Fact less fact Tables & Aggregate Tables Sunita Sahu
Factless fact tables record events like student attendance or meeting participation without numeric facts. They contain only foreign keys to associated dimensions. Aggregate fact tables contain pre-calculated summaries derived from the lowest level fact table. Having the fact table at the lowest grain allows retrieving large result sets from the data warehouse more efficiently than querying the operational system. Aggregate tables reduce fact table size and the need to aggregate data during queries.
Aggregates are precalculated summaries derived from fact tables at the lowest level of granularity, where facts are captured at the most detailed level possible from operational systems. Choosing a star schema with a fact table at the lowest grain has advantages, as it allows producing large result sets from a simple structure with dimension tables connected to a most granular fact table reflecting single orders, invoices, and products from operational systems. This requires aggregating the fact table to address the large size and need for aggregated views of the data.
The document discusses strategies for improving data warehouse performance through the use of aggregate tables. It describes how aggregate tables summarize fact data across dimension attributes to reduce the number of rows that must be accessed by queries. The key points covered include: defining potential aggregate tables based on fact and dimension grains; identifying aggregates that can benefit common queries and drill-across reports; and assessing aggregates based on the number of rows summarized and potential user benefits to optimize performance gains.
This document discusses different types of slowly changing dimensions in a data warehouse: Type 1, Type 2, and Type 3. Type 1 dimensions involve corrections to existing data. Type 2 dimensions track true changes over time by adding new rows. Type 3 dimensions store both old and new attribute values in the same row. The document also covers junk dimensions, large dimensions, and rapidly changing dimensions.
A fact table is the central table in a star schema data warehouse. It contains foreign keys that join to dimension tables and measures containing analyzed data. Measures can be additive, semi-additive, or non-additive depending on whether they can be summed across all dimensions. A factless fact table is also possible, containing only dimension keys to represent intersections but no measures.
The document discusses dimensional modeling versus entity relationship (ER) modeling. Dimensional modeling uses a denormalized structure that is optimized for select queries, while ER modeling uses normalization to reduce redundancy and is optimized for transactions. Case studies are presented showing dimensional modeling implementations with technologies like SQL Server and Teradata. Skills in dimensional modeling techniques, extract-transform-load processes, and reporting with dimensional models are discussed.
The document defines conceptual, logical, and physical data models and compares their key features. A conceptual model shows entities and relationships without attributes or keys. A logical model adds attributes, primary keys, and foreign keys. A physical model specifies tables, columns, data types, and other implementation details.
The document discusses dimensional modeling and star schemas for data warehousing. It describes how requirements are used to design dimensional models, including choosing dimensions, grains, and facts. The key aspects of a star schema are presented, including fact tables containing measurements and dimension tables containing business context. Slowly changing dimensions, large dimensions, and snowflake schemas are also covered. Aggregate fact tables and fact constellations are introduced as extensions of the star schema.
Fact less fact Tables & Aggregate Tables Sunita Sahu
Factless fact tables record events like student attendance or meeting participation without numeric facts. They contain only foreign keys to associated dimensions. Aggregate fact tables contain pre-calculated summaries derived from the lowest level fact table. Having the fact table at the lowest grain allows retrieving large result sets from the data warehouse more efficiently than querying the operational system. Aggregate tables reduce fact table size and the need to aggregate data during queries.
Aggregates are precalculated summaries derived from fact tables at the lowest level of granularity, where facts are captured at the most detailed level possible from operational systems. Choosing a star schema with a fact table at the lowest grain has advantages, as it allows producing large result sets from a simple structure with dimension tables connected to a most granular fact table reflecting single orders, invoices, and products from operational systems. This requires aggregating the fact table to address the large size and need for aggregated views of the data.
The document discusses strategies for improving data warehouse performance through the use of aggregate tables. It describes how aggregate tables summarize fact data across dimension attributes to reduce the number of rows that must be accessed by queries. The key points covered include: defining potential aggregate tables based on fact and dimension grains; identifying aggregates that can benefit common queries and drill-across reports; and assessing aggregates based on the number of rows summarized and potential user benefits to optimize performance gains.
This document discusses different types of slowly changing dimensions in a data warehouse: Type 1, Type 2, and Type 3. Type 1 dimensions involve corrections to existing data. Type 2 dimensions track true changes over time by adding new rows. Type 3 dimensions store both old and new attribute values in the same row. The document also covers junk dimensions, large dimensions, and rapidly changing dimensions.
A fact table is the central table in a star schema data warehouse. It contains foreign keys that join to dimension tables and measures containing analyzed data. Measures can be additive, semi-additive, or non-additive depending on whether they can be summed across all dimensions. A factless fact table is also possible, containing only dimension keys to represent intersections but no measures.
The document discusses dimensional modeling versus entity relationship (ER) modeling. Dimensional modeling uses a denormalized structure that is optimized for select queries, while ER modeling uses normalization to reduce redundancy and is optimized for transactions. Case studies are presented showing dimensional modeling implementations with technologies like SQL Server and Teradata. Skills in dimensional modeling techniques, extract-transform-load processes, and reporting with dimensional models are discussed.
The document defines conceptual, logical, and physical data models and compares their key features. A conceptual model shows entities and relationships without attributes or keys. A logical model adds attributes, primary keys, and foreign keys. A physical model specifies tables, columns, data types, and other implementation details.
The document discusses dimensional modeling and star schemas for data warehousing. It describes how requirements are used to design dimensional models, including choosing dimensions, grains, and facts. The key aspects of a star schema are presented, including fact tables containing measurements and dimension tables containing business context. Slowly changing dimensions, large dimensions, and snowflake schemas are also covered. Aggregate fact tables and fact constellations are introduced as extensions of the star schema.
Case study: Implementation of dimension table and fact tablechirag patil
Dimensional modeling is a database structure used for data warehousing that organizes data into fact and dimension tables. Fact tables contain numeric facts and foreign keys to dimension tables. Dimension tables provide context for the facts with attributes like date, customer, or product. Together, the fact and dimension tables form a star schema with the fact table at the center connected to the dimension tables. This structure allows for efficient analysis of business metrics across various dimensions like time periods, locations, or customer demographics.
Dimensional data modeling is a technique for database design intended to support analysis and reporting. It contains dimension tables that provide context about the business and fact tables that contain measures. Dimension tables describe attributes and may include hierarchies, while fact tables contain measurable events linked to dimensions. When designing a dimensional model, the business process, grain, dimensions, and facts are identified. Star and snowflake schemas are common types that differ in normalization of the dimensions. Slowly changing dimensions also must be accounted for.
The document discusses designing dimensional models for data warehouses and business intelligence systems. It provides an overview of key concepts in dimensional modeling including facts, dimensions, and the importance of conformed dimensions to enable analysis across multiple business processes. It also describes the process of designing dimensional models, including defining facts and dimensions, bringing them together into a star schema, and using a bus matrix to map business processes to dimensional models.
The document discusses dimensional modeling, which structures data from online transaction processing (OLTP) systems for online analytical processing (OLAP). It covers extracting and transforming OLTP data and loading it into a data warehouse with a star schema. Facts and dimensions are identified based on business requirements and grains of data. Tables are designed around the identified dimensions and facts. Data is then transformed from the OLTP to the OLAP schema for analysis and reporting.
The document discusses dimensional data modeling concepts. It provides examples of dimensions like date, store, and inventory. It explains that dimensions relate to facts in a fact table through surrogate keys. It also discusses slowly changing dimensions, conformed dimensions, and avoiding snowflakes which can hurt performance. The goal is to choose a business process, declare the grain, identify dimensions, and then identify facts to populate the fact table.
This document discusses dimensional data modeling for data warehouses. It begins by explaining star schemas, fact tables, and dimension tables. It then provides tips for combining data into a dimensional model and contrasts dimensional modeling with entity-relationship modeling. The document also covers topics like dimension table structures, updating dimensions, large dimensions, and snowflake schemas.
This document defines dimensional data modeling and describes its key concepts. Dimensional modeling uses facts and dimensions to structure data warehouses in star or snowflake schemas for understandability and query performance. Facts are numeric measures that can be aggregated, while dimensions provide context as descriptive attributes. The document outlines the modeling process and benefits of dimensional modeling for data querying, extensibility, and understandability.
Group 11 analyzed sales data from Dominick's Finer Foods stores to answer business questions. They extracted data from flat files into staging tables then transformed and loaded it into a data warehouse with dimensions for time, store, and product, and a fact table for store sales. Reports and OLAP cubes were created in SQL Server Analysis Services and SQL Server Reporting Services to analyze the impact of factors like season, holidays, and promotions on sales and profits by product and store. Significant effort was required to cleanse the large raw data, design the data marts, implement the ETL process, and generate reports and analyses.
The document provides an overview of dimensional data modeling. It defines key concepts such as facts, dimensions, and star schemas. It discusses the differences between relational and dimensional modeling and how dimensional modeling organizes data into facts and dimensions. The document also covers more complex dimensional modeling topics such as slowly changing dimensions, bridge tables, and hierarchies. It emphasizes the importance of understanding the data and iterating on the design. Finally, it provides 10 recommendations for dimensional modeling including using surrogate keys and type 2 slowly changing dimensions.
A data warehouse stores current and historical data for analysis and decision making. It uses a star schema with fact and dimension tables. The fact table contains measures that can be aggregated and connected to dimension tables through foreign keys. Dimensions describe the facts and contain descriptive attributes to analyze measures over time, products, locations etc. This allows analyzing large volumes of historical data for informed decisions.
Dimensional modeling is a technique used in data warehouse design that organizes data into facts and dimensions. Facts are typically numeric measures that can be aggregated, while dimensions provide context like timestamps, products, and stores. Dimensional models are built around specific business processes and efficiency is achieved through shared or conformed dimensions.
This document discusses logical design of data warehouse fact tables. It covers defining fact table column types including measures, foreign keys and surrogate keys. It describes additive, semi-additive and non-additive measures and how they impact aggregation. Finally, it discusses resolving many-to-many relationships in a star schema by creating an intermediate dimension table.
Difference between fact tables and dimension tablesKamran Haider
The document discusses the differences between fact tables and dimension tables in a data warehouse. It also discusses surrogate keys, natural keys, and why surrogate keys are used in data warehouses. Specifically:
- Fact tables contain measures/facts and foreign keys, while dimension tables contain descriptive attributes. Surrogate keys are integers assigned sequentially in dimension tables to join with fact tables.
- Surrogate keys are used instead of natural keys for faster joins, better performance, and to integrate heterogeneous data sources. They allow maintaining historical and current data when natural keys may change over time.
- The document outlines advantages like performance and disadvantages like unnecessary burden during ETL of using surrogate keys versus natural keys which have business meaning but can impact performance
A data warehouse integrates data from multiple source systems to create a consistent set of data over time to support decision making. It contains historical data derived from transaction systems. A data warehouse uses a star schema with one large fact table containing measures connected to multiple dimension tables. This allows for flexible analysis by slicing and dicing measures across dimensions.
A star schema is a data warehouse design that represents multidimensional data with one or more fact tables referencing any number of dimension tables. It consists of a central fact table surrounded by dimension tables that describe the facts. To design a star schema, business processes are identified, measures or facts are selected, dimensions for the facts are determined, dimension columns are listed, and the lowest level of summary in the fact table is defined. Star schemas have advantages like simpler queries, simplified business reporting, query performance gains, and fast aggregations. The ERDPlus tool can be used to implement star schemas.
The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.
The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
The document presents on multidimensional data models. It discusses the key components of multidimensional data models including dimensions and facts. It describes different types of multidimensional data models such as data cube model, star schema model, snowflake schema model, and fact constellations. The star schema model and snowflake schema model are explained in more detail through examples and their benefits are highlighted.
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.
Star ,Snow and Fact-Constullation Schemas??Abdul Aslam
This document compares and contrasts star schema, snowflake schema, and fact constellation schema. It defines each schema and discusses their key differences. Star schema has a single table for each dimension, while snowflake schema normalizes dimensions into multiple tables. Fact constellation allows dimension tables to be shared between multiple fact tables, modeling interrelated subjects. Performance is typically better with star schema, while snowflake schema reduces data redundancy at the cost of increased complexity.
A data warehouse is a consolidated view of enterprise data structured for dynamic queries and analytics. It has the following key characteristics: integrated, subject-oriented, time-variant, and non-volatile. A data warehouse uses a three-tier architecture including a database bottom tier, middle OLAP server tier, and top reporting tools tier. It enables improved decision making by storing large volumes of historical data separately from operational systems and facilitating analysis through dimensional modeling.
Case study: Implementation of dimension table and fact tablechirag patil
Dimensional modeling is a database structure used for data warehousing that organizes data into fact and dimension tables. Fact tables contain numeric facts and foreign keys to dimension tables. Dimension tables provide context for the facts with attributes like date, customer, or product. Together, the fact and dimension tables form a star schema with the fact table at the center connected to the dimension tables. This structure allows for efficient analysis of business metrics across various dimensions like time periods, locations, or customer demographics.
Dimensional data modeling is a technique for database design intended to support analysis and reporting. It contains dimension tables that provide context about the business and fact tables that contain measures. Dimension tables describe attributes and may include hierarchies, while fact tables contain measurable events linked to dimensions. When designing a dimensional model, the business process, grain, dimensions, and facts are identified. Star and snowflake schemas are common types that differ in normalization of the dimensions. Slowly changing dimensions also must be accounted for.
The document discusses designing dimensional models for data warehouses and business intelligence systems. It provides an overview of key concepts in dimensional modeling including facts, dimensions, and the importance of conformed dimensions to enable analysis across multiple business processes. It also describes the process of designing dimensional models, including defining facts and dimensions, bringing them together into a star schema, and using a bus matrix to map business processes to dimensional models.
The document discusses dimensional modeling, which structures data from online transaction processing (OLTP) systems for online analytical processing (OLAP). It covers extracting and transforming OLTP data and loading it into a data warehouse with a star schema. Facts and dimensions are identified based on business requirements and grains of data. Tables are designed around the identified dimensions and facts. Data is then transformed from the OLTP to the OLAP schema for analysis and reporting.
The document discusses dimensional data modeling concepts. It provides examples of dimensions like date, store, and inventory. It explains that dimensions relate to facts in a fact table through surrogate keys. It also discusses slowly changing dimensions, conformed dimensions, and avoiding snowflakes which can hurt performance. The goal is to choose a business process, declare the grain, identify dimensions, and then identify facts to populate the fact table.
This document discusses dimensional data modeling for data warehouses. It begins by explaining star schemas, fact tables, and dimension tables. It then provides tips for combining data into a dimensional model and contrasts dimensional modeling with entity-relationship modeling. The document also covers topics like dimension table structures, updating dimensions, large dimensions, and snowflake schemas.
This document defines dimensional data modeling and describes its key concepts. Dimensional modeling uses facts and dimensions to structure data warehouses in star or snowflake schemas for understandability and query performance. Facts are numeric measures that can be aggregated, while dimensions provide context as descriptive attributes. The document outlines the modeling process and benefits of dimensional modeling for data querying, extensibility, and understandability.
Group 11 analyzed sales data from Dominick's Finer Foods stores to answer business questions. They extracted data from flat files into staging tables then transformed and loaded it into a data warehouse with dimensions for time, store, and product, and a fact table for store sales. Reports and OLAP cubes were created in SQL Server Analysis Services and SQL Server Reporting Services to analyze the impact of factors like season, holidays, and promotions on sales and profits by product and store. Significant effort was required to cleanse the large raw data, design the data marts, implement the ETL process, and generate reports and analyses.
The document provides an overview of dimensional data modeling. It defines key concepts such as facts, dimensions, and star schemas. It discusses the differences between relational and dimensional modeling and how dimensional modeling organizes data into facts and dimensions. The document also covers more complex dimensional modeling topics such as slowly changing dimensions, bridge tables, and hierarchies. It emphasizes the importance of understanding the data and iterating on the design. Finally, it provides 10 recommendations for dimensional modeling including using surrogate keys and type 2 slowly changing dimensions.
A data warehouse stores current and historical data for analysis and decision making. It uses a star schema with fact and dimension tables. The fact table contains measures that can be aggregated and connected to dimension tables through foreign keys. Dimensions describe the facts and contain descriptive attributes to analyze measures over time, products, locations etc. This allows analyzing large volumes of historical data for informed decisions.
Dimensional modeling is a technique used in data warehouse design that organizes data into facts and dimensions. Facts are typically numeric measures that can be aggregated, while dimensions provide context like timestamps, products, and stores. Dimensional models are built around specific business processes and efficiency is achieved through shared or conformed dimensions.
This document discusses logical design of data warehouse fact tables. It covers defining fact table column types including measures, foreign keys and surrogate keys. It describes additive, semi-additive and non-additive measures and how they impact aggregation. Finally, it discusses resolving many-to-many relationships in a star schema by creating an intermediate dimension table.
Difference between fact tables and dimension tablesKamran Haider
The document discusses the differences between fact tables and dimension tables in a data warehouse. It also discusses surrogate keys, natural keys, and why surrogate keys are used in data warehouses. Specifically:
- Fact tables contain measures/facts and foreign keys, while dimension tables contain descriptive attributes. Surrogate keys are integers assigned sequentially in dimension tables to join with fact tables.
- Surrogate keys are used instead of natural keys for faster joins, better performance, and to integrate heterogeneous data sources. They allow maintaining historical and current data when natural keys may change over time.
- The document outlines advantages like performance and disadvantages like unnecessary burden during ETL of using surrogate keys versus natural keys which have business meaning but can impact performance
A data warehouse integrates data from multiple source systems to create a consistent set of data over time to support decision making. It contains historical data derived from transaction systems. A data warehouse uses a star schema with one large fact table containing measures connected to multiple dimension tables. This allows for flexible analysis by slicing and dicing measures across dimensions.
A star schema is a data warehouse design that represents multidimensional data with one or more fact tables referencing any number of dimension tables. It consists of a central fact table surrounded by dimension tables that describe the facts. To design a star schema, business processes are identified, measures or facts are selected, dimensions for the facts are determined, dimension columns are listed, and the lowest level of summary in the fact table is defined. Star schemas have advantages like simpler queries, simplified business reporting, query performance gains, and fast aggregations. The ERDPlus tool can be used to implement star schemas.
The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.
The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
The document presents on multidimensional data models. It discusses the key components of multidimensional data models including dimensions and facts. It describes different types of multidimensional data models such as data cube model, star schema model, snowflake schema model, and fact constellations. The star schema model and snowflake schema model are explained in more detail through examples and their benefits are highlighted.
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.
Star ,Snow and Fact-Constullation Schemas??Abdul Aslam
This document compares and contrasts star schema, snowflake schema, and fact constellation schema. It defines each schema and discusses their key differences. Star schema has a single table for each dimension, while snowflake schema normalizes dimensions into multiple tables. Fact constellation allows dimension tables to be shared between multiple fact tables, modeling interrelated subjects. Performance is typically better with star schema, while snowflake schema reduces data redundancy at the cost of increased complexity.
A data warehouse is a consolidated view of enterprise data structured for dynamic queries and analytics. It has the following key characteristics: integrated, subject-oriented, time-variant, and non-volatile. A data warehouse uses a three-tier architecture including a database bottom tier, middle OLAP server tier, and top reporting tools tier. It enables improved decision making by storing large volumes of historical data separately from operational systems and facilitating analysis through dimensional modeling.
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.
This document contains a question bank for the subject of data warehousing and mining. It provides definitions and characteristics of data warehouses, including that they are subject-oriented, integrated, time-variant, and non-volatile stores of data from multiple sources made available for analysis. It also defines multidimensional data models using fact and dimension tables, and classifies OLAP tools as relational, multidimensional, or hybrid. Key differences between star and snowflake schemas are that snowflake schemas further normalize dimension tables. Metadata is defined as data about data.
The document defines data warehousing and its key concepts according to Bill Inmon and Ralph Kimball's paradigms. It discusses the components of a dimensional data model including dimensions, attributes, hierarchies, and fact tables. It also covers ETL processes, schema types like star and snowflake, and OLAP tools.
The document provides an introduction to Ab Initio, an ETL tool. It discusses the history and meaning of Ab Initio, and describes the key components of Ab Initio including the Co>Operating System, Component Library, and Graphical Development Environment. It also covers data warehouse concepts such as the ETL process, star schemas, and data marts that are relevant for understanding how Ab Initio is used.
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 key concepts in data warehouse architecture including:
1) The functions of data warehouse tools which extract, clean, transform, load, and refresh data from source systems.
2) Key terminologies like metadata, which provides information about the data warehouse contents, and dimensional modeling using facts, dimensions, and data cubes.
3) Common multidimensional data models like star schemas with a central fact table linked to dimension tables and snowflake schemas which further normalize dimension tables.
This document provides an overview of data warehousing concepts including:
- Data warehouses store historical data from operational systems for analysis and reporting. The data passes through a staging area and operational data store for cleaning before loading into the data warehouse.
- Common data warehouse architectures include star schemas with fact and dimension tables and snowflake schemas with normalized dimensions. Data marts contain summarized data for specific business questions.
- ETL processes extract, transform, and load the data in three phases. Transformation cleans and prepares the data before loading into dimensional schemas.
- Data warehouses typically contain historical data, derived data generated from existing data, and metadata describing the data and schemas.
The document discusses dimensional modeling best practices for a retail sales case study. It outlines a four-step process: 1) select the business process, 2) declare the grain, 3) choose dimensions, 4) identify facts. For the retail case, the process modeled is point-of-sale sales, with a grain of individual transactions. Key dimensions are date, product, store, and promotion. Facts include sales quantity, price, amount, and costs. The document also discusses design considerations like degenerate dimensions, extensibility, and surrogate keys.
Data Warehousing for students educationpptxjainyshah20
This document discusses data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data used to support management decision making. Key aspects covered include the multi-dimensional data model using cubes and dimensions, various data warehouse architectures like star schemas and snowflake schemas, and OLAP operations for analysis like roll-up, drill-down, slice and dice. Building a data warehouse requires a range of business, technology, and program management skills.
IDW Lecture 21-Families of STAR schema.pptxIntisarAhmad5
There are several types of families of STAR schemas including transaction/snapshot tables, core/custom tables, and value chain/circle tables. Transaction tables store transactional data while snapshot tables store periodic summaries. Core tables contain metrics for all products/services while custom tables contain metrics for specific products/services. Value chain/circle tables measure metrics for each step in a sequential business process or related business processes. Dimensions should be conformed across fact tables.
The document provides explanations of various SQL concepts including cross join, order by, distinct, union and union all, truncate and delete, compute clause, data warehousing, data marts, fact and dimension tables, snowflake schema, ETL processing, BCP, DTS, multidimensional analysis, and bulk insert. It also discusses the three primary ways of storing information in OLAP: MOLAP, ROLAP, and HOLAP.
Data warehousing and online analytical processingVijayasankariS
The document discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used to support management decision making. It describes key concepts such as data warehouse modeling using data cubes and dimensions, extraction, transformation and loading of data, and common OLAP operations. The document also provides examples of star schemas and how they are used to model data warehouses.
Operational database systems are designed to support transaction processing while data warehouses are designed to support analytical processing and report generation. Operational systems focus on business processes, contain current data, and are optimized for fast updates. Data warehouses are subject-oriented, contain historical data that is rarely changed, and are optimized for fast data retrieval. The three main components of a data warehouse architecture are the database server, OLAP server, and client tools. Data is extracted from operational systems, transformed, cleansed, and loaded into fact and dimension tables in the data warehouse using the ETL process. Multidimensional schemas like star, snowflake, and constellation organize this data. Common OLAP operations performed on the data include roll-up,
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 Hyperion's product suite which includes tools for business intelligence, planning, performance management, and data management. It provides an overview of Essbase, a multidimensional database that allows users to analyze business data from multiple perspectives and levels. Key concepts covered include multidimensional data modeling, OLAP operations for analyzing data (e.g. drill-down, drill-up, slice and dice), and comparing multi-dimensional and relational database approaches.
This document provides a summary and conclusion to a slide deck presentation on commissioning a website. It outlines that the presentation is comprised of multiple slide decks covering topics like privacy legislation, policy and governance, connecting to the internet, website development, security, and selecting a developer. The author aims to provide guidance to small and medium businesses on issues to consider when commissioning a website. While the slides offer opinions based on the author's experience, the overarching goal is to serve as a guide rather than strict rules, with authoritative links included where possible. Feedback on the presentation is welcomed.
GDPR and EA - Commissioning a web site part 7 - Choosing a web site developerAllen Woods
Seventh of eight decks written to provide overview guidance of the way the web works for small to medium sized enterprises who are considering commissioning a web site for the first time. Arguably, the preceding six decks are primarily concerned with the subject matter of this deck.
GDPR and EA Commissioning a web site Part 6 of 8Allen Woods
Sixth of eight decks written to provide overview guidance of the way the web works for small to medium sized enterprises who are considering commissioning a web site for the first time. This deck introduces the idea that a web site is "not just for Christmas" and once set live, arguably, the work begins. Search engine optimisation (SEO) and cookie management and some of their associated legal issues are introduced
GDPR and EA Commissioning a web site part 5, writing a web pageAllen Woods
5th of eight slide decks targeted at small to medium sized enterprises considering commissioning a web site for the first time. It uses a simple "Hello World" exercise to illustrate some of the factors to consider when building a web page including routing through to support component and the concept of the "twin seductions of free and simple".
GDPR and EA Commissioning a web site. 1 of 8. IntroductionAllen Woods
Aimed at small to medium sized enterprises considering commissioning a web site for the first time. The deck introduces the concept of the organisation boundary as the architectural basis for such an exercise.
GDPR and EA - Commissioning a web site Part 4. The nature of the webAllen Woods
Fourth of eight decks written to provide overview guidance of the way the web works for small to medium sized enterprises. It takes a quasi OSI 7 layer analogy to explain, from chips to the net itself how connectivity is established
GDPR and EA Commissioning a web site part 2 - Legal EnvironmentAllen Woods
Second of 8 slide decks aimed at small to medium enterprises on factors to consider when commissioning a web site. This slide deck focusing on a changing legal environment brought about because of legislation like the EU GDPR
The document discusses using a multi-dimensional data model to organize both documents and databases for information management. It proposes using dimensions like ownership, operating concepts, lines of development, and perspectives to categorize data. By applying this framework to both document folder structures and database designs with foreign keys, a unique coordinate system can be created to locate any data element across both documents and databases. This allows complex search requests that span both document libraries and databases to be answered more efficiently.
Information management architecture concept modelAllen Woods
Part of an information managers course that sets out a simple IS architectural model that explains how the relationships between corporate strategy and process can be mapped from the perspective of the information manager
1. The Organisation As A System An information management framework The Performance Organiser Data Warehousing
2. Data Warehousing The Performance Organiser A data warehouse is a repository of an organization's electronically stored data, designed to facilitate reporting and analysis. A data warehouse is sometimes referred to as a “data mart”.
3. Data Warehousing The Performance Organiser Perhaps the two most well know forms of data stored in a data warehouse are: Datebases Data stored in rows and columns and related tables as a database Document Folders 01 -Design 02 -Accounts 03 - Production Or a series of files, in multiple formats stored in a directory structure
4. Data Warehousing The Performance Organiser While both can be analysed and analysis tools exist to search and collate each of them, the sheer volume of data contained in either or both, can turn any analysis effort into a complex and time consuming exercise.
5. Data Warehousing The Performance Organiser As a consequence, there is a need for a third type of data storage that provides the means to store the analysis results of the bulk of data but also gives the the means to “drill down” into the main data stores if required.
6. Data Warehousing The Performance Organiser Datebases Document Folders 01 -Design 02 -Accounts 03 - Production That third form is known as the “Fact Table” and enables the concept of “On Line Analytical Processing”
7. Data Warehousing The Performance Organiser A fact table consists of the measurements, metrics or facts of a business process. Fact tables have their own structure or schema. Often, when drawn, their schema takes the shape of a star, or snowflake, with the fact table surrounded by dimension tables, which as mathematically based summaries of main data tables.
8. Data Warehousing The Performance Organiser Fact tables provide the (usually) additive values that act as independent variables by which dimensional attributes are analyzed. Fact tables are often defined by their grain . The grain of a fact table represents the most atomic level by which the facts may be defined. The grain of a SALES fact table might be stated as "Sales volume by Day by Product by Store". Each record in this fact table is therefore uniquely defined by a day, product and store. Other dimensions might be members of this fact table (such as location/region) but these add nothing to the uniqueness of the fact records. These "affiliate dimensions" allow for additional slices of the independent facts but generally provide insights at a higher level of aggregation (a region contains many stores).
9. Data Warehousing The Performance Organiser Additive - Measures that can be added across all dimensions. Non Additive - Measures that cannot be added across all dimensions. Semi Additive - Measures that can be added across few dimensions and not with others.
10. Data Warehousing The Performance Organiser A fact table might contain either detail level facts or facts that have been aggregated (fact tables that contain aggregated facts are often instead called summary tables). Special care must be taken when handling ratios and percentage. One good design rule is to never store percentages or ratios in fact tables but only calculate these in the at the business of presentational level. Thus only store the numerator and denominator in the fact table, which then can be aggregated and the aggregated stored values can then be used for calculating the ratio or percentage at the business logic or presentational level.
11. Data Warehousing The Performance Organiser Fact table design approach: Identify a business process for analysis (like sales). Identify measures or facts (sales value), by asking questions like what ‘number of’ XX are relevant for the business process (Replace the XX, and test if the question makes sense business wise). Identify dimensions for facts (product dimension, location dimension, time dimension, organization dimension), by asking questions which makes sense business wise, like 'Analyse by' XX, where XX are replaced with the subject to test. List the columns that describe each dimension (region name, branch name, business unit name). Determine the lowest level (granularity) of summary in a fact table (e.g. sales).
12. Data Warehousing The Performance Organiser If the business process is SALES, then the corresponding fact table will typically contain columns representing both raw facts and aggregations in rows such as: £12,000 , being "sales for A store for 15-Jan-2005" £34,000 , being "sales for B store for 15-Jan-2005" £22,000 , being "sales for C store for 16-Jan-2005" £50,000 , being "sales for D store for 16-Jan-2005" £21,000 , being "average daily sales for A for Jan-2005" £65,000 , being "average daily sales for B Store for Feb-2005" £33,000 , being "average daily sales for C Store for year 2005" "average monthly sales" is a measurement which is stored in the fact table.
13. Data Warehousing The Performance Organiser The fact table also contains foreign keys from the dimension tables, where time series (e.g. dates) and other dimensions(e.g. store location, salesperson, product) are stored. All foreign keys between fact and dimension tables should be surrogate keys, not reused keys from operational data. The centralized table in a star schema is called a fact table. A fact table typically has two types of columns: those that contain facts and those that are foreign keys to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys. Fact tables contain the content of the data warehouse and store different types of measures like additive, non additive, and semi additive measures.
14. Data Warehousing The Performance Organiser Fact table data provides the primary data feed for kpi reporting and monitoring. From KPI’s come the status indicators for higher level monitoring mechanisms like scorecards and dashboards.
15. The Performance Organiser Data Warehousing Single KPI Dashboard Current achievable mean = 22 Possible Achievable mean = 28 Flag state = Green Qualitative Quantitative Achievable mean Achievable Best Worst Time Qualitative or Quantitative Scale J F M A M J J A S O N D 12 36 12 48 23 12 11 36 12 88 23 12 16 32 27 27 15 19 19 45 41 41 For each indicator provide additional documentary evidence
16. Data Warehousing The Performance Organiser No of widgets Produced No of widgets unfit for purpose
17. Data Warehousing The Performance Organiser The collation and summary of facts from main table data will mean running additional processes (typically out of normal working hours) which in turn will mean a time delay between the collation exercise and its readiness for delivery at the presentation or dashboard level. However, the speed of response for reporting purposes will be greatly enhanced
18. Data Warehousing The Performance Organiser A data warehouse typically consists of three data forms. Two, the databases and document libraries contain the bulk of an organisations data. The third form, Fact Tables, contain summary data, usually of the database content, the primary function of which is to provide accurate, timely analysis. Fact tables should provide the primary reporting source for kpis. Datebases Document Folders 01 -Design 02 -Accounts 03 - Production
19. Data Warehousing The Performance Organiser While fact tables present their own information management issues, they are one of the key tools in an information managers armoury that facilitates decision support. Fact tables can be further supported by techniques like pattern recognition, but for majority of circumstances, a mix of fact tables and bulk data stores, linked by a common referencing system will meet the most significant reporting requirements information managers will meet