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This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices.
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Cssu dw dm
The document discusses data warehousing, data mining, and business intelligence applications. It explains that data warehousing organizes and structures data for analysis, and that data mining involves preprocessing, characterization, comparison, classification, and forecasting of data to discover knowledge. The final stage is presenting discovered knowledge to end users through visualization and business intelligence applications.
Part1
The document provides an overview of data mining techniques and related concepts. It defines data mining and compares it to knowledge discovery in databases (KDD). It discusses the basic data mining tasks of classification, clustering, association rule mining, and summarization. It also covers related areas like databases, statistics, machine learning, and visualization techniques used in data mining. Finally, it provides an overview of common data mining techniques including decision trees, neural networks, genetic algorithms, and others.
Datawarehousing
The document discusses key concepts related to data warehousing including:
1) What data warehousing is, its main components, and differences from OLTP systems.
2) The typical architecture of a data warehouse including operational data sources, storage, and end-user access tools.
3) Important considerations like data flows, integration, management of metadata, and tools/technologies used.
4) Additional topics such as benefits, challenges, administration, and data marts.
Database
The document provides an overview of fundamentals of database design including definitions of key concepts like data, information, and databases. It discusses the purpose of databases and database management systems. It also covers topics like selecting a database system, database development best practices, and data entry considerations.
Chapter 13 data warehousing
Data warehousing involves extracting and consolidating data from multiple sources into a centralized database to support decision-making across an organization. It provides a comprehensive, integrated, and historical view of data organized by subject area rather than transactionally. Data warehouses use dimensional modeling and star schemas to store aggregated, multidimensional data in a way that facilitates analysis. Online analytical processing (OLAP) tools enable interactive exploration and analysis of multidimensional data through techniques like drill-down, roll-up, and slicing and dicing.
Datamining
This document discusses data mining and its applications. It notes that large amounts of data are being collected from various sources and stored. Data mining can help analyze this data by discovering patterns and relationships that would be difficult for humans to find manually. The document provides an overview of data mining techniques like classification and discusses software used for data mining like SAS Enterprise Miner, R, and Weka.
Lecture1
This document provides a summary of a course syllabus for a data warehousing and mining course. The key details include:
- The course meets on Tuesdays from 4:40-7:10pm and is taught by Professor Slobodan Vucetic.
- The objective is to discuss data management techniques like data warehouses, data marts, and online analytical processing (OLAP) for efficient data analysis.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- Grading will be based on homework, quizzes, a class presentation, individual project, and a final exam.
Classification and prediction in data mining
The document defines data mining as extracting useful information from large datasets. It discusses two main types of data mining tasks: descriptive tasks like frequent pattern mining and classification/prediction tasks like decision trees. Several data mining techniques are covered, including association, classification, clustering, prediction, sequential patterns, and decision trees. Real-world applications of data mining are also outlined, such as market basket analysis, fraud detection, healthcare, education, and CRM.
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Cssu dw dm
The document discusses data warehousing, data mining, and business intelligence applications. It explains that data warehousing organizes and structures data for analysis, and that data mining involves preprocessing, characterization, comparison, classification, and forecasting of data to discover knowledge. The final stage is presenting discovered knowledge to end users through visualization and business intelligence applications.
Part1
The document provides an overview of data mining techniques and related concepts. It defines data mining and compares it to knowledge discovery in databases (KDD). It discusses the basic data mining tasks of classification, clustering, association rule mining, and summarization. It also covers related areas like databases, statistics, machine learning, and visualization techniques used in data mining. Finally, it provides an overview of common data mining techniques including decision trees, neural networks, genetic algorithms, and others.
Datawarehousing
The document discusses key concepts related to data warehousing including:
1) What data warehousing is, its main components, and differences from OLTP systems.
2) The typical architecture of a data warehouse including operational data sources, storage, and end-user access tools.
3) Important considerations like data flows, integration, management of metadata, and tools/technologies used.
4) Additional topics such as benefits, challenges, administration, and data marts.
Database
The document provides an overview of fundamentals of database design including definitions of key concepts like data, information, and databases. It discusses the purpose of databases and database management systems. It also covers topics like selecting a database system, database development best practices, and data entry considerations.
Chapter 13 data warehousing
Data warehousing involves extracting and consolidating data from multiple sources into a centralized database to support decision-making across an organization. It provides a comprehensive, integrated, and historical view of data organized by subject area rather than transactionally. Data warehouses use dimensional modeling and star schemas to store aggregated, multidimensional data in a way that facilitates analysis. Online analytical processing (OLAP) tools enable interactive exploration and analysis of multidimensional data through techniques like drill-down, roll-up, and slicing and dicing.
Datamining
This document discusses data mining and its applications. It notes that large amounts of data are being collected from various sources and stored. Data mining can help analyze this data by discovering patterns and relationships that would be difficult for humans to find manually. The document provides an overview of data mining techniques like classification and discusses software used for data mining like SAS Enterprise Miner, R, and Weka.
Lecture1
This document provides a summary of a course syllabus for a data warehousing and mining course. The key details include:
- The course meets on Tuesdays from 4:40-7:10pm and is taught by Professor Slobodan Vucetic.
- The objective is to discuss data management techniques like data warehouses, data marts, and online analytical processing (OLAP) for efficient data analysis.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- Grading will be based on homework, quizzes, a class presentation, individual project, and a final exam.
Classification and prediction in data mining
The document defines data mining as extracting useful information from large datasets. It discusses two main types of data mining tasks: descriptive tasks like frequent pattern mining and classification/prediction tasks like decision trees. Several data mining techniques are covered, including association, classification, clustering, prediction, sequential patterns, and decision trees. Real-world applications of data mining are also outlined, such as market basket analysis, fraud detection, healthcare, education, and CRM.
Data warehouse logical design
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.
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The document provides an overview of the key components and considerations for building a data warehouse. It discusses 7 main components: 1) the data warehouse database, 2) sourcing, acquisition, cleanup and transformation tools, 3) metadata, 4) access (query) tools, 5) data marts, 6) data warehouse administration and management, and 7) information delivery systems. It also outlines important design considerations, technical considerations, and implementation considerations that must be addressed when building a data warehouse environment.
Project Presentation on Data WareHouse
Rohit Sharma presented a seminar on a project that discussed data warehousing, data mining, and how to apply data warehousing concepts to project data. The presentation covered terminology, pulling together and correctly using data from multiple sources, software requirements including PHP and MySQL, and screenshots of the admin panel and user interfaces.
DATA WAREHOUSING AND DATA MINING
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.
Data Mining & Data Warehousing Lecture Notes
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Difference between data warehouse and data mining
The document discusses data warehousing, online analytical processing (OLAP), and data mining. It describes a data warehouse as a subject-oriented collection of integrated data used to support management decision making. The typical architecture involves extracting, transforming, and loading data from operational systems into a data warehouse for analysis. Dimensional data modeling, including star schemas, is used to design data warehouses to enable efficient ad-hoc querying. OLAP and data mining tools are then used to analyze the data for patterns and insights.
Data Warehouse Architectures
There are three common data warehouse architectures: basic, with a staging area, and with a staging area and data marts. The basic architecture extracts data directly from source systems into the data warehouse for users. The staging area architecture uses a staging area to clean and process data before loading it into the warehouse. The third architecture adds data marts, which are subsets of the warehouse organized for specific business units like sales or purchasing.
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The document provides an overview of data warehousing, decision support, online analytical processing (OLAP), and data mining. It discusses what data warehousing is, how it can help organizations make better decisions by integrating data from various sources and making it available for analysis. It also describes OLAP as a way to transform warehouse data into meaningful information for interactive analysis, and lists some common OLAP operations like roll-up, drill-down, slice and dice, and pivot. Finally, it gives a brief introduction to data mining as the process of extracting patterns and relationships from data.
Data mining concepts and work
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
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The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
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The document discusses association rule mining and the Apriori algorithm. It defines key concepts in association rule mining such as frequent itemsets, support, confidence, and association rules. It also explains the steps in the Apriori algorithm to generate frequent itemsets and rules, including candidate generation, pruning infrequent subsets, and determining support. An example transaction database is used to demonstrate calculating support and confidence for rules and illustrate the Apriori algorithm.
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Data mining involves discovering patterns from large data sources and has evolved from database technology. It includes data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Mining can occur on different data sources and involves characterizing, associating, classifying, clustering, and identifying outliers and trends in data. Major issues include scalability, noise handling, pattern evaluation, and privacy concerns.
introduction to data warehousing and mining
1) The document discusses the evolution of database technology from the 1960s to present, including primitive file processing, DBMS, relational DBMS, advanced data models, and data warehousing and mining.
2) It explains why organizations mine data from a commercial viewpoint, to gain competitive advantages through better customized services, and from a scientific viewpoint to make use of large amounts of data being collected.
3) Data mining is defined as the process of analyzing data from different perspectives to extract useful patterns and knowledge, and examples are given of what is and is not considered data mining.
Data mining and data warehousing
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
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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.
Data Warehouse
this is the ppt this contains definition of data ware house , data , ware house, data modeling , data warehouse architecture and its type , data warehouse types, single tire, two tire, three tire .
An introduction to data warehousing
A data warehouse is a large collection of integrated data from multiple sources that is structured for analysis and reporting. It allows users to gain insights from historical data to support business decisions and identify trends. Data is extracted from operational systems, transformed for consistency and quality, and loaded into the data warehouse where it is stored in a multidimensional structure to enable analysis. This involves fact and dimension tables along with techniques like denormalization to optimize query performance.
Star schema
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.
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This document provides an overview of data warehousing and data mining. It begins by defining a data warehouse as a system that contains historical and cumulative data from single or multiple sources for simplifying reporting, analysis, and decision making. It describes three common data warehouse architectures and the key components of a data warehouse, including the database, ETL tools, metadata, query tools, and data marts. The document then defines data mining as extracting usable data from raw data using software to analyze patterns. It outlines descriptive and predictive data mining tasks and techniques like clustering, associations, summarization, prediction, and classification. Finally, it provides examples of data mining applications and discusses how AWS services like Amazon Redshift can provide scalable data warehousing
Data ware housing- Introduction to data ware housing
This ppt gives information about:
1. Introduction
2. Overview
3. Data Ware Housing
4. Terminologies
Data warehousing
The document discusses data warehousing, including its purpose of realizing value from data to support better business decisions. A data warehouse contains integrated data from multiple sources to support analysis. It discusses the components of a data warehouse like staging areas, data marts, and operational data stores. The document also covers topics like the evolution of data warehouse architectures, complexities in creating a data warehouse, potential pitfalls, and best practices.
Oracle: Fundamental Of Dw
This document provides an introduction to data warehousing fundamentals. It defines a data warehouse as an enterprise repository for subject-oriented, time-variant data used for decision support. It outlines the typical phases of a data warehousing project including strategy, definition, analysis, design, build, populate, test and evolution. It compares data warehouses to operational databases and data marts. Finally, it discusses extract, transform, load processes, possible ETL failures, and typical warehousing development tasks.
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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.
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The document provides an overview of the key components and considerations for building a data warehouse. It discusses 7 main components: 1) the data warehouse database, 2) sourcing, acquisition, cleanup and transformation tools, 3) metadata, 4) access (query) tools, 5) data marts, 6) data warehouse administration and management, and 7) information delivery systems. It also outlines important design considerations, technical considerations, and implementation considerations that must be addressed when building a data warehouse environment.
Project Presentation on Data WareHouse
Rohit Sharma presented a seminar on a project that discussed data warehousing, data mining, and how to apply data warehousing concepts to project data. The presentation covered terminology, pulling together and correctly using data from multiple sources, software requirements including PHP and MySQL, and screenshots of the admin panel and user interfaces.
DATA WAREHOUSING AND DATA MINING
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.
Data Mining & Data Warehousing Lecture Notes
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Difference between data warehouse and data mining
The document discusses data warehousing, online analytical processing (OLAP), and data mining. It describes a data warehouse as a subject-oriented collection of integrated data used to support management decision making. The typical architecture involves extracting, transforming, and loading data from operational systems into a data warehouse for analysis. Dimensional data modeling, including star schemas, is used to design data warehouses to enable efficient ad-hoc querying. OLAP and data mining tools are then used to analyze the data for patterns and insights.
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There are three common data warehouse architectures: basic, with a staging area, and with a staging area and data marts. The basic architecture extracts data directly from source systems into the data warehouse for users. The staging area architecture uses a staging area to clean and process data before loading it into the warehouse. The third architecture adds data marts, which are subsets of the warehouse organized for specific business units like sales or purchasing.
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The document provides an overview of data warehousing, decision support, online analytical processing (OLAP), and data mining. It discusses what data warehousing is, how it can help organizations make better decisions by integrating data from various sources and making it available for analysis. It also describes OLAP as a way to transform warehouse data into meaningful information for interactive analysis, and lists some common OLAP operations like roll-up, drill-down, slice and dice, and pivot. Finally, it gives a brief introduction to data mining as the process of extracting patterns and relationships from data.
Data mining concepts and work
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
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The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
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The document discusses association rule mining and the Apriori algorithm. It defines key concepts in association rule mining such as frequent itemsets, support, confidence, and association rules. It also explains the steps in the Apriori algorithm to generate frequent itemsets and rules, including candidate generation, pruning infrequent subsets, and determining support. An example transaction database is used to demonstrate calculating support and confidence for rules and illustrate the Apriori algorithm.
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Data mining involves discovering patterns from large data sources and has evolved from database technology. It includes data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Mining can occur on different data sources and involves characterizing, associating, classifying, clustering, and identifying outliers and trends in data. Major issues include scalability, noise handling, pattern evaluation, and privacy concerns.
introduction to data warehousing and mining
1) The document discusses the evolution of database technology from the 1960s to present, including primitive file processing, DBMS, relational DBMS, advanced data models, and data warehousing and mining.
2) It explains why organizations mine data from a commercial viewpoint, to gain competitive advantages through better customized services, and from a scientific viewpoint to make use of large amounts of data being collected.
3) Data mining is defined as the process of analyzing data from different perspectives to extract useful patterns and knowledge, and examples are given of what is and is not considered data mining.
Data mining and data warehousing
Data mining and data warehousing, database management system, Data mining and data warehousing, complete presentation of Data mining and data warehousing,
Data mining and data warehousing
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.
Data Warehouse
this is the ppt this contains definition of data ware house , data , ware house, data modeling , data warehouse architecture and its type , data warehouse types, single tire, two tire, three tire .
An introduction to data warehousing
A data warehouse is a large collection of integrated data from multiple sources that is structured for analysis and reporting. It allows users to gain insights from historical data to support business decisions and identify trends. Data is extracted from operational systems, transformed for consistency and quality, and loaded into the data warehouse where it is stored in a multidimensional structure to enable analysis. This involves fact and dimension tables along with techniques like denormalization to optimize query performance.
Star schema
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.
Data Warehousing AWS 12345
This document provides an overview of data warehousing and data mining. It begins by defining a data warehouse as a system that contains historical and cumulative data from single or multiple sources for simplifying reporting, analysis, and decision making. It describes three common data warehouse architectures and the key components of a data warehouse, including the database, ETL tools, metadata, query tools, and data marts. The document then defines data mining as extracting usable data from raw data using software to analyze patterns. It outlines descriptive and predictive data mining tasks and techniques like clustering, associations, summarization, prediction, and classification. Finally, it provides examples of data mining applications and discusses how AWS services like Amazon Redshift can provide scalable data warehousing
Data ware housing- Introduction to data ware housing
This ppt gives information about:
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The BI Sandbox
A description of the functions and purpose of a business intelligence self-service sandbox as part of an overall data warehousing architecture.
Innovation Track AWS Cloud Experience Argentina - Data Lakes & Analytics en AWS
Innovation Track AWS Cloud Experience Argentina - Data Lakes & Analytics en AWS Amazon Web Services LATAM
Data lakes allow organizations to store all types of data in a centralized repository at scale. AWS Lake Formation makes it easy to build secure data lakes by automatically registering and cleaning data, enforcing access permissions, and enabling analytics. Data stored in data lakes can be analyzed using services like Amazon Athena, Redshift, and EMR depending on the type of analysis and latency required.Similar to 142230 633685297550892500 (20)
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
Innovation Track AWS Cloud Experience Argentina - Data Lakes & Analytics en AWS
Innovation Track AWS Cloud Experience Argentina - Data Lakes & Analytics en AWS
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The document provides an overview of data warehousing and discusses its history, purpose, and key definitions. A data warehouse stores diverse data from multiple sources in a single repository for analysis and decision making. It differs from operational databases by being optimized for queries rather than transactions, containing historical rather than current data, and supporting strategic rather than operational needs. The need for data warehousing arose from an "information crisis" as organizations struggled to convert growing volumes of heterogeneous data into useful information to guide strategic decisions.
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
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Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
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Introduction of Cybersecurity with OSS at Code Europe 2024
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142230 633685297550892500
- 1. By: RAVI RANJAN DATA WAREHOUSE By: Ravi Ranjan
- 2. DEFINITION Data Warehouse A collection of corporate information, derived directly from operational systems and some external data sources. Its specific purpose is to support business decisions, not business operations.
- 3. THE PURPOSE OF DATA WAREHOUSING Realize the value of data Data / information is an asset Methods to realize the value, (Reporting, Analysis, etc.) Make better decisions Turn data into information Create competitive advantage Methods to support the decision making process, (EIS, DSS, etc.)
- 4. Data Warehouse Components • Staging Area • A preparatory repository where transaction data can be transformed for use in the data warehouse • Data Mart • Traditional dimensionally modeled set of dimension and fact tables • Per Kimball, a data warehouse is the union of a set of data marts • Operational Data Store (ODS) • Modeled to support near real-time reporting needs.
- 5. DATA WAREHOUSE FUNCTIONALITY Relational Databases Optimized Loader Extraction ERP Systems Cleansing Data Warehouse Engine Analyze Purchased Query Data Legacy Data Metadata Repository
- 6. EVOLUTION ARCHITECTURE OF DATA WAREHOUSE GO TO Top-Down Architecture DIAGRAM GO TO Bottom-Up Architecture DIAGRAM GO TO Enterprise Data Mart Architecture DIAGRAM GO TO Data Stage/Data Mart Architecture DIAGRAM
- 7. VERY LARGE DATA BASES WAREHOUSES ARE VERY LARGE DATABASES Terabytes -- 10^12 bytes: Wal-Mart -- 24 Terabytes Petabytes -- 10^15 bytes: Geographic Information Systems Exabytes -- 10^18 bytes: National Medical Records Zettabytes -- 10^21 bytes: Weather images Zottabytes -- 10^24 bytes: Intelligence Agency Videos
- 8. COMPLEXITIES OF CREATING A DATA WAREHOUSE Incomplete errors Missing Fields Records or Fields That, by Design, are not Being Recorded Incorrecterrors Wrong Calculations, Aggregations Duplicate Records Wrong Information Entered into Source System
- 9. SUCCESS & FUTURE OF DATA WAREHOUSE The Data Warehouse has successfully supported the increased needs of the State over the past eight years. The need for growth continues however, as the desire for more integrated data increases. The Data Warehouse has software and tools in place to provide the functionality needed to support new enterprise Data Warehouse projects. The future capabilities of the Data Warehouse can be expanded to include other programs and agencies.
- 10. DATA WAREHOUSE PITFALLS Youare going to spend much time extracting, cleaning, and loading data Youare going to find problems with systems feeding the data warehouse Youwill find the need to store/validate data not being captured/validated by any existing system Large scale data warehousing can become an exercise in data homogenizing
- 11. DATA WAREHOUSE PITFALLS… The time it takes to load the warehouse will expand to the amount of the time in the available window... and then some You are building a HIGH maintenance system You will fail if you concentrate on resource optimization to the neglect of project, data, and customer management issues and an understanding of what adds value to the customer
- 12. BEST PRACTICES Complete requirements and design Prototyping is key to business understanding Utilizing proper aggregations and detailed data Training is an on-going process Build data integrity checks into your system.
- 13. Top-Down Architecture BACK TO ARCHITECTURE
- 14. Bottom-Up Architecture BACK TO ARCHITECTURE
- 15. Enterprise Data Mart Architecture BACK TO ARCHITECTURE
- 16. Data Stage/Data Mart Architecture BACK TO ARCHITECTURE
Editor's Notes
- Legacy data is historical dataThe working information of a staff member Working hours or time-off hours within the fiscal period, up to the current dateWorking Hours = Overtime, etc.Time-Off Hours = Vacation, Sick Leave, etc.
- DataStage database, toolA tool set for designing, developing, and runnin.gapplications that populate one or more tables in a data warehouse