This document provides an overview of data warehousing and dimensional modeling. It defines key terms like data warehouse and data mart. It describes the need for data warehousing to integrate data from multiple sources and support analysis. Common data warehouse architectures and dimensional modeling techniques are explained, including star schemas, facts and dimensions, and slowly changing dimensions. The document also discusses related topics like big data, NoSQL, and how data warehousing supports business intelligence and analytics.
The document discusses using the Data Vault 2.0 methodology for agile data mining projects. It provides background on a customer segmentation project for a motor insurance company. The Data Vault 2.0 modeling approach is described as well as the CRISP-DM process model. An example is then shown applying several iterations of a decision tree model to a sample database, improving results with each iteration by adding additional attributes to the Data Vault 2.0 model and RapidMiner process. The conclusions state that Data Vault 2.0 provides a flexible data model that supports an agile approach to data mining projects by allowing incremental changes to the model and attributes.
Agile Data Engineering - Intro to Data Vault Modeling (2016)Kent Graziano
The document provides an introduction to Data Vault data modeling and discusses how it enables agile data warehousing. It describes the core structures of a Data Vault model including hubs, links, and satellites. It explains how the Data Vault approach provides benefits such as model agility, productivity, and extensibility. The document also summarizes the key changes in the Data Vault 2.0 methodology.
Agile BI via Data Vault and ModelstormingDaniel Upton
Audience: Business Intelligence Architects, Project Managers and Sponsors. This slideshow accompanies a video presentation of the same name, available at http://youtu.be/e0cHFdeGEeE.
Data Vault 2.0 DeMystified with Dan Linstedt and WhereScapeWhereScape
Join Dan Linstedt and WhereScape to learn the benefits that Data Vault 2.0 offers to data warehousing teams, what it is and isn't, and how data vault automation can help teams implement Data Vault 2.0 more quickly and successfully.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
The document introduces Visual DataVault, a modeling language for visually expressing Data Vault models. It aims to generate DDL from models and support Microsoft Office. The language defines basic entities like hubs, links, satellites and reference tables. It also covers query assistant tables, computed structures, exploration links and business vault tables to enhance the raw data vault. Some remarks note it focuses on logical not physical modeling and more features are planned.
The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
The document discusses using the Data Vault 2.0 methodology for agile data mining projects. It provides background on a customer segmentation project for a motor insurance company. The Data Vault 2.0 modeling approach is described as well as the CRISP-DM process model. An example is then shown applying several iterations of a decision tree model to a sample database, improving results with each iteration by adding additional attributes to the Data Vault 2.0 model and RapidMiner process. The conclusions state that Data Vault 2.0 provides a flexible data model that supports an agile approach to data mining projects by allowing incremental changes to the model and attributes.
Agile Data Engineering - Intro to Data Vault Modeling (2016)Kent Graziano
The document provides an introduction to Data Vault data modeling and discusses how it enables agile data warehousing. It describes the core structures of a Data Vault model including hubs, links, and satellites. It explains how the Data Vault approach provides benefits such as model agility, productivity, and extensibility. The document also summarizes the key changes in the Data Vault 2.0 methodology.
Agile BI via Data Vault and ModelstormingDaniel Upton
Audience: Business Intelligence Architects, Project Managers and Sponsors. This slideshow accompanies a video presentation of the same name, available at http://youtu.be/e0cHFdeGEeE.
Data Vault 2.0 DeMystified with Dan Linstedt and WhereScapeWhereScape
Join Dan Linstedt and WhereScape to learn the benefits that Data Vault 2.0 offers to data warehousing teams, what it is and isn't, and how data vault automation can help teams implement Data Vault 2.0 more quickly and successfully.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
The document introduces Visual DataVault, a modeling language for visually expressing Data Vault models. It aims to generate DDL from models and support Microsoft Office. The language defines basic entities like hubs, links, satellites and reference tables. It also covers query assistant tables, computed structures, exploration links and business vault tables to enhance the raw data vault. Some remarks note it focuses on logical not physical modeling and more features are planned.
The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
Chapter 10: Document and Content Management Ahmed Alorage
This document discusses document and content management. It covers concepts like document management, which involves storing, tracking, and controlling electronic and paper documents, and content management, which organizes and structures access to information content. The key activities covered are planning and policies for managing documents, implementing document management systems for storage, access and security, backup and recovery of documents, retention and disposition according to policies and regulations, and auditing document management. The document provides details on each of these concepts and activities.
This document discusses data operations management. It defines data operations management as developing, maintaining, and supporting structured data to maximize value. Key activities include database support and data technology management. Database administrators play an important role in ensuring database availability, performance, integrity, and recoverability through activities like backups, monitoring, tuning, and setting service level agreements.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
The document provides information on skills needed to be a database professional. It lists logical data modeling, translating logical models into real database systems, special design challenges like security and access, normalization from 1NF to 5NF, and tools for data modeling like ER-Studio and ER-Win as important skills. It also discusses star schemas and snowflake schemas for data warehousing, with star schemas being better for performance in most cases.
Data Vault 2.0: Using MD5 Hashes for Change Data CaptureKent Graziano
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 Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
Data Vault Modeling and Methodology introduction that I provided to a Montreal event in September 2011. It covers an introduction and overview of the Data Vault components for Business Intelligence and Data Warehousing. I am Dan Linstedt, the author and inventor of Data Vault Modeling and methodology.
If you use the images anywhere in your presentations, please credit http://LearnDataVault.com as the source (me).
Thank-you kindly,
Daniel Linstedt
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Agile Data Warehouse Modeling: Introduction to Data Vault Data ModelingKent Graziano
The document introduces Data Vault modeling as an agile approach to data warehousing. It discusses how Data Vault addresses some limitations of traditional dimensional modeling by allowing for more flexible, adaptable designs. The Data Vault model consists of three simple structures - hubs, links, and satellites. Hubs contain unique business keys, links represent relationships between keys, and satellites hold descriptive attributes. This structure supports incremental development and rapid changes to meet evolving business needs in an agile manner.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Chapter 1: The Importance of Data AssetsAhmed Alorage
The document summarizes Chapter 1 of the DAMA-DMBOK Guide, which discusses data as a vital enterprise asset and introduces key concepts in data management. It defines data, information, and knowledge; describes the data lifecycle and data management functions; and explains that data management is a shared responsibility between data stewards and professionals. It also provides overviews of the DAMA organization and the goals and audiences of the DAMA-DMBOK Guide.
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Power BI is a self-service business intelligence tool that allows users to analyze data and create reports and visualizations. It includes components for data discovery, analysis, and visualization both on-premises using Excel and in the cloud using the Power BI service. The tool integrates with Office 365 and allows users to discover, visualize, and share insights from data.
Presentation of use cases of Master Data Management for product Data. It presents the five facets of MDM for product Data (MDM for Material, MDM for Lean Managed Services, MDM for Regulated Products, Product Information Management, MDM for “Anything”) and how Talend platform for MDM can adress them
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
Data warehousing Demo PPTS | Over View | Introduction Kernel Training
This document provides an overview of data warehousing concepts including:
- Data warehousing involves collecting, integrating, and organizing data from multiple sources to support business intelligence and decision making.
- It discusses the differences between data, information, and knowledge and how they relate.
- Two common approaches to data warehousing are described - the Inmon approach involving a centralized data warehouse and the Kimball approach involving decentralized data marts.
- The roles and responsibilities of different types of data stores in a warehousing environment are outlined.
Data-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model provides a framework for organizations to evaluate their current data management capabilities, identify gaps, and develop a roadmap for process improvement. The webinar will describe the DMM model, which is based on the Capability Maturity Model and allows organizations to assess their maturity level across various data management practices. Attendees will learn about using the DMM to guide strategic improvements to their organizational data management.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords such as “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities o
This document provides an overview of data warehousing and dimensional modeling concepts. It defines key terms like data warehouse and data mart. It explores reasons for data warehousing like the need for an integrated company-wide view of information. It describes common data warehouse architectures and components of the star schema model. It also discusses topics like slowly changing dimensions, data visualization, and data mining.
1) The document discusses data warehousing and defines it as a subject-oriented, integrated collection of data used to support management decision making. It is not updatable and is refreshed periodically.
2) It describes reasons for implementing a data warehouse like having an integrated company-wide view of information and separating operational and informational systems.
3) Common data warehouse architectures include independent data marts, dependent data marts with an operational data store, and a three-layer architecture with an extraction-transformation-loading process. Dimensional data models like star schemas are also discussed.
Chapter 10: Document and Content Management Ahmed Alorage
This document discusses document and content management. It covers concepts like document management, which involves storing, tracking, and controlling electronic and paper documents, and content management, which organizes and structures access to information content. The key activities covered are planning and policies for managing documents, implementing document management systems for storage, access and security, backup and recovery of documents, retention and disposition according to policies and regulations, and auditing document management. The document provides details on each of these concepts and activities.
This document discusses data operations management. It defines data operations management as developing, maintaining, and supporting structured data to maximize value. Key activities include database support and data technology management. Database administrators play an important role in ensuring database availability, performance, integrity, and recoverability through activities like backups, monitoring, tuning, and setting service level agreements.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
The document provides information on skills needed to be a database professional. It lists logical data modeling, translating logical models into real database systems, special design challenges like security and access, normalization from 1NF to 5NF, and tools for data modeling like ER-Studio and ER-Win as important skills. It also discusses star schemas and snowflake schemas for data warehousing, with star schemas being better for performance in most cases.
Data Vault 2.0: Using MD5 Hashes for Change Data CaptureKent Graziano
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 Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
Data Vault Modeling and Methodology introduction that I provided to a Montreal event in September 2011. It covers an introduction and overview of the Data Vault components for Business Intelligence and Data Warehousing. I am Dan Linstedt, the author and inventor of Data Vault Modeling and methodology.
If you use the images anywhere in your presentations, please credit http://LearnDataVault.com as the source (me).
Thank-you kindly,
Daniel Linstedt
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Agile Data Warehouse Modeling: Introduction to Data Vault Data ModelingKent Graziano
The document introduces Data Vault modeling as an agile approach to data warehousing. It discusses how Data Vault addresses some limitations of traditional dimensional modeling by allowing for more flexible, adaptable designs. The Data Vault model consists of three simple structures - hubs, links, and satellites. Hubs contain unique business keys, links represent relationships between keys, and satellites hold descriptive attributes. This structure supports incremental development and rapid changes to meet evolving business needs in an agile manner.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Chapter 1: The Importance of Data AssetsAhmed Alorage
The document summarizes Chapter 1 of the DAMA-DMBOK Guide, which discusses data as a vital enterprise asset and introduces key concepts in data management. It defines data, information, and knowledge; describes the data lifecycle and data management functions; and explains that data management is a shared responsibility between data stewards and professionals. It also provides overviews of the DAMA organization and the goals and audiences of the DAMA-DMBOK Guide.
Business Intelligence made easy! This is the first part of a two-part presentation I prepared for one of our customers to help them understand what Business Intelligence is and what can it do...
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Power BI is a self-service business intelligence tool that allows users to analyze data and create reports and visualizations. It includes components for data discovery, analysis, and visualization both on-premises using Excel and in the cloud using the Power BI service. The tool integrates with Office 365 and allows users to discover, visualize, and share insights from data.
Presentation of use cases of Master Data Management for product Data. It presents the five facets of MDM for product Data (MDM for Material, MDM for Lean Managed Services, MDM for Regulated Products, Product Information Management, MDM for “Anything”) and how Talend platform for MDM can adress them
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
Data warehousing Demo PPTS | Over View | Introduction Kernel Training
This document provides an overview of data warehousing concepts including:
- Data warehousing involves collecting, integrating, and organizing data from multiple sources to support business intelligence and decision making.
- It discusses the differences between data, information, and knowledge and how they relate.
- Two common approaches to data warehousing are described - the Inmon approach involving a centralized data warehouse and the Kimball approach involving decentralized data marts.
- The roles and responsibilities of different types of data stores in a warehousing environment are outlined.
Data-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model provides a framework for organizations to evaluate their current data management capabilities, identify gaps, and develop a roadmap for process improvement. The webinar will describe the DMM model, which is based on the Capability Maturity Model and allows organizations to assess their maturity level across various data management practices. Attendees will learn about using the DMM to guide strategic improvements to their organizational data management.
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords such as “big data,” “NoSQL,” “data scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities o
This document provides an overview of data warehousing and dimensional modeling concepts. It defines key terms like data warehouse and data mart. It explores reasons for data warehousing like the need for an integrated company-wide view of information. It describes common data warehouse architectures and components of the star schema model. It also discusses topics like slowly changing dimensions, data visualization, and data mining.
1) The document discusses data warehousing and defines it as a subject-oriented, integrated collection of data used to support management decision making. It is not updatable and is refreshed periodically.
2) It describes reasons for implementing a data warehouse like having an integrated company-wide view of information and separating operational and informational systems.
3) Common data warehouse architectures include independent data marts, dependent data marts with an operational data store, and a three-layer architecture with an extraction-transformation-loading process. Dimensional data models like star schemas are also discussed.
The document defines a data warehouse as a subject-oriented, integrated collection of historical data used for decision making. It is non-updatable and periodically refreshed. A data mart is a limited-scope data warehouse. There are several data warehouse architectures including independent data marts, dependent data marts with an operational data store, and logical data marts with a real-time warehouse. The architectures involve extracting, transforming, and loading data from source systems.
This document defines data warehousing and data marts. It discusses the need for data warehousing to provide an integrated view of information from multiple sources. Various data warehouse architectures are described, including the two-level, independent data mart, dependent data mart with operational data store, and logical data mart architectures. The extract, transform, load process is explained. Dimensional data modeling using star schemas is also covered.
This document outlines the key topics covered in a chapter on operations management layout strategies. It includes an overview of different types of layouts for offices, retail stores, warehouses, production processes, and more. Specific layout strategies and considerations are discussed for each type, including objectives and examples. McDonald's innovations that involved layout changes are highlighted. Overall, the document provides an outline and introduction to the various layout strategies covered in the chapter.
This document provides information about a course on data warehousing and data mining, including:
1. It outlines the course syllabus which covers the basics of data warehousing, data preprocessing, association rules, classification and clustering, and recent trends in data mining.
2. It describes the 5 units that make up the course, including an overview of the topics covered in each unit such as data warehouse architecture, data integration, decision trees, and applications of data mining.
3. It lists two textbooks and four references that will be used for the course.
The document discusses various concepts related to data warehousing including:
1. The key characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-updatable.
2. Common data warehouse architectures including two-level, independent data marts, dependent data marts with an operational data store, logical data marts with an active warehouse, and a three-layer architecture.
3. The Extract, Transform, Load (ETL) process and data reconciliation to integrate and transform data from source systems into the data warehouse.
This document provides a checklist report on modernizing data warehouse infrastructure. It discusses six key points regarding modernization: 1) Diversifying the portfolio of data platforms to satisfy modern data requirements, 2) Modernizing with cloud and hybrid strategies, 3) Modernizing hardware for greater speed, scale and lower costs, 4) Coordinating modernization with business and analytics modernization, 5) Adjusting data management practices to fit modern warehousing, and 6) Leveraging multi-vendor partnerships for a unified, high-performance infrastructure. The report emphasizes that modern warehouses require multiple data platform types to meet diverse needs, and that infrastructure modernization is driven by business demands for advanced analytics and self-service data practices.
The document discusses data warehousing and OLAP (online analytical processing). It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used to support management decision making. The document outlines common data warehouse architectures like star schemas and snowflake schemas and discusses how data is modeled and organized in multidimensional data cubes. It also describes typical OLAP operations for analyzing and exploring cube data like roll-up, drill-down, slice and dice.
Data Mining Concept & Technique-ch04.pptMutiaSari53
This chapter discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented collection of integrated and nonvolatile data used for analysis. Key concepts covered include the multidimensional data cube model used to organize warehouse data, ETL processes for loading data into the warehouse, and star and snowflake schemas for conceptual modeling. The chapter also distinguishes between OLTP and OLAP systems and operations.
The document discusses the need for data warehousing and provides examples of how data warehousing can help companies analyze data from multiple sources to help with decision making. It describes common data warehouse architectures like star schemas and snowflake schemas. It also outlines the process of building a data warehouse, including data selection, preprocessing, transformation, integration and loading. Finally, it discusses some advantages and disadvantages of data warehousing.
I do not have enough context to answer the questions posed in the interactive session. The document provided is a chapter overview and does not describe any specific cases or companies. It primarily focuses on foundational database and business intelligence concepts.
The document outlines the agenda for a data warehousing training course. The agenda covers topics such as data warehouse structure and modeling, extract transform load (ETL) processes, dimensional modeling, aggregation, online analytical processing (OLAP), and data marts. Time is allocated to discuss loading, refreshing, and querying the data warehouse.
This document discusses data warehousing and OLAP (online analytical processing) technology. It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data to support management decision making. It describes how data warehouses use a multi-dimensional data model with facts and dimensions to organize historical data from multiple sources for analysis. Common data warehouse architectures like star schemas and snowflake schemas are also summarized.
Chapter 4. Data Warehousing and On-Line Analytical Processing.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
The document discusses a workshop on designing information systems for business organizations. It covers topics like the $10 billion industry shift towards information management, motivation for next generation databases, challenges of database technology, scenarios involving instant virtual enterprises and personalized information systems, and the aims and objectives of familiarizing participants with database development techniques.
This document discusses managing data as a critical organizational resource. It covers why data needs to be managed, including that organizations rely on data and replacing or reconciling inconsistent data can be costly. Technical aspects of data management include data modeling to map business data needs, different database architectures like relational and multidimensional, and tools for managing data like database management systems. The document also discusses managerial issues in data management, such as principles of separating data from applications and having data standards, as well as policies around data ownership, administration, and roles of database administrators.
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
The document discusses database management systems and databases. It covers fundamental concepts like data organization, database structures, relational databases, and database management system components. It also describes the roles and responsibilities of database administrators and the process of knowledge discovery in databases.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main