際際滷shows by User: kgraziano / http://www.slideshare.net/images/logo.gif 際際滷shows by User: kgraziano / Wed, 15 Dec 2021 22:05:00 GMT 際際滷Share feed for 際際滷shows by User: kgraziano Balance agility and governance with #TrueDataOps and The Data Cloud /slideshow/balance-agility-and-governance-with-truedataops-and-the-data-cloud/250846286 balanceagilityandgovernance-montreal-211215220500
DataOps is the application of DevOps concepts to data. The DataOps Manifesto outlines WHAT that means, similar to how the Agile Manifesto outlines the goals of the Agile Software movement. But, as the demand for data governance has increased, and the demand to do more with less and be more agile has put more pressure on data teams, we all need more guidance on HOW to manage all this. Seeing that need, a small group of industry thought leaders and practitioners got together and created the #TrueDataOps philosophy to describe the best way to deliver DataOps by defining the core pillars that must underpin a successful approach. Combining this approach with an agile and governed platform like Snowflakes Data Cloud allows organizations to indeed balance these seemingly competing goals while still delivering value at scale. Given in Montreal on 14-Dec-2021]]>
DataOps is the application of DevOps concepts to data. The DataOps Manifesto outlines WHAT that means, similar to how the Agile Manifesto outlines the goals of the Agile Software movement. But, as the demand for data governance has increased, and the demand to do more with less and be more agile has put more pressure on data teams, we all need more guidance on HOW to manage all this. Seeing that need, a small group of industry thought leaders and practitioners got together and created the #TrueDataOps philosophy to describe the best way to deliver DataOps by defining the core pillars that must underpin a successful approach. Combining this approach with an agile and governed platform like Snowflakes Data Cloud allows organizations to indeed balance these seemingly competing goals while still delivering value at scale. Given in Montreal on 14-Dec-2021]]> Wed, 15 Dec 2021 22:05:00 GMT /slideshow/balance-agility-and-governance-with-truedataops-and-the-data-cloud/250846286 kgraziano@slideshare.net(kgraziano) Balance agility and governance with #TrueDataOps and The Data Cloud kgraziano DataOps is the application of DevOps concepts to data. The DataOps Manifesto outlines WHAT that means, similar to how the Agile Manifesto outlines the goals of the Agile Software movement. But, as the demand for data governance has increased, and the demand to do more with less and be more agile has put more pressure on data teams, we all need more guidance on HOW to manage all this. Seeing that need, a small group of industry thought leaders and practitioners got together and created the #TrueDataOps philosophy to describe the best way to deliver DataOps by defining the core pillars that must underpin a successful approach. Combining this approach with an agile and governed platform like Snowflakes Data Cloud allows organizations to indeed balance these seemingly competing goals while still delivering value at scale. Given in Montreal on 14-Dec-2021 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/balanceagilityandgovernance-montreal-211215220500-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> DataOps is the application of DevOps concepts to data. The DataOps Manifesto outlines WHAT that means, similar to how the Agile Manifesto outlines the goals of the Agile Software movement. But, as the demand for data governance has increased, and the demand to do more with less and be more agile has put more pressure on data teams, we all need more guidance on HOW to manage all this. Seeing that need, a small group of industry thought leaders and practitioners got together and created the #TrueDataOps philosophy to describe the best way to deliver DataOps by defining the core pillars that must underpin a successful approach. Combining this approach with an agile and governed platform like Snowflakes Data Cloud allows organizations to indeed balance these seemingly competing goals while still delivering value at scale. Given in Montreal on 14-Dec-2021
Balance agility and governance with #TrueDataOps and The Data Cloud from Kent Graziano
]]> 249 0 https://cdn.slidesharecdn.com/ss_thumbnails/balanceagilityandgovernance-montreal-211215220500-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Mesh for Dinner /slideshow/data-mesh-for-dinner/250846275 datameshfordinner-211215215951
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help? Given in Montreal on 14-Dec-2021]]>
Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help? Given in Montreal on 14-Dec-2021]]> Wed, 15 Dec 2021 21:59:51 GMT /slideshow/data-mesh-for-dinner/250846275 kgraziano@slideshare.net(kgraziano) Data Mesh for Dinner kgraziano Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help? Given in Montreal on 14-Dec-2021 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datameshfordinner-211215215951-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Wonder what this data mesh stuff is all about? What are the principles of data mesh? Can you or should you consider data mesh as the approach for your analytics platform? And most important - how can Snowflake help? Given in Montreal on 14-Dec-2021
Data Mesh for Dinner from Kent Graziano
]]> 2582 0 https://cdn.slidesharecdn.com/ss_thumbnails/datameshfordinner-211215215951-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 HOW TO SAVE PILEs of $$$鐃BY CREATING THE BEST DATA MODEL THE FIRST TIME (Kscope 19) /kgraziano/how-to-save-piles-of-by-creating-the-best-data-model-the-first-time-kscope-19 bestdatamodelkscope19-210616190933
A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn't? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk? In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it.]]>
A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn't? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk? In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it.]]> Wed, 16 Jun 2021 19:09:32 GMT /kgraziano/how-to-save-piles-of-by-creating-the-best-data-model-the-first-time-kscope-19 kgraziano@slideshare.net(kgraziano) HOW TO SAVE PILEs of $$$鐃BY CREATING THE BEST DATA MODEL THE FIRST TIME (Kscope 19) kgraziano A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn't? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk? In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bestdatamodelkscope19-210616190933-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn&#39;t? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk? In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it.
HOW TO SAVE PILEs of $$$ BY CREATING THE BEST DATA MODEL THE FIRST TIME (Kscope 19) from Kent Graziano
]]> 150 0 https://cdn.slidesharecdn.com/ss_thumbnails/bestdatamodelkscope19-210616190933-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Intro to Data Vault 2.0 on Snowflake /slideshow/intro-to-data-vault-20-on-snowflake/249384345 introtodatavaultonsf-main-210616184506
History, benefits and basics of Data Vault 2.0 along with some of the benefits and reference architecture for using it with Snowflake.]]>
History, benefits and basics of Data Vault 2.0 along with some of the benefits and reference architecture for using it with Snowflake.]]> Wed, 16 Jun 2021 18:45:06 GMT /slideshow/intro-to-data-vault-20-on-snowflake/249384345 kgraziano@slideshare.net(kgraziano) Intro to Data Vault 2.0 on Snowflake kgraziano History, benefits and basics of Data Vault 2.0 along with some of the benefits and reference architecture for using it with Snowflake. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introtodatavaultonsf-main-210616184506-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> History, benefits and basics of Data Vault 2.0 along with some of the benefits and reference architecture for using it with Snowflake.
Intro to Data Vault 2.0 on Snowflake from Kent Graziano
]]> 2385 0 https://cdn.slidesharecdn.com/ss_thumbnails/introtodatavaultonsf-main-210616184506-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Rise of the Data Cloud /slideshow/rise-of-the-data-cloud/238979708 riseofthedatacloud-dtsummit-201026191642
This talk will introduce you to the Data Cloud, how it works, and the problems it solves for companies across the globe and across industries. The Data Cloud is a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflakes platform is the engine that powers and provides access to the Data Cloud]]>
This talk will introduce you to the Data Cloud, how it works, and the problems it solves for companies across the globe and across industries. The Data Cloud is a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflakes platform is the engine that powers and provides access to the Data Cloud]]> Mon, 26 Oct 2020 19:16:42 GMT /slideshow/rise-of-the-data-cloud/238979708 kgraziano@slideshare.net(kgraziano) Rise of the Data Cloud kgraziano This talk will introduce you to the Data Cloud, how it works, and the problems it solves for companies across the globe and across industries. The Data Cloud is a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflakes platform is the engine that powers and provides access to the Data Cloud <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/riseofthedatacloud-dtsummit-201026191642-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This talk will introduce you to the Data Cloud, how it works, and the problems it solves for companies across the globe and across industries. The Data Cloud is a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflakes platform is the engine that powers and provides access to the Data Cloud
Rise of the Data Cloud from Kent Graziano
]]> 489 0 https://cdn.slidesharecdn.com/ss_thumbnails/riseofthedatacloud-dtsummit-201026191642-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Delivering Data Democratization in the Cloud with Snowflake /slideshow/delivering-data-democratization-in-the-cloud-with-snowflake/232791958 datademocratization-20-20vision-200428210520
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.]]>
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.]]> Tue, 28 Apr 2020 21:05:20 GMT /slideshow/delivering-data-democratization-in-the-cloud-with-snowflake/232791958 kgraziano@slideshare.net(kgraziano) Delivering Data Democratization in the Cloud with Snowflake kgraziano This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datademocratization-20-20vision-200428210520-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
Delivering Data Democratization in the Cloud with Snowflake from Kent Graziano
]]> 941 0 https://cdn.slidesharecdn.com/ss_thumbnails/datademocratization-20-20vision-200428210520-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Demystifying Data Warehousing as a Service (GLOC 2019) /slideshow/demystifying-data-warehousing-as-a-service-gloc-2019/146168418 demystifyingdwaas-gloc-190517025757
Extended deck from the 2019 GLOC event in Cleveland. Discusses what a DWaaS is, the top 10 features of Snowflake that represent that, and a check list for what questions to ask when choosing a cloud based data warehouse.]]>
Extended deck from the 2019 GLOC event in Cleveland. Discusses what a DWaaS is, the top 10 features of Snowflake that represent that, and a check list for what questions to ask when choosing a cloud based data warehouse.]]> Fri, 17 May 2019 02:57:57 GMT /slideshow/demystifying-data-warehousing-as-a-service-gloc-2019/146168418 kgraziano@slideshare.net(kgraziano) Demystifying Data Warehousing as a Service (GLOC 2019) kgraziano Extended deck from the 2019 GLOC event in Cleveland. Discusses what a DWaaS is, the top 10 features of Snowflake that represent that, and a check list for what questions to ask when choosing a cloud based data warehouse. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/demystifyingdwaas-gloc-190517025757-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Extended deck from the 2019 GLOC event in Cleveland. Discusses what a DWaaS is, the top 10 features of Snowflake that represent that, and a check list for what questions to ask when choosing a cloud based data warehouse.
Demystifying Data Warehousing as a Service (GLOC 2019) from Kent Graziano
]]> 2114 21 https://cdn.slidesharecdn.com/ss_thumbnails/demystifyingdwaas-gloc-190517025757-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Making Sense of Schema on Read /slideshow/making-sense-of-schema-on-read/122445017 makingsenseofschemaonread-181108184038
[Given at DAMA WI, Nov 2018] With the increasing prevalence of semi-structured data from IoT devices, web logs, and other sources, data architects and modelers have to learn how to interpret and project data from things like JSON. While the concept of loading data without upfront modeling is appealing to many, ultimately, in order to make sense of the data and use it to drive business value, we have to turn that schema-on-read data into a real schema! That means data modeling! In this session I will walk through both simple and complex JSON documents, decompose them, then turn them into a representative data model using Oracle SQL Developer Data Modeler. I will show you how they might look using both traditional 3NF and data vault styles of modeling. In this session you will: 1. See what a JSON document looks like 2. Understand how to read it 3. Learn how to convert it to a standard data model ]]>
[Given at DAMA WI, Nov 2018] With the increasing prevalence of semi-structured data from IoT devices, web logs, and other sources, data architects and modelers have to learn how to interpret and project data from things like JSON. While the concept of loading data without upfront modeling is appealing to many, ultimately, in order to make sense of the data and use it to drive business value, we have to turn that schema-on-read data into a real schema! That means data modeling! In this session I will walk through both simple and complex JSON documents, decompose them, then turn them into a representative data model using Oracle SQL Developer Data Modeler. I will show you how they might look using both traditional 3NF and data vault styles of modeling. In this session you will: 1. See what a JSON document looks like 2. Understand how to read it 3. Learn how to convert it to a standard data model ]]> Thu, 08 Nov 2018 18:40:38 GMT /slideshow/making-sense-of-schema-on-read/122445017 kgraziano@slideshare.net(kgraziano) Making Sense of Schema on Read kgraziano [Given at DAMA WI, Nov 2018] With the increasing prevalence of semi-structured data from IoT devices, web logs, and other sources, data architects and modelers have to learn how to interpret and project data from things like JSON. While the concept of loading data without upfront modeling is appealing to many, ultimately, in order to make sense of the data and use it to drive business value, we have to turn that schema-on-read data into a real schema! That means data modeling! In this session I will walk through both simple and complex JSON documents, decompose them, then turn them into a representative data model using Oracle SQL Developer Data Modeler. I will show you how they might look using both traditional 3NF and data vault styles of modeling. In this session you will: 1. See what a JSON document looks like 2. Understand how to read it 3. Learn how to convert it to a standard data model <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/makingsenseofschemaonread-181108184038-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> [Given at DAMA WI, Nov 2018] With the increasing prevalence of semi-structured data from IoT devices, web logs, and other sources, data architects and modelers have to learn how to interpret and project data from things like JSON. While the concept of loading data without upfront modeling is appealing to many, ultimately, in order to make sense of the data and use it to drive business value, we have to turn that schema-on-read data into a real schema! That means data modeling! In this session I will walk through both simple and complex JSON documents, decompose them, then turn them into a representative data model using Oracle SQL Developer Data Modeler. I will show you how they might look using both traditional 3NF and data vault styles of modeling. In this session you will: 1. See what a JSON document looks like 2. Understand how to read it 3. Learn how to convert it to a standard data model
Making Sense of Schema on Read from Kent Graziano
]]> 10623 13 https://cdn.slidesharecdn.com/ss_thumbnails/makingsenseofschemaonread-181108184038-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Agile Data Engineering: Introduction to Data Vault 2.0 (2018) /kgraziano/agile-data-engineering-introduction-to-data-vault-20-2018 introtodatavault2018-181010142744
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring ]]>
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring ]]> Wed, 10 Oct 2018 14:27:44 GMT /kgraziano/agile-data-engineering-introduction-to-data-vault-20-2018 kgraziano@slideshare.net(kgraziano) Agile Data Engineering: Introduction to Data Vault 2.0 (2018) kgraziano (updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introtodatavault2018-181010142744-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> (updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring
Agile Data Engineering: Introduction to Data Vault 2.0 (2018) from Kent Graziano
]]> 4361 11 https://cdn.slidesharecdn.com/ss_thumbnails/introtodatavault2018-181010142744-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Demystifying Data Warehousing as a Service - DFW /slideshow/demystifying-data-warehousing-as-a-service-dfw/87290802 demystifyingdwaas-dfw-180206044522
This is the extended presentation I gave to the DFW Data Science meetup on Feb 5, 2018.]]>
This is the extended presentation I gave to the DFW Data Science meetup on Feb 5, 2018.]]> Tue, 06 Feb 2018 04:45:22 GMT /slideshow/demystifying-data-warehousing-as-a-service-dfw/87290802 kgraziano@slideshare.net(kgraziano) Demystifying Data Warehousing as a Service - DFW kgraziano This is the extended presentation I gave to the DFW Data Science meetup on Feb 5, 2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/demystifyingdwaas-dfw-180206044522-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is the extended presentation I gave to the DFW Data Science meetup on Feb 5, 2018.
Demystifying Data Warehousing as a Service - DFW from Kent Graziano
]]> 2367 11 https://cdn.slidesharecdn.com/ss_thumbnails/demystifyingdwaas-dfw-180206044522-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Extreme BI: Creating Virtualized Hybrid Type 1+2 Dimensions /slideshow/extreme-bi-creating-virtualized-hybrid-type-12-dimensions/71981696 eco15extremebi-hybriddimensions-170209224505
From a talk I gave at WWDVC and ECO in 2015 about how we built virtual dimensions (views) on a data vault-style data warehouse (see Data Warehousing in the Real World for full details on that architecture)]]>
From a talk I gave at WWDVC and ECO in 2015 about how we built virtual dimensions (views) on a data vault-style data warehouse (see Data Warehousing in the Real World for full details on that architecture)]]> Thu, 09 Feb 2017 22:45:05 GMT /slideshow/extreme-bi-creating-virtualized-hybrid-type-12-dimensions/71981696 kgraziano@slideshare.net(kgraziano) Extreme BI: Creating Virtualized Hybrid Type 1+2 Dimensions kgraziano From a talk I gave at WWDVC and ECO in 2015 about how we built virtual dimensions (views) on a data vault-style data warehouse (see Data Warehousing in the Real World for full details on that architecture) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eco15extremebi-hybriddimensions-170209224505-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> From a talk I gave at WWDVC and ECO in 2015 about how we built virtual dimensions (views) on a data vault-style data warehouse (see Data Warehousing in the Real World for full details on that architecture)
Extreme BI: Creating Virtualized Hybrid Type 1+2 Dimensions from Kent Graziano
]]> 1338 6 https://cdn.slidesharecdn.com/ss_thumbnails/eco15extremebi-hybriddimensions-170209224505-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Demystifying Data Warehouse as a Service (DWaaS) /slideshow/demystifying-data-warehouse-as-a-service-dwaas/69267372 demystifyingdwaas-161118194040
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA. We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that). But what is a DWaaS really? How is it different from traditional on-premises data warehousing? In this talk I will: Demystify DWaaS by defining it and its goals Discuss the real-world benefits of DWaaS Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse. ]]>
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA. We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that). But what is a DWaaS really? How is it different from traditional on-premises data warehousing? In this talk I will: Demystify DWaaS by defining it and its goals Discuss the real-world benefits of DWaaS Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse. ]]> Fri, 18 Nov 2016 19:40:40 GMT /slideshow/demystifying-data-warehouse-as-a-service-dwaas/69267372 kgraziano@slideshare.net(kgraziano) Demystifying Data Warehouse as a Service (DWaaS) kgraziano This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA. We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that). But what is a DWaaS really? How is it different from traditional on-premises data warehousing? In this talk I will: Demystify DWaaS by defining it and its goals Discuss the real-world benefits of DWaaS Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/demystifyingdwaas-161118194040-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA. We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that). But what is a DWaaS really? How is it different from traditional on-premises data warehousing? In this talk I will: Demystify DWaaS by defining it and its goals Discuss the real-world benefits of DWaaS Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
Demystifying Data Warehouse as a Service (DWaaS) from Kent Graziano
]]> 2906 4 https://cdn.slidesharecdn.com/ss_thumbnails/demystifyingdwaas-161118194040-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Agile Data Warehousing: Using SDDM to Build a Virtualized ODS /slideshow/agile-data-warehousing-using-sddm-to-build-a-virtualized-ods/65133278 agiledatawarehousing-virtualods-160818161619
(This is the talk I gave at Houston DAMA and Agile Denver BI meetups) At a past client, in order to meet timelines to fulfill urgent, unmet reporting needs, I found it necessary to build a virtualized Operational Data Store as the first phase of a new Data Vault 2.0 project. This allowed me to deliver new objects, quickly and incrementally to the report developer so we could quickly show the business users their data. In order to limit the need for refactoring in later stages of the data warehouse development, I chose to build this virtualization layer on top of a Type 2 persistent staging layer. All of this was done using Oracle SQL Developer Data Modeler (SDDM) against (gasp!) a MS SQL Server Database. In this talk I will show you the architecture for this approach, the rationale, and then the tricks I used in SDDM to build all the stage tables and views very quickly. In the end you will see actual SQL code for a virtual ODS that can easily be translated to an Oracle database.]]>
(This is the talk I gave at Houston DAMA and Agile Denver BI meetups) At a past client, in order to meet timelines to fulfill urgent, unmet reporting needs, I found it necessary to build a virtualized Operational Data Store as the first phase of a new Data Vault 2.0 project. This allowed me to deliver new objects, quickly and incrementally to the report developer so we could quickly show the business users their data. In order to limit the need for refactoring in later stages of the data warehouse development, I chose to build this virtualization layer on top of a Type 2 persistent staging layer. All of this was done using Oracle SQL Developer Data Modeler (SDDM) against (gasp!) a MS SQL Server Database. In this talk I will show you the architecture for this approach, the rationale, and then the tricks I used in SDDM to build all the stage tables and views very quickly. In the end you will see actual SQL code for a virtual ODS that can easily be translated to an Oracle database.]]> Thu, 18 Aug 2016 16:16:19 GMT /slideshow/agile-data-warehousing-using-sddm-to-build-a-virtualized-ods/65133278 kgraziano@slideshare.net(kgraziano) Agile Data Warehousing: Using SDDM to Build a Virtualized ODS kgraziano (This is the talk I gave at Houston DAMA and Agile Denver BI meetups) At a past client, in order to meet timelines to fulfill urgent, unmet reporting needs, I found it necessary to build a virtualized Operational Data Store as the first phase of a new Data Vault 2.0 project. This allowed me to deliver new objects, quickly and incrementally to the report developer so we could quickly show the business users their data. In order to limit the need for refactoring in later stages of the data warehouse development, I chose to build this virtualization layer on top of a Type 2 persistent staging layer. All of this was done using Oracle SQL Developer Data Modeler (SDDM) against (gasp!) a MS SQL Server Database. In this talk I will show you the architecture for this approach, the rationale, and then the tricks I used in SDDM to build all the stage tables and views very quickly. In the end you will see actual SQL code for a virtual ODS that can easily be translated to an Oracle database. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agiledatawarehousing-virtualods-160818161619-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> (This is the talk I gave at Houston DAMA and Agile Denver BI meetups) At a past client, in order to meet timelines to fulfill urgent, unmet reporting needs, I found it necessary to build a virtualized Operational Data Store as the first phase of a new Data Vault 2.0 project. This allowed me to deliver new objects, quickly and incrementally to the report developer so we could quickly show the business users their data. In order to limit the need for refactoring in later stages of the data warehouse development, I chose to build this virtualization layer on top of a Type 2 persistent staging layer. All of this was done using Oracle SQL Developer Data Modeler (SDDM) against (gasp!) a MS SQL Server Database. In this talk I will show you the architecture for this approach, the rationale, and then the tricks I used in SDDM to build all the stage tables and views very quickly. In the end you will see actual SQL code for a virtual ODS that can easily be translated to an Oracle database.
Agile Data Warehousing: Using SDDM to Build a Virtualized ODS from Kent Graziano
]]> 1749 5 https://cdn.slidesharecdn.com/ss_thumbnails/agiledatawarehousing-virtualods-160818161619-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Agile Data Engineering - Intro to Data Vault Modeling (2016) /slideshow/agile-data-engineering-intro-to-data-vault-modeling-2016/61516892 agiledataengineering-dv2016-160429203342
(Updated deck) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring]]>
(Updated deck) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring]]> Fri, 29 Apr 2016 20:33:42 GMT /slideshow/agile-data-engineering-intro-to-data-vault-modeling-2016/61516892 kgraziano@slideshare.net(kgraziano) Agile Data Engineering - Intro to Data Vault Modeling (2016) kgraziano (Updated deck) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agiledataengineering-dv2016-160429203342-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> (Updated deck) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics: What the basic components of a DV model are How to build, and design structures incrementally, without constant refactoring
Agile Data Engineering - Intro to Data Vault Modeling (2016) from Kent Graziano
]]> 2845 12 https://cdn.slidesharecdn.com/ss_thumbnails/agiledataengineering-dv2016-160429203342-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Agile Methods and Data Warehousing (2016 update) /slideshow/agile-methods-and-data-warehousing-2016-update/59341565 agilemethodsanddw-160309223158
This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included. Includes more details on using Data Vault as well. (I gave this presentation at OUGF14 in Helsinki, Finland and again in 2016 for TDWI Nashville.)]]>
This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included. Includes more details on using Data Vault as well. (I gave this presentation at OUGF14 in Helsinki, Finland and again in 2016 for TDWI Nashville.)]]> Wed, 09 Mar 2016 22:31:58 GMT /slideshow/agile-methods-and-data-warehousing-2016-update/59341565 kgraziano@slideshare.net(kgraziano) Agile Methods and Data Warehousing (2016 update) kgraziano This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included. Includes more details on using Data Vault as well. (I gave this presentation at OUGF14 in Helsinki, Finland and again in 2016 for TDWI Nashville.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agilemethodsanddw-160309223158-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included. Includes more details on using Data Vault as well. (I gave this presentation at OUGF14 in Helsinki, Finland and again in 2016 for TDWI Nashville.)
Agile Methods and Data Warehousing (2016 update) from Kent Graziano
]]> 2073 12 https://cdn.slidesharecdn.com/ss_thumbnails/agilemethodsanddw-160309223158-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Warehousing 2016 /slideshow/data-warehousing-2016/57185518 datawarehousing2016-160118161443
These are the slides from my talk at Data Day Texas 2016 (#ddtx16). The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.]]>
These are the slides from my talk at Data Day Texas 2016 (#ddtx16). The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.]]> Mon, 18 Jan 2016 16:14:43 GMT /slideshow/data-warehousing-2016/57185518 kgraziano@slideshare.net(kgraziano) Data Warehousing 2016 kgraziano These are the slides from my talk at Data Day Texas 2016 (#ddtx16). The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datawarehousing2016-160118161443-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> These are the slides from my talk at Data Day Texas 2016 (#ddtx16). The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.
Data Warehousing 2016 from Kent Graziano
]]> 10098 13 https://cdn.slidesharecdn.com/ss_thumbnails/datawarehousing2016-160118161443-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Worst Practices in Data Warehouse Design /slideshow/worst-dw-practices/39757318 worstdwpractices-141001102505-phpapp01
This presentation was given at OakTable World 2014 (#OTW14) in San Francisco. After many years of designing data warehouses and consulting on data warehouse architectures, I have seen a lot of bad design choices by supposedly experienced professional. A sense of professionalism, confidentiality agreements, and some sense of common decency have prevented me from calling people out on some of this. No more! In this session I will walk you through a typical bad design like many I have seen. I will show you what I see when I reverse engineer a supposedly complete design and walk through what is wrong with it and discuss options to correct it. This will be a test of your knowledge of data warehouse best practices by seeing if you can recognize these worst practices.]]>
This presentation was given at OakTable World 2014 (#OTW14) in San Francisco. After many years of designing data warehouses and consulting on data warehouse architectures, I have seen a lot of bad design choices by supposedly experienced professional. A sense of professionalism, confidentiality agreements, and some sense of common decency have prevented me from calling people out on some of this. No more! In this session I will walk you through a typical bad design like many I have seen. I will show you what I see when I reverse engineer a supposedly complete design and walk through what is wrong with it and discuss options to correct it. This will be a test of your knowledge of data warehouse best practices by seeing if you can recognize these worst practices.]]> Wed, 01 Oct 2014 10:25:05 GMT /slideshow/worst-dw-practices/39757318 kgraziano@slideshare.net(kgraziano) Worst Practices in Data Warehouse Design kgraziano This presentation was given at OakTable World 2014 (#OTW14) in San Francisco. After many years of designing data warehouses and consulting on data warehouse architectures, I have seen a lot of bad design choices by supposedly experienced professional. A sense of professionalism, confidentiality agreements, and some sense of common decency have prevented me from calling people out on some of this. No more! In this session I will walk you through a typical bad design like many I have seen. I will show you what I see when I reverse engineer a supposedly complete design and walk through what is wrong with it and discuss options to correct it. This will be a test of your knowledge of data warehouse best practices by seeing if you can recognize these worst practices. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/worstdwpractices-141001102505-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation was given at OakTable World 2014 (#OTW14) in San Francisco. After many years of designing data warehouses and consulting on data warehouse architectures, I have seen a lot of bad design choices by supposedly experienced professional. A sense of professionalism, confidentiality agreements, and some sense of common decency have prevented me from calling people out on some of this. No more! In this session I will walk you through a typical bad design like many I have seen. I will show you what I see when I reverse engineer a supposedly complete design and walk through what is wrong with it and discuss options to correct it. This will be a test of your knowledge of data warehouse best practices by seeing if you can recognize these worst practices.
Worst Practices in Data Warehouse Design from Kent Graziano
]]> 3706 7 https://cdn.slidesharecdn.com/ss_thumbnails/worstdwpractices-141001102505-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Vault 2.0: Using MD5 Hashes for Change Data Capture /slideshow/dv-20-md5-hash-cdc/39756921 dv2-141001101600-phpapp02
This presentation was given at OakTable World 2014 (#OTW14) in San Francisco as a short Ted-style 10 minute talk. In it I introduce Data Vault 2.0 and its innovative approach to doing change data capture in a data warehouse by using MD5 Hash columns.]]>
This presentation was given at OakTable World 2014 (#OTW14) in San Francisco as a short Ted-style 10 minute talk. In it I introduce Data Vault 2.0 and its innovative approach to doing change data capture in a data warehouse by using MD5 Hash columns.]]> Wed, 01 Oct 2014 10:16:00 GMT /slideshow/dv-20-md5-hash-cdc/39756921 kgraziano@slideshare.net(kgraziano) Data Vault 2.0: Using MD5 Hashes for Change Data Capture kgraziano This presentation was given at OakTable World 2014 (#OTW14) in San Francisco as a short Ted-style 10 minute talk. In it I introduce Data Vault 2.0 and its innovative approach to doing change data capture in a data warehouse by using MD5 Hash columns. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dv2-141001101600-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation was given at OakTable World 2014 (#OTW14) in San Francisco as a short Ted-style 10 minute talk. In it I introduce Data Vault 2.0 and its innovative approach to doing change data capture in a data warehouse by using MD5 Hash columns.
Data Vault 2.0: Using MD5 Hashes for Change Data Capture from Kent Graziano
]]> 9929 7 https://cdn.slidesharecdn.com/ss_thumbnails/dv2-141001101600-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Agile Methods and Data Warehousing /slideshow/agile-methods-and-data-warehousing/35726954 agiledw2014-140610225508-phpapp01
I gave this presentation at OUGF14 in Helsinki, Finland and again for TDWI Nashville. This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included.]]>
I gave this presentation at OUGF14 in Helsinki, Finland and again for TDWI Nashville. This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included.]]> Tue, 10 Jun 2014 22:55:08 GMT /slideshow/agile-methods-and-data-warehousing/35726954 kgraziano@slideshare.net(kgraziano) Agile Methods and Data Warehousing kgraziano I gave this presentation at OUGF14 in Helsinki, Finland and again for TDWI Nashville. This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agiledw2014-140610225508-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> I gave this presentation at OUGF14 in Helsinki, Finland and again for TDWI Nashville. This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included.
Agile Methods and Data Warehousing from Kent Graziano
]]> 2221 5 https://cdn.slidesharecdn.com/ss_thumbnails/agiledw2014-140610225508-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling /slideshow/agile-data-warehouse-modeling-introduction-to-data-vault-data-modeling/35726773 agiledatawarehousingougf14-140610224424-phpapp01
This is a presentation I gave at OUGF14 in Helsinki, Finland. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures incrementally, without constant refactoring, when using the Data Vault modeling technique. This technique works well for: Building the Enterprise Data Warehouse repository in a CIF architecture Building a Persistent Staging Area (PSA) in a Kimball Bus Architecture Building your data model incrementally, one sprint at a time using a repeatable technique Providing a model that is easily extensible without need to re-engineer existing structure or load processes ]]>
This is a presentation I gave at OUGF14 in Helsinki, Finland. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures incrementally, without constant refactoring, when using the Data Vault modeling technique. This technique works well for: Building the Enterprise Data Warehouse repository in a CIF architecture Building a Persistent Staging Area (PSA) in a Kimball Bus Architecture Building your data model incrementally, one sprint at a time using a repeatable technique Providing a model that is easily extensible without need to re-engineer existing structure or load processes ]]> Tue, 10 Jun 2014 22:44:24 GMT /slideshow/agile-data-warehouse-modeling-introduction-to-data-vault-data-modeling/35726773 kgraziano@slideshare.net(kgraziano) Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling kgraziano This is a presentation I gave at OUGF14 in Helsinki, Finland. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures incrementally, without constant refactoring, when using the Data Vault modeling technique. This technique works well for: Building the Enterprise Data Warehouse repository in a CIF architecture Building a Persistent Staging Area (PSA) in a Kimball Bus Architecture Building your data model incrementally, one sprint at a time using a repeatable technique Providing a model that is easily extensible without need to re-engineer existing structure or load processes <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agiledatawarehousingougf14-140610224424-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is a presentation I gave at OUGF14 in Helsinki, Finland. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures incrementally, without constant refactoring, when using the Data Vault modeling technique. This technique works well for: Building the Enterprise Data Warehouse repository in a CIF architecture Building a Persistent Staging Area (PSA) in a Kimball Bus Architecture Building your data model incrementally, one sprint at a time using a repeatable technique Providing a model that is easily extensible without need to re-engineer existing structure or load processes
Agile Data Warehouse Modeling: Introduction to Data Vault Data Modeling from Kent Graziano
]]> 12785 21 https://cdn.slidesharecdn.com/ss_thumbnails/agiledatawarehousingougf14-140610224424-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-kgraziano-48x48.jpg?cb=1671134931 I am a recognized industry expert and thought leader, award wining speaker and published author in the areas of data modeling, data warehousing, data architecture, data mesh, agile and cloud analytics. I am a certified Data Vault Master, Data Vault 2.0 Practitioner (CDVP2), former Data Vault 2.0 Bootcamp Instructor, and Oracle ACE Director (Alumni) with over 35 years experience in analysis, design, and applications development, including data modeling, relational data base design and development; data warehouse architecture, design and implementation. I have written dozens of articles and done hundreds of presentations (both nationally and internationally). kentgraziano.com https://cdn.slidesharecdn.com/ss_thumbnails/balanceagilityandgovernance-montreal-211215220500-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/balance-agility-and-governance-with-truedataops-and-the-data-cloud/250846286 Balance agility and go... https://cdn.slidesharecdn.com/ss_thumbnails/datameshfordinner-211215215951-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-mesh-for-dinner/250846275 Data Mesh for Dinner https://cdn.slidesharecdn.com/ss_thumbnails/bestdatamodelkscope19-210616190933-thumbnail.jpg?width=320&height=320&fit=bounds kgraziano/how-to-save-piles-of-by-creating-the-best-data-model-the-first-time-kscope-19 HOW TO SAVE PILEs of ...