The document discusses the structure of data warehouses and data marts. It defines a data warehouse as a subject-oriented, integrated collection of time-variant data used for decision making. A data warehouse can be classified as lite, deluxe, or supreme based on its scope and technologies used. The document also defines a data mart as a scaled-down version of a data warehouse that can be sourced from a data warehouse or developed independently to meet specific user needs. It provides examples of how to implement and structure both data warehouses and data marts.
Improving Traffic Prediction Using Weather Data with Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
This document provides an introduction and overview of master data management (MDM). It begins with defining MDM as managing an organization's critical data. The agenda then outlines an overview of MDM, how it helps businesses succeed, and risks and challenges. It provides examples of master data and how MDM systems work. Key benefits of MDM include a single source of truth, reduced costs, and increased customer satisfaction by avoiding duplicate or inconsistent data across systems. Risks include data inconsistencies from mergers and acquisitions. Challenges involve determining what data to manage, ensuring consistency, and establishing appropriate data governance and information systems.
This document discusses the importance of effectively implementing marketing plans. It notes that the success of any marketing plan relies on how well it is implemented and managed. A marketing plan indicates objectives, strategies, and tactics for accomplishing planned activities and serves as a guide for implementation and control. The document outlines the annual marketing planning cycle of strategic planning, implementation, and evaluation. It emphasizes that people tasked with implementing the plan must have strong communication, teamwork, and motivational skills. Periodic reviews of key performance indicators are needed to identify any gaps between the plan and actual results for making necessary adjustments.
CDISC's CDASH and SDTM: Why You Need Both!Kit Howard
CDISC's clinical data standards are widely used for clinical research, but many people wonder why there seem to be two standards for collected data: the Clinical Data Acquisition Standards Harmonization (CDASH) standard and the Study Data Tabulation Model (SDTM) standard. This poster steps through four significant reasons that reflect the differences in philosophy, intermediate goals and broad-scale uses. Examples illustrate each reason and how they affect your studies.
Strategic management is the process of specifying an organization's objectives, developing policies to achieve those objectives, and allocating resources to implement the policies. It involves environmental scanning, strategy formulation, strategy implementation, and evaluation and control. Strategic decisions are made at the corporate, business unit, and functional levels. Strategic intent is reflected through an organization's vision, mission, objectives, and goals. The strategic management process involves analyzing the environment, identifying strategic alternatives, choosing a strategy, implementing it, and evaluating performance. Mintzberg proposed that strategies can emerge through deliberate planning or as patterns from actions and decisions over time.
How to build ADaM BDS dataset from mock up tableKevin Lee
This document provides instructions for building ADaM basic data structures (BDS) from annotated mock up tables. It discusses how to design mock up tables based on the statistical analysis plan, annotate the tables, create metadata, and then build the ADaM BDS datasets according to the metadata. The process results in analysis-ready ADaM datasets where all numbers in the final report can be calculated with one SAS procedure. An example is provided demonstrating how to annotate a mock up table and extract the necessary variables and parameters to include in the ADaM datasets and metadata.
The document discusses common issues companies face when trying to meet business reporting needs from their data. It notes that companies often end up with thousands of reports due to:
1) Building reports directly from transactional data sources rather than consolidating data into a data warehouse optimized for analysis and reporting.
2) Creating denormalized tables within the data warehouse for each individual report, leading to inconsistent, hard to manage data.
3) Resolving this by building separate data marts with a dimensional model, but this still resulted in fragmented, redundant data structures.
The ideal approach would have been to design a single centralized data warehouse from the start using a dimensional model to provide a consistent, integrated view of
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.
Improving Traffic Prediction Using Weather Data with Ramya RaghavendraSpark Summit
As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.
This document provides an introduction and overview of master data management (MDM). It begins with defining MDM as managing an organization's critical data. The agenda then outlines an overview of MDM, how it helps businesses succeed, and risks and challenges. It provides examples of master data and how MDM systems work. Key benefits of MDM include a single source of truth, reduced costs, and increased customer satisfaction by avoiding duplicate or inconsistent data across systems. Risks include data inconsistencies from mergers and acquisitions. Challenges involve determining what data to manage, ensuring consistency, and establishing appropriate data governance and information systems.
This document discusses the importance of effectively implementing marketing plans. It notes that the success of any marketing plan relies on how well it is implemented and managed. A marketing plan indicates objectives, strategies, and tactics for accomplishing planned activities and serves as a guide for implementation and control. The document outlines the annual marketing planning cycle of strategic planning, implementation, and evaluation. It emphasizes that people tasked with implementing the plan must have strong communication, teamwork, and motivational skills. Periodic reviews of key performance indicators are needed to identify any gaps between the plan and actual results for making necessary adjustments.
CDISC's CDASH and SDTM: Why You Need Both!Kit Howard
CDISC's clinical data standards are widely used for clinical research, but many people wonder why there seem to be two standards for collected data: the Clinical Data Acquisition Standards Harmonization (CDASH) standard and the Study Data Tabulation Model (SDTM) standard. This poster steps through four significant reasons that reflect the differences in philosophy, intermediate goals and broad-scale uses. Examples illustrate each reason and how they affect your studies.
Strategic management is the process of specifying an organization's objectives, developing policies to achieve those objectives, and allocating resources to implement the policies. It involves environmental scanning, strategy formulation, strategy implementation, and evaluation and control. Strategic decisions are made at the corporate, business unit, and functional levels. Strategic intent is reflected through an organization's vision, mission, objectives, and goals. The strategic management process involves analyzing the environment, identifying strategic alternatives, choosing a strategy, implementing it, and evaluating performance. Mintzberg proposed that strategies can emerge through deliberate planning or as patterns from actions and decisions over time.
How to build ADaM BDS dataset from mock up tableKevin Lee
This document provides instructions for building ADaM basic data structures (BDS) from annotated mock up tables. It discusses how to design mock up tables based on the statistical analysis plan, annotate the tables, create metadata, and then build the ADaM BDS datasets according to the metadata. The process results in analysis-ready ADaM datasets where all numbers in the final report can be calculated with one SAS procedure. An example is provided demonstrating how to annotate a mock up table and extract the necessary variables and parameters to include in the ADaM datasets and metadata.
The document discusses common issues companies face when trying to meet business reporting needs from their data. It notes that companies often end up with thousands of reports due to:
1) Building reports directly from transactional data sources rather than consolidating data into a data warehouse optimized for analysis and reporting.
2) Creating denormalized tables within the data warehouse for each individual report, leading to inconsistent, hard to manage data.
3) Resolving this by building separate data marts with a dimensional model, but this still resulted in fragmented, redundant data structures.
The ideal approach would have been to design a single centralized data warehouse from the start using a dimensional model to provide a consistent, integrated view of
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.
This document provides an overview of dimensional modeling techniques for data warehousing. It defines key concepts like facts, dimensions, and star schemas. Facts contain measures about business processes, dimensions provide context for slicing and dicing facts, and star schemas arrange facts and dimensions into a shape resembling a star. The presentation emphasizes best practices like identifying business processes, determining grain, conforming dimensions, and avoiding over-normalization. It also covers dimension types, slowly changing dimensions, and techniques for handling complex modeling scenarios. The goal is to introduce fundamental dimensional modeling concepts and principles in a practical yet non-technical manner.
The document discusses SQL Parallel Data Warehouse (PDW), which is a massively parallel processing appliance for large data warehousing workloads. It describes the different types of nodes in PDW, including control nodes that manage query execution, compute nodes that store and process data, and administrative nodes. The document also explains how PDW uses a hub and spoke architecture with the PDW appliance acting as a central data hub and individual data marts acting as spokes optimized for different user groups.
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.
This document provides an overview of a course on data warehousing, data mining, and decision support. It discusses what data warehousing is, how it differs from operational transaction processing systems, and the processes involved like data extraction, transformation, loading and refreshing the warehouse. It also covers warehouse architecture, design considerations, and multidimensional data modeling. Examples from Walmart's data warehouse implementation are provided to illustrate real-world warehouse concepts and capabilities.
This document provides an overview of dimensional modeling techniques for data warehouse design, including what a data warehouse is, how dimensional modeling fits into the data presentation area, and some of the key concepts and components of dimensional modeling such as facts, dimensions, and star schemas. It also discusses design concepts like snowflake schemas, slowly changing dimensions, and conformed dimensions.
Best practices and tips on how to design and develop a Data Warehouse using Microsoft SQL Server BI products.
This presentation describes the inception and full lifecycle of the Carl Zeiss Vision corporate enterprise data warehouse.
Technologies covered include:
•Using SQL Server 2008 as your data warehouse DB
•SSIS as your ETL Tool
•SSAS as your data cube Tool
You will Learn:
•How to Architect a data warehouse system from End-to-End
•Components of the data warehouse and functionality
•How to Profile data and understand your source systems
•Whether to ODS or not to ODS (Determining if a operational Data Store is required)
•The staging area of the data warehouse
•How to Build the data warehouse – Designing Dimensions and Fact tables
•The Importance of using Conformed Dimensions
•ETL – Moving data through your data warehouse system
•Data Cubes - OLAP
•Lessons learned from Zeiss and other projects
Difference between ER-Modeling and Dimensional ModelingAbdul Aslam
Entity relationship (ER) modeling and dimensional modeling (DM) are different logical design techniques. ER modeling seeks to eliminate data redundancy and shows relationships between data, while DM presents data in a standard framework that allows for high performance access. The key differences are that ER modeling contains both logical and physical models, processes normalized data for online transaction processing databases, uses current data with many users, and has smaller and volatile storage, while DM contains only a physical model, processes denormalized data for data warehousing, uses historical data for top management, and has larger and non-volatile storage.
Cassandra By Example: Data Modelling with CQL3Eric Evans
CQL is the query language for Apache Cassandra that provides an SQL-like interface. The document discusses the evolution from the older Thrift RPC interface to CQL and provides examples of modeling tweet data in Cassandra using tables like users, tweets, following, followers, userline, and timeline. It also covers techniques like denormalization, materialized views, and batch loading of related data to optimize for common queries.
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
The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.
Data warehousing combines data from multiple sources into a single database to provide businesses with analytics results from data mining, OLAP, scorecarding and reporting. It extracts, transforms and loads data from operational data stores and data marts into a data warehouse and staging area to integrate and store large amounts of corporate data. Data mining analyzes large databases to extract previously unknown and potentially useful patterns and relationships to improve business processes.
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
1. A data lake is a storage repository that holds vast amounts of raw data in its native format until it is needed for analysis. It addresses challenges of big data by allowing data to be stored and analyzed together without upfront structuring.
2. Traditional data warehouses structure data upfront, limiting flexibility. A data lake avoids this by storing all data as-is and analyzing data when questions arise. This provides greater analytic power on emerging big data sources.
3. While data lakes provide benefits like reduced costs and more flexibility, challenges remain around metadata management, governance, preparation, and security when storing all raw data in one place. Effective solutions are needed for these challenges to realize the full potential of data lakes.
History, definition, need, attributes, applications of data warehousing ; difference between data mining, big data, database and data warehouse ; future scope
This document discusses data warehousing and data mining. It defines data warehousing as combining data from multiple sources into a single database for analysis. Data warehousing provides businesses with analytics from data mining, OLAP, scorecarding and reporting. It also discusses the need for data warehousing to gather information from various sources. Common components of data warehousing architectures include extracting, transforming and loading data, as well as operational data stores, data warehouses, data marts and ETL processes. Finally, the document outlines typical applications of data mining such as customer relationship management, medical research, and combating terrorism.
The document provides an overview of data warehousing concepts introduced by Bill Inmon, who is considered the father of data warehousing. It defines a data warehouse as a collection of integrated subject-oriented databases designed to support decision-making. An operational data store feeds raw data to the data warehouse. Data marts contain targeted subsets of data for specific user groups. Metadata provides data about the structure and meaning of data within the warehouse.
The document provides an overview of data warehousing concepts including:
- William Inmon is considered the "father of data warehousing" and has written extensively on the topic.
- A data warehouse is a collection of integrated subject-oriented databases designed to support decision-making. It contains non-volatile, time-variant data from one or more sources.
- An operational data store feeds the data warehouse with a stream of raw data. A data mart offers targeted access to a subset of warehouse data. Metadata provides data about the structure and meaning of warehouse data.
A data warehouse integrates data from multiple sources into a central repository used for creating trend reports for senior management. It stores current and historical data. Data marts extract specific data from the data warehouse to provide quicker access to frequently used data for groups of users, improving response time at a lower cost than a full data warehouse.
The document provides an overview of the key components of a data warehouse, including:
1) The source data component which sources data from operational systems, internal/archived data, and external sources.
2) The data staging component which performs ETL (extraction, transformation, and loading) of data including cleaning, standardizing, and loading the data.
3) The data storage component which stores historical data from various sources in a separate repository with structures suitable for analysis.
4) The information delivery component which provides reports, complex queries, OLAP analysis, and data to applications like EIS and data mining tools.
5) The metadata component which contains operational, extraction/transformation, and end
William Inmon is considered the father of data warehousing. He has over 35 years of experience in database technology and data warehouse design. Inmon has written over 650 articles and published 45 books on topics related to building, using, and maintaining data warehouses and information factories. A data warehouse is a collection of integrated, subject-oriented databases designed to support decision-making. It contains data that is non-volatile, time-variant, integrated, and summarized for analysis. Key components of a data warehouse environment include the data store, data marts, and metadata.
This document provides an overview of dimensional modeling techniques for data warehousing. It defines key concepts like facts, dimensions, and star schemas. Facts contain measures about business processes, dimensions provide context for slicing and dicing facts, and star schemas arrange facts and dimensions into a shape resembling a star. The presentation emphasizes best practices like identifying business processes, determining grain, conforming dimensions, and avoiding over-normalization. It also covers dimension types, slowly changing dimensions, and techniques for handling complex modeling scenarios. The goal is to introduce fundamental dimensional modeling concepts and principles in a practical yet non-technical manner.
The document discusses SQL Parallel Data Warehouse (PDW), which is a massively parallel processing appliance for large data warehousing workloads. It describes the different types of nodes in PDW, including control nodes that manage query execution, compute nodes that store and process data, and administrative nodes. The document also explains how PDW uses a hub and spoke architecture with the PDW appliance acting as a central data hub and individual data marts acting as spokes optimized for different user groups.
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.
This document provides an overview of a course on data warehousing, data mining, and decision support. It discusses what data warehousing is, how it differs from operational transaction processing systems, and the processes involved like data extraction, transformation, loading and refreshing the warehouse. It also covers warehouse architecture, design considerations, and multidimensional data modeling. Examples from Walmart's data warehouse implementation are provided to illustrate real-world warehouse concepts and capabilities.
This document provides an overview of dimensional modeling techniques for data warehouse design, including what a data warehouse is, how dimensional modeling fits into the data presentation area, and some of the key concepts and components of dimensional modeling such as facts, dimensions, and star schemas. It also discusses design concepts like snowflake schemas, slowly changing dimensions, and conformed dimensions.
Best practices and tips on how to design and develop a Data Warehouse using Microsoft SQL Server BI products.
This presentation describes the inception and full lifecycle of the Carl Zeiss Vision corporate enterprise data warehouse.
Technologies covered include:
•Using SQL Server 2008 as your data warehouse DB
•SSIS as your ETL Tool
•SSAS as your data cube Tool
You will Learn:
•How to Architect a data warehouse system from End-to-End
•Components of the data warehouse and functionality
•How to Profile data and understand your source systems
•Whether to ODS or not to ODS (Determining if a operational Data Store is required)
•The staging area of the data warehouse
•How to Build the data warehouse – Designing Dimensions and Fact tables
•The Importance of using Conformed Dimensions
•ETL – Moving data through your data warehouse system
•Data Cubes - OLAP
•Lessons learned from Zeiss and other projects
Difference between ER-Modeling and Dimensional ModelingAbdul Aslam
Entity relationship (ER) modeling and dimensional modeling (DM) are different logical design techniques. ER modeling seeks to eliminate data redundancy and shows relationships between data, while DM presents data in a standard framework that allows for high performance access. The key differences are that ER modeling contains both logical and physical models, processes normalized data for online transaction processing databases, uses current data with many users, and has smaller and volatile storage, while DM contains only a physical model, processes denormalized data for data warehousing, uses historical data for top management, and has larger and non-volatile storage.
Cassandra By Example: Data Modelling with CQL3Eric Evans
CQL is the query language for Apache Cassandra that provides an SQL-like interface. The document discusses the evolution from the older Thrift RPC interface to CQL and provides examples of modeling tweet data in Cassandra using tables like users, tweets, following, followers, userline, and timeline. It also covers techniques like denormalization, materialized views, and batch loading of related data to optimize for common queries.
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
The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.
Data warehousing combines data from multiple sources into a single database to provide businesses with analytics results from data mining, OLAP, scorecarding and reporting. It extracts, transforms and loads data from operational data stores and data marts into a data warehouse and staging area to integrate and store large amounts of corporate data. Data mining analyzes large databases to extract previously unknown and potentially useful patterns and relationships to improve business processes.
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
1. A data lake is a storage repository that holds vast amounts of raw data in its native format until it is needed for analysis. It addresses challenges of big data by allowing data to be stored and analyzed together without upfront structuring.
2. Traditional data warehouses structure data upfront, limiting flexibility. A data lake avoids this by storing all data as-is and analyzing data when questions arise. This provides greater analytic power on emerging big data sources.
3. While data lakes provide benefits like reduced costs and more flexibility, challenges remain around metadata management, governance, preparation, and security when storing all raw data in one place. Effective solutions are needed for these challenges to realize the full potential of data lakes.
History, definition, need, attributes, applications of data warehousing ; difference between data mining, big data, database and data warehouse ; future scope
This document discusses data warehousing and data mining. It defines data warehousing as combining data from multiple sources into a single database for analysis. Data warehousing provides businesses with analytics from data mining, OLAP, scorecarding and reporting. It also discusses the need for data warehousing to gather information from various sources. Common components of data warehousing architectures include extracting, transforming and loading data, as well as operational data stores, data warehouses, data marts and ETL processes. Finally, the document outlines typical applications of data mining such as customer relationship management, medical research, and combating terrorism.
The document provides an overview of data warehousing concepts introduced by Bill Inmon, who is considered the father of data warehousing. It defines a data warehouse as a collection of integrated subject-oriented databases designed to support decision-making. An operational data store feeds raw data to the data warehouse. Data marts contain targeted subsets of data for specific user groups. Metadata provides data about the structure and meaning of data within the warehouse.
The document provides an overview of data warehousing concepts including:
- William Inmon is considered the "father of data warehousing" and has written extensively on the topic.
- A data warehouse is a collection of integrated subject-oriented databases designed to support decision-making. It contains non-volatile, time-variant data from one or more sources.
- An operational data store feeds the data warehouse with a stream of raw data. A data mart offers targeted access to a subset of warehouse data. Metadata provides data about the structure and meaning of warehouse data.
A data warehouse integrates data from multiple sources into a central repository used for creating trend reports for senior management. It stores current and historical data. Data marts extract specific data from the data warehouse to provide quicker access to frequently used data for groups of users, improving response time at a lower cost than a full data warehouse.
The document provides an overview of the key components of a data warehouse, including:
1) The source data component which sources data from operational systems, internal/archived data, and external sources.
2) The data staging component which performs ETL (extraction, transformation, and loading) of data including cleaning, standardizing, and loading the data.
3) The data storage component which stores historical data from various sources in a separate repository with structures suitable for analysis.
4) The information delivery component which provides reports, complex queries, OLAP analysis, and data to applications like EIS and data mining tools.
5) The metadata component which contains operational, extraction/transformation, and end
William Inmon is considered the father of data warehousing. He has over 35 years of experience in database technology and data warehouse design. Inmon has written over 650 articles and published 45 books on topics related to building, using, and maintaining data warehouses and information factories. A data warehouse is a collection of integrated, subject-oriented databases designed to support decision-making. It contains data that is non-volatile, time-variant, integrated, and summarized for analysis. Key components of a data warehouse environment include the data store, data marts, and metadata.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
A data mart is a smaller subset of data from a data warehouse that is tailored to a specific business unit or function. It provides faster access to relevant data than searching an entire data warehouse. There are three main types of data marts - dependent, which get data from a data warehouse; independent, which access data directly from sources; and hybrid, which integrate multiple data sources. Data marts use either a star or snowflake schema to logically structure the data in dimension and fact tables for analysis. Implementing a data mart involves designing it, constructing the logical and physical structures, transferring data using ETL tools, configuring access, and ongoing management.
A data warehouse consists of several key components:
- Current detail data from operational systems of record which is stored for analysis.
- Integration and transformation programs that convert operational data into a common format for the data warehouse.
- Summarized and archived data used for reporting and analysis over time.
- Metadata that describes the structure and meaning of the data.
Data warehouses are used for standard reporting, queries on summarized data, and data mining of patterns in large datasets to gain business insights.
The document discusses the purpose and history of data warehousing. It defines a data warehouse as a centralized, well-managed environment for storing high-value data from various sources. The data warehouse processes this data into a format optimized for analysis and information processing. The data warehouse has evolved from mainframe-based systems in the 1970s to today's cost-effective solutions embedded in software. A data warehouse is not defined by its size but by its functionality and ability to meet business objectives through consolidated, consistent data.
The document discusses the purpose and history of data warehousing. It defines a data warehouse as a centralized, well-managed environment for storing high-value data from various sources. The data warehouse processes this data into a format optimized for analysis and information processing. The data warehouse has evolved from mainframe-based systems in the 1970s to today's cost-effective solutions embedded in software. A data warehouse is not defined by its size but by its functionality and ability to meet business objectives through consolidated, consistent data.
Chapter 13The Data WarehouseBLCN-534 Fundamentals o.docxketurahhazelhurst
Chapter 13
The Data Warehouse
BLCN-534: Fundamentals of Database Systems
Chapter ObjectivesCompare the data needs of transaction processing systems with those of decision support systems. Describe the data warehouse concept and list its main features. Compare the enterprise data warehouse with the data mart. Design a data warehouse. Build a data warehouse, including the steps of data extraction, data cleaning, data transformation, and data loading. Describe how to use a data warehouse with online analytic processing and data mining.
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Chapter Objectives
List the types of expertise needed to administer a data warehouse.
List the challenges in data warehousing.
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Application SystemsTransaction Processing Systems (TPS)Everyday application systems that support banking and insurance operations, manage the parts inventory on manufacturing assembly lines, keep track of airline and hotel reservations, support Web-based sales, etc.
Decision Support Systems (DSS)specifically designed to aid managers in decision-making tasks.
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The Data Warehouse Concept
A data warehouse is a broad-based, shared database for management decision making that contains data that has been accumulated over time.
Formally, a database warehouse is, “a subject oriented, integrated, non-volatile, and time variant collection of data in support of management’s decisions.”
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Characteristics of
Data Warehouse DataThe data is subject orientedThe data is integratedThe data is non-volatileThe data is time variantThe data must be high qualityThe data may be aggregatedThe data is often denormalizedThe data is not necessarily absolutely current
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The Data is Subject Oriented
Data warehouses are organized around subjects, really the major entities of concern in the business environment.Sales, customers, orders, claims, accounts, employees, other entities that are central to the company’s business.
13-*
The Data is Integrated
Data about each of the subjects in the data warehouse is typically collected from several of the company’s transactional databases, each of which supports one or more applications that have something to do with the particular subject.
All of the data about a subject must be organized or integrated in such a way that it provides a unified, overall picture of all the important details about the subject over time.
Data from disparate application databases must be transformed into common measurements, codes, data types.
13-*
The Data is Non-Volatile
Once data is added to the data warehouse, it doesn’t change.
It will never change. Changing it would be like going back and rewriting history.
13-*
The Data is Time Variant
Data warehouse data, with its historic nature, always includes some kind of a timestamp.
If we are storing sales data on a weekly or monthly basis and we have accumulated ten years of such historic data, each weekly or monthly sales figure must be accompanied by a timestamp indicating the week or month (and ...
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1. SUBMITTED TO: SUBMITTED BY:
MR. ASHOK WAHI NIKISHA GUPTA
CHANDNI RASTOGI
SAKSHI JAIN
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 1
2. DATA WAREHOUSE
A subject-oriented, integrated, time-variant, non-updatable collection
of data used in support of management decision-making processes.
Subject-oriented: Customers, patients, students, products.
Integrated: Consistent naming conventions, formats, encoding
structures; from multiple data sources.
Time-variant: Can study trends and changes.
Non-updatable: Read-only, periodically refreshed; never deleted.
A data warehouse is a home for your high-value data, or data
assets, that originates in other corporate applications, such as the one
your company uses to fill customer orders for its products, or some
data source external to your company, such as a public database that
contains sales information gathered from all your competitors.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 2
3. CLASSIFICATION OF DATA
WAREHOUSE
Each of these classifications of data warehouses implements various aspects of an
overall data warehousing architecture are:
Data warehouse lite: A relatively straightforward implementation of a modest
scope (often, for a small user group or team) in which you don’t go out on any
technological limbs; almost a low-tech implementation.
Data warehouse deluxe: A standard data warehouse implementation that uses
advanced technologies to solve complex business information and analytical
needs across a broader user population.
Data warehouse supreme: A data warehouse that has large-scale data
distribution and advanced technologies that can integrate various “run the
business” systems, improving the overall quality of the data assets across
business information analytical needs and transactional needs.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 3
4. 2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 4
5. This architecture assures that your data warehouse meets your user’s information
requirements and focuses on the following business organization and technical-
architecture presentation components:
Subject area and data content: A subject area is a high-level grouping of data
content that relates to a major area of business interests, such as customers, products,
sales orders, and contracts.
Data source: Data sources are very similar to raw materials that support the
creation of finished goods in manufacturing.
Business intelligence tools: The user’s requirements for information access
dictate the type of business intelligence tool deployed for your data warehouse.
Some users require only simple querying or reporting on the data content within a
subject area; others might require sophisticated analytics. These data access
requirements assist in classifying your data warehouse.
Database: The database refers to the technology of choice leveraged to manage
the data content within a set of target data structures.
Data integration: Data integration is a broad classification for the extraction,
movement, transformation, and loading of data from the data’s source into the target
database.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 5
6. DATA WAREHOUSE LITE
A data warehouse lite is a no-frills, bare-bones, low-tech approach to providing
data that can help with some of your business decision-making. No-frills
means that you put together, wherever possible, proven capabilities and
tools already within your organization to build your system.
Figure: A data warehouse lite has a narrow subject area focus.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 6
7. Denormalizing data from a single application restructures that data to make it more
conducive to reporting needs.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 7
8. The low-tech approach to moving data into a data warehouse lite database
backup tapes.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 8
9. The architecture of a data warehouse lite is built around straight-line
movement of data.
STRUCTURE OF DATA WAREHOUSE & DATA
2/29/2012 MARTS 9
10. DATA WAREHOUSE DELUXE
A data warehouse deluxe has a broader subject area focus than a data warehouse
lite.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 10
11. A data warehouse deluxe often has a complicated architecture with many different
collection points for data.
STRUCTURE OF DATA WAREHOUSE & DATA
2/29/2012 MARTS 11
12. DATA WAREHOUSE SUPREME
Intelligent agents are an important part of the push technology architecture of a
data warehouse supreme.
STRUCTURE OF DATA WAREHOUSE & DATA
2/29/2012 MARTS 12
13. Sample architecture from a data warehouse supreme (although it can look like
just about anything).
STRUCTURE OF DATA WAREHOUSE & DATA
2/29/2012 MARTS 13
14. A data warehouse might consist of more than one database, under the control of
the overall warehousing environment.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 14
15. DATA MART
A data mart is simply a scaled-down data warehouse.The idea of a
data mart is hardly revolutionary, despite what you might read on
blogs and in the computer trade press, and what you might hear at
conferences or seminars.
There are three main approaches to create a data mart:
✓ Sourced by a data warehouse (most or all of the data mart’s
contents come from a data warehouse)
Quickly developed and created from scratch
Developed from scratch with an eye toward eventual integration
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 15
16. Data marts sourced by a data warehouse
Many data warehousing experts would argue (and I’m one of them, in this
case) that a true data mart is a “retail outlet,” and a data warehouse
provides its contents.
The data sources, data warehouse, data mart, and user interact in this way:
The data sources, acting as suppliers of raw materials, send data into the
data warehouse.
The data warehouse serves as a consolidation and distribution center,
collecting the raw materials in much the same way that any data
warehouse does.
Instead of the user (the consumer) going straight to the data warehouse,
though, the data warehouse serves as a wholesaler with the premise of “we
sell only to retailers, not directly to the public.” In this case, the retailers
are the data marts.
The data marts order data from the warehouse and, after stocking the
newly acquired information, make it available to consumers (users).
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 16
17. The retail-outlet approach to data marts: All the data comes
from a data warehouse.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 17
18. In a variation of the sourced-from-the-warehouse model, the data
warehouse that serves as the source for the data mart doesn’t have
all the information the data mart’s users need. You can solve this
problem in one of two ways:
Supplement the missing information directly into the data
warehouse before sending the selected contents to the data mart.
Don’t touch the data warehouse; instead, add the supplemental
information to the data mart in addition to what it receives from the
data warehouse.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 18
19. Top-down, quick-strike data marts
There are three reasons to go the data-mart route:
Speed: A quick-strike data mart is typically completed in 90
to 120 days, rather than the much longer time required for a
full-scale data warehouse.
Cost: Doing the job faster means that you spend less money;
it’s that simple.
Complexity and risk: When you work with less data and
fewer sources over a shorter period, you’re likely to create a
significantly less complex environment — and have fewer
associated risks.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 19
20. A top-down, quick-strike data mart is a subset of what can be built
if you pursue full scale data warehousing instead.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 20
21. Bottom-up, integration-oriented
data marts
Theoretically, you can design data marts so that they’re
eventually integrated in a bottom-up manner by building a data
warehousing environment (in contrast to a single, monolithic
data warehouse).
Bottom-up integration of data marts isn’t for the
fainthearted. You can do it, but it’s more difficult than creating
a top-down, quick-strike data mart that will always remain
stand-alone. You might be able to successfully use this
approach . . . but you might not.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 21
22. SUBSETS OF INFORMATION FOR
DATA MART
Geography-bounded data: A data mart might contain only the information
relevant to a certain geographical area, such as a region or territory within your
company.
Organization-bounded data: When deciding what you want to put in your data
mart, you can base decisions on what information a specific organization needs
when it’s the sole (or, at least, primary) user of the data mart. This approach
works well when the overwhelming majority of inquiries and reports are
organization-oriented. For example, the commercial checking group has no need
whatsoever to analyze consumer checking accounts and vice versa.
Function-bounded data: Using an approach that crosses organizational
boundaries, you can establish a data mart’s contents based on a specific function
(or set of related functions) within the company. A multinational chemical
company, for example, might create a data mart exclusively for the sales and
marketing functions across all organizations and across all product lines.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 22
23. Market-bounded data: A company might occasionally be so
focused on a specific market and the associated competitors that it
makes sense to create a data mart oriented with that particular focus.
This type of environment might include competitive sales, all
available public information about the market and competitors
(particularly if you can find this information on the Internet), and
industry analysts’ reports, for example.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 23
24. Data mart or data warehouse?
If you start a project from the outset with either of the following
premises, you already have two strikes against you:
“We’re building a real data warehouse, not a puny little data mart.”
“We’re building a data mart, not a data warehouse.”
Until you understand the following three issues, you have no
foundation on which to classify your impending project as either a
data mart or a data warehouse:
The volumes and characteristics of data you need
The business problems you’re trying to solve and the questions
you’re trying to answer
The business value you expect to gain when your system is
successfully built
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 24
25. IMPLEMENTING A DATA MART
There are the three keys to speedy implementation:
Follow an iterative, phased methodology.
Hold to a fixed time for each phase.
Avoid scope creep at all costs.
2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 25
26. 2/29/2012 STRUCTURE OF DATA WAREHOUSE & DATA MARTS 26