data Quality, and Data Observability.pdf4dalert
 
Data warehousing in practice 2016
Data warehousing in practice 2016Data warehousing in practice 2016
Data warehousing in practice 2016Sjors Otten
 
Sqlserver interview questions
Sqlserver interview questionsSqlserver interview questions
Sqlserver interview questionsTaj Basha
 
Data warehousing in practice 2015
Data warehousing in practice 2015Data warehousing in practice 2015
Data warehousing in practice 2015Sjors Otten
 
Business Intelligence Portfolio
Business Intelligence PortfolioBusiness Intelligence Portfolio
Business Intelligence Portfolioeileensauer
 
Business Intelligence Portfolio
Business Intelligence PortfolioBusiness Intelligence Portfolio
Business Intelligence Portfolioeileensauer
 

Similaire à Data Warehousing (20)

ITReady DW Day2
ITReady DW Day2ITReady DW Day2
ITReady DW Day2
 
Date Analysis .pdf
Date Analysis .pdfDate Analysis .pdf
Date Analysis .pdf
 
Data quality and bi
Data quality and biData quality and bi
Data quality and bi
 
Datawarehouse Overview
Datawarehouse OverviewDatawarehouse Overview
Datawarehouse Overview
 
(Lecture 3) Star Schema.pdf
(Lecture 3) Star Schema.pdf(Lecture 3) Star Schema.pdf
(Lecture 3) Star Schema.pdf
 
Data Warehousing for students educationpptx
Data Warehousing for students educationpptxData Warehousing for students educationpptx
Data Warehousing for students educationpptx
 
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Analyst View of Data Virtualization: Conversations with Boulder Business Inte...
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...
 
OLAP Cubes in Datawarehousing
OLAP Cubes in DatawarehousingOLAP Cubes in Datawarehousing
OLAP Cubes in Datawarehousing
 
Become BI Architect with 1KEY Agile BI Suite - OLAP
Become BI Architect with 1KEY Agile BI Suite - OLAPBecome BI Architect with 1KEY Agile BI Suite - OLAP
Become BI Architect with 1KEY Agile BI Suite - OLAP
 
Overview of business intelligence
Overview of business intelligenceOverview of business intelligence
Overview of business intelligence
 
BI_LECTURE_4-2021.pptx
BI_LECTURE_4-2021.pptxBI_LECTURE_4-2021.pptx
BI_LECTURE_4-2021.pptx
 
BI - Data warehousing in practice
BI - Data warehousing in practiceBI - Data warehousing in practice
BI - Data warehousing in practice
 
Data Warehouse-Final
Data Warehouse-FinalData Warehouse-Final
Data Warehouse-Final
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modeling
 
Automatic Data Reconciliation, Data Quality, and Data Observability.pdf
Automatic Data Reconciliation, Data Quality, and Data Observability.pdfAutomatic Data Reconciliation, Data Quality, and Data Observability.pdf
Automatic Data Reconciliation, Data Quality, and Data Observability.pdf
 
Data warehousing in practice 2016
Data warehousing in practice 2016Data warehousing in practice 2016
Data warehousing in practice 2016
 
Sqlserver interview questions
Sqlserver interview questionsSqlserver interview questions
Sqlserver interview questions
 
Data warehousing in practice 2015
Data warehousing in practice 2015Data warehousing in practice 2015
Data warehousing in practice 2015
 
Business Intelligence Portfolio
Business Intelligence PortfolioBusiness Intelligence Portfolio
Business Intelligence Portfolio
 
Business Intelligence Portfolio
Business Intelligence PortfolioBusiness Intelligence Portfolio
Business Intelligence Portfolio
 

Plus de BDPA Charlotte - Information Technology Thought Leaders

Plus de BDPA Charlotte - Information Technology Thought Leaders (10)

2011 BDPA Charlotte Membership Packet
2011 BDPA Charlotte Membership Packet2011 BDPA Charlotte Membership Packet
2011 BDPA Charlotte Membership Packet
 
Mastering linkedIn Advanced Techniques and Insider Secrets BDPA National Pres...
Mastering linkedIn Advanced Techniques and Insider Secrets BDPA National Pres...Mastering linkedIn Advanced Techniques and Insider Secrets BDPA National Pres...
Mastering linkedIn Advanced Techniques and Insider Secrets BDPA National Pres...
 
Information security for small business
Information security for small businessInformation security for small business
Information security for small business
 
Information Security and the SDLC
Information Security and the SDLCInformation Security and the SDLC
Information Security and the SDLC
 
Running an IT Consulting Firm
Running an IT Consulting FirmRunning an IT Consulting Firm
Running an IT Consulting Firm
 
Professional Development Toolkit
Professional Development ToolkitProfessional Development Toolkit
Professional Development Toolkit
 
Health Information Technology Workforce Development Program Presentation
Health Information Technology Workforce Development Program PresentationHealth Information Technology Workforce Development Program Presentation
Health Information Technology Workforce Development Program Presentation
 
How to Create a Business Plan by SCORE
How to Create a Business Plan by SCOREHow to Create a Business Plan by SCORE
How to Create a Business Plan by SCORE
 
How to Start a Small IT Consulting Firm
How to Start a Small IT Consulting FirmHow to Start a Small IT Consulting Firm
How to Start a Small IT Consulting Firm
 
BDPA Charlotte Information Technology Thought Leaders 2010 Membership Drive
BDPA Charlotte   Information Technology Thought Leaders  2010 Membership DriveBDPA Charlotte   Information Technology Thought Leaders  2010 Membership Drive
BDPA Charlotte Information Technology Thought Leaders 2010 Membership Drive
 

Dernier

Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Google AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAGGoogle AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAGSujit Pal
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 

Dernier (20)

Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Google AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAGGoogle AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAG
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 

Data Warehousing

  • 1. AN INTRO TO DATA MARTS AND DATA WAREHOUSES MARKUS BEAMER BDPA-CHARLOTTE WWW.BDPA-CHARLOTTE.ORG ALSO AVAILABLE ON MOBEAMER.BLOGGER.COM Intelligent Data Strategies
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Simple Data Warehousing Many systems performing many different business functions Intelligence A centralized shared location for all the data. Automated reporting that understands all sources. Reports specific to each system can still be delivered.
  • 7. Adding Data Marts Intelligence Automated reporting that understands all sources can be delivered. Reports specific to each system can still be delivered. Data Mart Data Mart
  • 8.
  • 9. Components of a Datawarehouse External Sources Stage Data Marts Reports
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.