SlideShare a Scribd company logo
Datavault Hennie de Nooijer
Dan Linstedt Data modeling All data, all the time Method of design Data Vault
Agenda Position Definition Architecture Modeling Methodology Questions? 3 8-12-2010
Informationprovisioning 8-12-2010 4
Controllled informationprovisioning Information provisioning DWH 8-12-2010 5
Business Intelligence Data warehouse ETL Hardware RDBMS 8-12-2010 6
Definition The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. 7 The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. 8-12-2010
Detailoriented 8 8-12-2010
Historical tracking 9 8-12-2010
Uniquely linked  set normalized  tables 10 8-12-2010
Functional areas  of business 11 8-12-2010
8-12-2010 12 But there are more aspects…..
Auditable 13 8-12-2010
Scalable 14 8-12-2010
8-12-2010 15 Adaptable
8-12-2010 16 Active
8-12-2010 17 Metadata
8-12-2010 18 MDM aware
Agenda Position Definition Architecture Modeling Methodology Questions? 19 8-12-2010
Conventional architecture Current Business Demands/Wishes Integration Storage Presentation D W H TRANSFORM S T A G E Business Information Model
Modern architecture Integration Storage Presentation Storage Current Business Demands/Wishes S T A G E s o u r c e D W H b u s i n e s s D W H TRANSFORM ALL DATA, ALL THE TIME Current Business Information Model
Is geplaatst onder /betreft werkdag Bestelling op Business Information Model Ontvangt /Is geplaatst bij heeft omvang Verplicht tot /Is realisatie van Leverancier Bestaat uit /zit in Leverings condities Is bereid te leveren /kan geleverd worden door Levering Bestaat uit /komt voor in Materiaal soort Voorziet in /wordt in voorzien door werkdag omvang Komt voor in met Moet in voorzien worden voor Wordt ontvangen door /ontvangt Bestaat uit Materiaalbehoefte magazijn Betreft de bereidhied tot het levereren aan een /kan conform worden geleverd aan Magazijn
Architecture (detail) 23 8-12-2010 Frond end Patient Datamarts Patient Business Datavault Patient Raw  Datavault 1 Raw  Datavault 2 Raw  Datavault n KNA1 Patient Customer Replicatielaag Bron n Bron 2 Bron 1 KNA1 Customer Patient
Architecture (Advanced) Enterprise Service Bus (Biztalk/Cloverleaf/SOA) 24 8-12-2010 Frond end tools Datamarts Datavault Bron n Bron 1 Bron 2
Benefits Manage and enforce Compliance (SOX, HIPPA en BASEL II). Reduces Business cycle time. Enabling Master Data management. CMM Level 5 compliant. Repeatable, consistent and redundant. Trace all data back to source systems. Flexibility. Scalability. Consistent. Adaptable. Possible automatic generation of the DDL and ETL. Supports VLDB Designed for EDW 25 8-12-2010
Agenda Position Definition Architecture Modeling Methodology Questions? 26 8-12-2010 Patient Treat Satellite Satellite Treatment Link Satellite Hub Hub Satellite Satellite Satellite Satellite
Hub 27 8-12-2010 Hub Represents the business key. A surrogate key as the primary key. Load date timestamp (when did it get there?) Record source (where did it come from?) Patient_ID Patient_Key Patient_Code Patient_Name Patient_Desc Patient_Category Patient_SubCategory Patient_Address Patient_Gender Patient_Code Load_Date Record_Source Hub_Patient Patient
Satellite 28 8-12-2010 Satellite Descriptive items of a hub or a link A surrogate key as the primary key. Load date timestamp (when did it get there?) Record source (where did it come from?) Patient_Key Load_Date Patient_ID Patient_Key Load_Date Patient_Key Load_Date Patient_Code Patient_Name Patient_Desc Patient_Category Patient_SubCategory Patient_Address Patient_Gender Patient_Name Patient_Desc Patient_Category Patient_SubCategory Patient_Address Patient_Gender Patient_Name Patient_Desc Patient_Address Patient_Gender Patient_Category Patient_SubCategory SAT_Patient SAT_PatientCategory SAT_Patient Patient
Link Links two or more hubs Own surogate key. Keys from the hub Load date time stamp Record source 29 8-12-2010 Link Patient_Key Treat_Key Treatment_Key Hub_Patient Patient_Key Treat_Key Load_Date Record_Source Patient_Code Load_Date Record_Source Treat_Code Load_Date Record_Source Hub_Treat Link_Treatment
Bron datamodel 30 8-12-2010
Analyse datamodel 31 8-12-2010
Datavault datamodel 32 8-12-2010
8-12-2010 33 Datavault Point in Time views (PIT). ‘truth’ at a certain moment. Helper table? Bridge. Same as Point in Time but then a range.
Questions? 34 8-12-2010

More Related Content

What's hot

Agile BI via Data Vault and Modelstorming
Agile BI via Data Vault and ModelstormingAgile BI via Data Vault and Modelstorming
Agile BI via Data Vault and Modelstorming
Daniel Upton
 
Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
Agile Data Warehouse Modeling: Introduction to Data Vault Data ModelingAgile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
Kent Graziano
 
Data Warehouse Design and Best Practices
Data Warehouse Design and Best PracticesData Warehouse Design and Best Practices
Data Warehouse Design and Best Practices
Ivo Andreev
 
Data Vault Introduction
Data Vault IntroductionData Vault Introduction
Data Vault Introduction
Patrick Van Renterghem
 
IRM UK - 2009: DV Modeling And Methodology
IRM UK - 2009: DV Modeling And MethodologyIRM UK - 2009: DV Modeling And Methodology
IRM UK - 2009: DV Modeling And Methodology
Empowered Holdings, LLC
 
Why Data Vault?
Why Data Vault? Why Data Vault?
Why Data Vault?
Kent Graziano
 
Agile Data Mining with Data Vault 2.0 (english)
Agile Data Mining with Data Vault 2.0 (english)Agile Data Mining with Data Vault 2.0 (english)
Agile Data Mining with Data Vault 2.0 (english)
Michael Olschimke
 
Data Warehouse Project Report
Data Warehouse Project Report Data Warehouse Project Report
Data Warehouse Project Report
Tom Donoghue
 
Introduction To Data Vault - DAMA Oregon 2012
Introduction To Data Vault - DAMA Oregon 2012Introduction To Data Vault - DAMA Oregon 2012
Introduction To Data Vault - DAMA Oregon 2012
Empowered Holdings, LLC
 
Rando Veizi: Data warehouse and Pentaho suite
Rando Veizi: Data warehouse and Pentaho suiteRando Veizi: Data warehouse and Pentaho suite
Rando Veizi: Data warehouse and Pentaho suite
Carlo Vaccari
 
Warehouse components
Warehouse componentsWarehouse components
Warehouse components
ganblues
 
Data warehouse design
Data warehouse designData warehouse design
Data warehouse design
ines beltaief
 
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
 
Lecture 04 - Granularity in the Data Warehouse
Lecture 04 - Granularity in the Data WarehouseLecture 04 - Granularity in the Data Warehouse
Lecture 04 - Granularity in the Data Warehouse
phanleson
 
Data vault: What's Next
Data vault: What's NextData vault: What's Next
Data vault: What's Next
Empowered Holdings, LLC
 
Gartner Cool Vendor Report 2014
Gartner Cool Vendor Report 2014Gartner Cool Vendor Report 2014
Gartner Cool Vendor Report 2014
jenjermain
 
Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...
Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...
Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...
Edureka!
 
The Data Warehouse Lifecycle
The Data Warehouse LifecycleThe Data Warehouse Lifecycle
The Data Warehouse Lifecycle
bartlowe
 
Data vault what's Next: Part 2
Data vault what's Next: Part 2Data vault what's Next: Part 2
Data vault what's Next: Part 2
Empowered Holdings, LLC
 
Dw hk-white paper
Dw hk-white paperDw hk-white paper
Dw hk-white paper
july12jana
 

What's hot (20)

Agile BI via Data Vault and Modelstorming
Agile BI via Data Vault and ModelstormingAgile BI via Data Vault and Modelstorming
Agile BI via Data Vault and Modelstorming
 
Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
Agile Data Warehouse Modeling: Introduction to Data Vault Data ModelingAgile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling
 
Data Warehouse Design and Best Practices
Data Warehouse Design and Best PracticesData Warehouse Design and Best Practices
Data Warehouse Design and Best Practices
 
Data Vault Introduction
Data Vault IntroductionData Vault Introduction
Data Vault Introduction
 
IRM UK - 2009: DV Modeling And Methodology
IRM UK - 2009: DV Modeling And MethodologyIRM UK - 2009: DV Modeling And Methodology
IRM UK - 2009: DV Modeling And Methodology
 
Why Data Vault?
Why Data Vault? Why Data Vault?
Why Data Vault?
 
Agile Data Mining with Data Vault 2.0 (english)
Agile Data Mining with Data Vault 2.0 (english)Agile Data Mining with Data Vault 2.0 (english)
Agile Data Mining with Data Vault 2.0 (english)
 
Data Warehouse Project Report
Data Warehouse Project Report Data Warehouse Project Report
Data Warehouse Project Report
 
Introduction To Data Vault - DAMA Oregon 2012
Introduction To Data Vault - DAMA Oregon 2012Introduction To Data Vault - DAMA Oregon 2012
Introduction To Data Vault - DAMA Oregon 2012
 
Rando Veizi: Data warehouse and Pentaho suite
Rando Veizi: Data warehouse and Pentaho suiteRando Veizi: Data warehouse and Pentaho suite
Rando Veizi: Data warehouse and Pentaho suite
 
Warehouse components
Warehouse componentsWarehouse components
Warehouse components
 
Data warehouse design
Data warehouse designData warehouse design
Data warehouse design
 
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...
 
Lecture 04 - Granularity in the Data Warehouse
Lecture 04 - Granularity in the Data WarehouseLecture 04 - Granularity in the Data Warehouse
Lecture 04 - Granularity in the Data Warehouse
 
Data vault: What's Next
Data vault: What's NextData vault: What's Next
Data vault: What's Next
 
Gartner Cool Vendor Report 2014
Gartner Cool Vendor Report 2014Gartner Cool Vendor Report 2014
Gartner Cool Vendor Report 2014
 
Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...
Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...
Data Warehouse Interview Questions And Answers | Data Warehouse Tutorial | Ed...
 
The Data Warehouse Lifecycle
The Data Warehouse LifecycleThe Data Warehouse Lifecycle
The Data Warehouse Lifecycle
 
Data vault what's Next: Part 2
Data vault what's Next: Part 2Data vault what's Next: Part 2
Data vault what's Next: Part 2
 
Dw hk-white paper
Dw hk-white paperDw hk-white paper
Dw hk-white paper
 

Similar to Data vault

Data Virtualization: From Zero to Hero
Data Virtualization: From Zero to HeroData Virtualization: From Zero to Hero
Data Virtualization: From Zero to Hero
Denodo
 
Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)
Denodo
 
Is it sensible to use Data Vault at all? Conclusions from a project.
Is it sensible to use Data Vault at all? Conclusions from a project.Is it sensible to use Data Vault at all? Conclusions from a project.
Is it sensible to use Data Vault at all? Conclusions from a project.
Capgemini
 
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Alluxio, Inc.
 
Prague data management meetup #31 2020-01-27
Prague data management meetup #31 2020-01-27Prague data management meetup #31 2020-01-27
Prague data management meetup #31 2020-01-27
Martin Bém
 
Webinar Data Mesh - Part 3
Webinar Data Mesh - Part 3Webinar Data Mesh - Part 3
Webinar Data Mesh - Part 3
Jeffrey T. Pollock
 
Logical Data Warehouse: How to Build a Virtualized Data Services Layer
Logical Data Warehouse: How to Build a Virtualized Data Services LayerLogical Data Warehouse: How to Build a Virtualized Data Services Layer
Logical Data Warehouse: How to Build a Virtualized Data Services Layer
DataWorks Summit
 
Data warehousing unit 1
Data warehousing unit 1Data warehousing unit 1
Data warehousing unit 1
WE-IT TUTORIALS
 
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...
Denodo
 
Data virtualization an introduction
Data virtualization an introductionData virtualization an introduction
Data virtualization an introduction
Denodo
 
“A Distributed Operational and Informational Technological Stack”
“A Distributed Operational and Informational Technological Stack” “A Distributed Operational and Informational Technological Stack”
“A Distributed Operational and Informational Technological Stack”
Stratio
 
CV_Kamel_Mahdhaoui_2015-08_English
CV_Kamel_Mahdhaoui_2015-08_EnglishCV_Kamel_Mahdhaoui_2015-08_English
CV_Kamel_Mahdhaoui_2015-08_English
KMAHDHAOUI
 
CloverETL Provides Data Prep for Tableau
CloverETL Provides Data Prep for TableauCloverETL Provides Data Prep for Tableau
CloverETL Provides Data Prep for Tableau
CloverDX (formerly known as CloverETL)
 
Tamilarasu_Uthirasamy_10Yrs_Resume
Tamilarasu_Uthirasamy_10Yrs_ResumeTamilarasu_Uthirasamy_10Yrs_Resume
Tamilarasu_Uthirasamy_10Yrs_Resume
TAMILARASU UTHIRASAMY
 
Fast Data Strategy Houston Roadshow Presentation
Fast Data Strategy Houston Roadshow PresentationFast Data Strategy Houston Roadshow Presentation
Fast Data Strategy Houston Roadshow Presentation
Denodo
 
Thu-310pm-Impetus-SachinAndAjay
Thu-310pm-Impetus-SachinAndAjayThu-310pm-Impetus-SachinAndAjay
Thu-310pm-Impetus-SachinAndAjay
Ajay Shriwastava
 
Data Virtualization. An Introduction (ASEAN)
Data Virtualization. An Introduction (ASEAN)Data Virtualization. An Introduction (ASEAN)
Data Virtualization. An Introduction (ASEAN)
Denodo
 
Data Warehousing - in the real world
Data Warehousing - in the real worldData Warehousing - in the real world
Data Warehousing - in the real world
ukc4
 
Virtualisation de données : Enjeux, Usages & Bénéfices
Virtualisation de données : Enjeux, Usages & BénéficesVirtualisation de données : Enjeux, Usages & Bénéfices
Virtualisation de données : Enjeux, Usages & Bénéfices
Denodo
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An Introduction
Denodo
 

Similar to Data vault (20)

Data Virtualization: From Zero to Hero
Data Virtualization: From Zero to HeroData Virtualization: From Zero to Hero
Data Virtualization: From Zero to Hero
 
Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)
 
Is it sensible to use Data Vault at all? Conclusions from a project.
Is it sensible to use Data Vault at all? Conclusions from a project.Is it sensible to use Data Vault at all? Conclusions from a project.
Is it sensible to use Data Vault at all? Conclusions from a project.
 
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
 
Prague data management meetup #31 2020-01-27
Prague data management meetup #31 2020-01-27Prague data management meetup #31 2020-01-27
Prague data management meetup #31 2020-01-27
 
Webinar Data Mesh - Part 3
Webinar Data Mesh - Part 3Webinar Data Mesh - Part 3
Webinar Data Mesh - Part 3
 
Logical Data Warehouse: How to Build a Virtualized Data Services Layer
Logical Data Warehouse: How to Build a Virtualized Data Services LayerLogical Data Warehouse: How to Build a Virtualized Data Services Layer
Logical Data Warehouse: How to Build a Virtualized Data Services Layer
 
Data warehousing unit 1
Data warehousing unit 1Data warehousing unit 1
Data warehousing unit 1
 
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...
How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...
 
Data virtualization an introduction
Data virtualization an introductionData virtualization an introduction
Data virtualization an introduction
 
“A Distributed Operational and Informational Technological Stack”
“A Distributed Operational and Informational Technological Stack” “A Distributed Operational and Informational Technological Stack”
“A Distributed Operational and Informational Technological Stack”
 
CV_Kamel_Mahdhaoui_2015-08_English
CV_Kamel_Mahdhaoui_2015-08_EnglishCV_Kamel_Mahdhaoui_2015-08_English
CV_Kamel_Mahdhaoui_2015-08_English
 
CloverETL Provides Data Prep for Tableau
CloverETL Provides Data Prep for TableauCloverETL Provides Data Prep for Tableau
CloverETL Provides Data Prep for Tableau
 
Tamilarasu_Uthirasamy_10Yrs_Resume
Tamilarasu_Uthirasamy_10Yrs_ResumeTamilarasu_Uthirasamy_10Yrs_Resume
Tamilarasu_Uthirasamy_10Yrs_Resume
 
Fast Data Strategy Houston Roadshow Presentation
Fast Data Strategy Houston Roadshow PresentationFast Data Strategy Houston Roadshow Presentation
Fast Data Strategy Houston Roadshow Presentation
 
Thu-310pm-Impetus-SachinAndAjay
Thu-310pm-Impetus-SachinAndAjayThu-310pm-Impetus-SachinAndAjay
Thu-310pm-Impetus-SachinAndAjay
 
Data Virtualization. An Introduction (ASEAN)
Data Virtualization. An Introduction (ASEAN)Data Virtualization. An Introduction (ASEAN)
Data Virtualization. An Introduction (ASEAN)
 
Data Warehousing - in the real world
Data Warehousing - in the real worldData Warehousing - in the real world
Data Warehousing - in the real world
 
Virtualisation de données : Enjeux, Usages & Bénéfices
Virtualisation de données : Enjeux, Usages & BénéficesVirtualisation de données : Enjeux, Usages & Bénéfices
Virtualisation de données : Enjeux, Usages & Bénéfices
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An Introduction
 

Recently uploaded

CAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on BlockchainCAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on Blockchain
Claudio Di Ciccio
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
Octavian Nadolu
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
Matthew Sinclair
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
Claudio Di Ciccio
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
DianaGray10
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Speck&Tech
 
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfAI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
Techgropse Pvt.Ltd.
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Safe Software
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
Uni Systems S.M.S.A.
 
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
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Quotidiano Piemontese
 
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AIMind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Kumud Singh
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
tolgahangng
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
akankshawande
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
Aftab Hussain
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
名前 です男
 
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
 

Recently uploaded (20)

CAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on BlockchainCAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on Blockchain
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
 
20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
 
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
 
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfAI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
 
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
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
 
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AIMind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
 
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
 

Data vault

  • 2. Dan Linstedt Data modeling All data, all the time Method of design Data Vault
  • 3. Agenda Position Definition Architecture Modeling Methodology Questions? 3 8-12-2010
  • 5. Controllled informationprovisioning Information provisioning DWH 8-12-2010 5
  • 6. Business Intelligence Data warehouse ETL Hardware RDBMS 8-12-2010 6
  • 7. Definition The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. 7 The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. 8-12-2010
  • 10. Uniquely linked set normalized tables 10 8-12-2010
  • 11. Functional areas of business 11 8-12-2010
  • 12. 8-12-2010 12 But there are more aspects…..
  • 19. Agenda Position Definition Architecture Modeling Methodology Questions? 19 8-12-2010
  • 20. Conventional architecture Current Business Demands/Wishes Integration Storage Presentation D W H TRANSFORM S T A G E Business Information Model
  • 21. Modern architecture Integration Storage Presentation Storage Current Business Demands/Wishes S T A G E s o u r c e D W H b u s i n e s s D W H TRANSFORM ALL DATA, ALL THE TIME Current Business Information Model
  • 22. Is geplaatst onder /betreft werkdag Bestelling op Business Information Model Ontvangt /Is geplaatst bij heeft omvang Verplicht tot /Is realisatie van Leverancier Bestaat uit /zit in Leverings condities Is bereid te leveren /kan geleverd worden door Levering Bestaat uit /komt voor in Materiaal soort Voorziet in /wordt in voorzien door werkdag omvang Komt voor in met Moet in voorzien worden voor Wordt ontvangen door /ontvangt Bestaat uit Materiaalbehoefte magazijn Betreft de bereidhied tot het levereren aan een /kan conform worden geleverd aan Magazijn
  • 23. Architecture (detail) 23 8-12-2010 Frond end Patient Datamarts Patient Business Datavault Patient Raw Datavault 1 Raw Datavault 2 Raw Datavault n KNA1 Patient Customer Replicatielaag Bron n Bron 2 Bron 1 KNA1 Customer Patient
  • 24. Architecture (Advanced) Enterprise Service Bus (Biztalk/Cloverleaf/SOA) 24 8-12-2010 Frond end tools Datamarts Datavault Bron n Bron 1 Bron 2
  • 25. Benefits Manage and enforce Compliance (SOX, HIPPA en BASEL II). Reduces Business cycle time. Enabling Master Data management. CMM Level 5 compliant. Repeatable, consistent and redundant. Trace all data back to source systems. Flexibility. Scalability. Consistent. Adaptable. Possible automatic generation of the DDL and ETL. Supports VLDB Designed for EDW 25 8-12-2010
  • 26. Agenda Position Definition Architecture Modeling Methodology Questions? 26 8-12-2010 Patient Treat Satellite Satellite Treatment Link Satellite Hub Hub Satellite Satellite Satellite Satellite
  • 27. Hub 27 8-12-2010 Hub Represents the business key. A surrogate key as the primary key. Load date timestamp (when did it get there?) Record source (where did it come from?) Patient_ID Patient_Key Patient_Code Patient_Name Patient_Desc Patient_Category Patient_SubCategory Patient_Address Patient_Gender Patient_Code Load_Date Record_Source Hub_Patient Patient
  • 28. Satellite 28 8-12-2010 Satellite Descriptive items of a hub or a link A surrogate key as the primary key. Load date timestamp (when did it get there?) Record source (where did it come from?) Patient_Key Load_Date Patient_ID Patient_Key Load_Date Patient_Key Load_Date Patient_Code Patient_Name Patient_Desc Patient_Category Patient_SubCategory Patient_Address Patient_Gender Patient_Name Patient_Desc Patient_Category Patient_SubCategory Patient_Address Patient_Gender Patient_Name Patient_Desc Patient_Address Patient_Gender Patient_Category Patient_SubCategory SAT_Patient SAT_PatientCategory SAT_Patient Patient
  • 29. Link Links two or more hubs Own surogate key. Keys from the hub Load date time stamp Record source 29 8-12-2010 Link Patient_Key Treat_Key Treatment_Key Hub_Patient Patient_Key Treat_Key Load_Date Record_Source Patient_Code Load_Date Record_Source Treat_Code Load_Date Record_Source Hub_Treat Link_Treatment
  • 30. Bron datamodel 30 8-12-2010
  • 31. Analyse datamodel 31 8-12-2010
  • 33. 8-12-2010 33 Datavault Point in Time views (PIT). ‘truth’ at a certain moment. Helper table? Bridge. Same as Point in Time but then a range.

Editor's Notes

  1. Kern punten :Data Vault schema vergelijkbaar met eenneuralenetwerk.Neuronen,dendriten en synapses.Worden gemaakt en vernietigdwanneerditnodig is (vawegerelaties die ontstaan of ernietmeerzijn)Neuronenzijn Hubs en Hub SatellietenLinks zijn de dendritesAndere links zijn de synapses (vectors in the opposite direction). Conclusie:
  2. Compliance AuditabilityFlexibilityTraceabilityDDL and ETL generated.
  3. Kern punten :Conclusie:
  4. DWH is gereedschapkistvoor BIFinancieeldirecteur is nietgeinteresseerd in ETL
  5. Kern punten :Spreek voor zich.Conclusie:
  6. Kern punten :Lowest granularity.Atomic level.No aggregation.Details omdat je business rules op nieuw kunnen genereren als de inzichten in een organisatie kan veranderen.Als we het niet doen en je laad data geaggregeerd dan mis detail informatie.Conclusie:
  7. Kern punten :LineageConclusie:
  8. Kern punten :Spreek voor zich.Conclusie:
  9. Kern punten :Spreek voor zich.Conclusie:
  10. Kern punten :Spreek voor zich.Conclusie:
  11. Kern punten :Alle data moet traceerbaar zijn.Conclusie:
  12. Near real time dataOperational datawarehouse
  13. Kern punten :Conclusie:
  14. Information model close to the business.When information model close to the source systems you need to modify or rewrite complete ETL, DDL, etc.
  15. Kern punten :Naamgeving business vault voor business herkenbaar.Vraaggestuurd. Alleenelementen die gebruiktwordenvolgens businessBusiness key integratie (unieke business keys) (overeenkomstige business keys).Geendirecterapporten op de Raw datavault en Business datavault.Conclusie:
  16. Kern punten :Conclusie:
  17. Kern punten :Conclusie:
  18. Kern punten :Elegante modelleer techniek met een minimum van een aantal componenten: Hub, Link en Satellite.Hub representing the primary key. The Link Entities provide transaction integration between the Hubs. The Satellite Entities provide the context of the Hub primary key. Conclusie:
  19. Kern punten :Spreek voor zich.Conclusie:
  20. Kern punten :Historisch perpectiefChanging over timeHieruit kunnen we allerlei dimensies opbouwen met TYPE 1, 2 of 3Mogelijk om Load date time stamp, load end date time stamp en record source toe te voegen.Voor elke rij in de hub een satellite record. Waarom? Vanwege inner joining.Conclusie:
  21. Kern punten :Een patient wordt op een bepaald moment behandeldAls er meer informatie bij een behandeling hoort dan moet er een extra satellite bij de link tabel worden opgenomen.Het is mogelijkomelke hub, satellite en satellites parallel telaten laden.Hoge mate van parallelismemogelijk.Conclusie:
  22. Kern punten :Spreek voor zich.Conclusie:
  23. Kern punten :Spreek voor zich.Conclusie:
  24. Kern punten :Spreek voor zich.Conclusie: