The document discusses data warehousing, including what a data warehouse is, why organizations use them, and key concepts like ETL processes, dimensional modeling using star schemas, and OLAP for data analysis. A data warehouse consolidates data from multiple sources, structures it by subject areas like customers or products, and loads it in a time-variant, nonvolatile structure to support management decision making. It also describes common data warehouse architectures and compares OLTP and OLAP systems.
A data warehouse is a subject-oriented, consolidated collection of integrated data from multiple sources used to support management decision making. It is separate from operational databases and contains historical data for analysis. Data warehouses use a star schema with fact and dimension tables and support online analytical processing (OLAP) for complex analysis and reporting.
A data warehouse is a collection of integrated data from multiple sources organized to support management decision making. It contains subject-oriented, integrated, time-variant and non-volatile data stored in a way that is optimized for query and analysis. There are different types of data warehouses including data marts, operational data stores and enterprise data warehouses. Key components of a data warehouse include data sources, extraction, loading, a comprehensive database, metadata and middleware tools.
A data warehouse is a collection of data integrated from multiple sources to support decision making. It contains subject-oriented, integrated, time-variant, and non-volatile data stored in a way that makes it readily available for analysis. Data marts can be dependent on the warehouse or independent subsets designed for specific departments. Successful implementation requires identifying data sources and governance, planning data quality and modeling, selecting ETL and database tools, and supporting end users. Key challenges include unrealistic expectations, technical issues, and ensuring ongoing value.
Data warehousing provides consolidated historical data from multiple sources to support analysis and strategic decision-making. A data warehouse is subject-oriented, integrated, stores time-variant data nonvolatile, and is maintained separately from operational databases. It differs from operational databases which focus on current data and transactions, while data warehouses integrate historical data from different sources and organizations to support analysis and informed decisions. Data warehouses are constructed separately to promote high performance of both operational and analytical systems.
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
This document provides an overview of data warehousing. It defines data warehousing as collecting data from multiple sources into a central repository for analysis and decision making. The document outlines the history of data warehousing and describes its key characteristics like being subject-oriented, integrated, and time-variant. It also discusses the architecture of a data warehouse including sources, transformation, storage, and reporting layers. The document compares data warehousing to traditional DBMS and explains how data warehouses are better suited for analysis versus transaction processing.
The document provides information about data warehousing including definitions, how it works, types of data warehouses, components, architecture, and the ETL process. Some key points:
- A data warehouse is a system for collecting and managing data from multiple sources to support analysis and decision-making. It contains historical, integrated data organized around important subjects.
- Data flows into a data warehouse from transaction systems and databases. It is processed, transformed, and loaded so users can access it through BI tools. This allows organizations to analyze customers and data more holistically.
- The main components of a data warehouse are the load manager, warehouse manager, query manager, and end-user access tools. The ETL process
A data warehouse is a subject-oriented, consolidated collection of integrated data from multiple sources used to support management decision making. It is separate from operational databases and contains historical data for analysis. Data warehouses use a star schema with fact and dimension tables and support online analytical processing (OLAP) for complex analysis and reporting.
A data warehouse is a collection of integrated data from multiple sources organized to support management decision making. It contains subject-oriented, integrated, time-variant and non-volatile data stored in a way that is optimized for query and analysis. There are different types of data warehouses including data marts, operational data stores and enterprise data warehouses. Key components of a data warehouse include data sources, extraction, loading, a comprehensive database, metadata and middleware tools.
A data warehouse is a collection of data integrated from multiple sources to support decision making. It contains subject-oriented, integrated, time-variant, and non-volatile data stored in a way that makes it readily available for analysis. Data marts can be dependent on the warehouse or independent subsets designed for specific departments. Successful implementation requires identifying data sources and governance, planning data quality and modeling, selecting ETL and database tools, and supporting end users. Key challenges include unrealistic expectations, technical issues, and ensuring ongoing value.
Data warehousing provides consolidated historical data from multiple sources to support analysis and strategic decision-making. A data warehouse is subject-oriented, integrated, stores time-variant data nonvolatile, and is maintained separately from operational databases. It differs from operational databases which focus on current data and transactions, while data warehouses integrate historical data from different sources and organizations to support analysis and informed decisions. Data warehouses are constructed separately to promote high performance of both operational and analytical systems.
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
This document provides an overview of data warehousing. It defines data warehousing as collecting data from multiple sources into a central repository for analysis and decision making. The document outlines the history of data warehousing and describes its key characteristics like being subject-oriented, integrated, and time-variant. It also discusses the architecture of a data warehouse including sources, transformation, storage, and reporting layers. The document compares data warehousing to traditional DBMS and explains how data warehouses are better suited for analysis versus transaction processing.
The document provides information about data warehousing including definitions, how it works, types of data warehouses, components, architecture, and the ETL process. Some key points:
- A data warehouse is a system for collecting and managing data from multiple sources to support analysis and decision-making. It contains historical, integrated data organized around important subjects.
- Data flows into a data warehouse from transaction systems and databases. It is processed, transformed, and loaded so users can access it through BI tools. This allows organizations to analyze customers and data more holistically.
- The main components of a data warehouse are the load manager, warehouse manager, query manager, and end-user access tools. The ETL process
The document discusses various concepts related to data warehousing including:
1. The key characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-updatable.
2. Common data warehouse architectures including two-level, independent data marts, dependent data marts with an operational data store, logical data marts with an active warehouse, and a three-layer architecture.
3. The Extract, Transform, Load (ETL) process and data reconciliation to integrate and transform data from source systems into the data warehouse.
The document discusses data warehousing, including its history, types, security, applications, components, architecture, benefits and problems. A data warehouse is defined as a subject-oriented, integrated, time-variant collection of data to support management decision making. In the 1990s, organizations needed timely data but traditional systems were too slow. Data warehouses now provide competitive advantages through improved decision making and productivity. They integrate data from multiple sources to support applications like customer analysis, stock control and fraud detection.
This document provides an overview of key concepts in data warehousing including:
1. The need for data warehousing to consolidate data from multiple sources and support decision making.
2. Common data warehouse architectures like the two-tier architecture and data marts.
3. The extract, transform, load (ETL) process used to reconcile data and populate the data warehouse.
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.
The document discusses data warehousing and data marts. It defines a data warehouse as a database designed for business intelligence and analysis rather than transactions, containing historical data from multiple sources. A data mart has a narrower scope, serving a department. The key characteristics of a data warehouse are that data is structured for simplicity and speed, contains large amounts of historical data, and involves data from multiple sources undergoing extraction, transformation and loading.
The document discusses different data warehouse architectures that can vary based on the layers included. A basic data warehouse architecture contains five main layers: the system operation layer, metadata layer, data source layer, ETL layer, and data warehouse/storage layer. Other common layers seen in more complex architectures include the staging layer, extraction layer, data mart layer, data logic layer, and data presentation layer.
This document provides an introduction to data warehousing. It defines key concepts like data, databases, information and metadata. It describes problems with heterogeneous data sources and fragmented data management in large enterprises. The solution is a data warehouse, which provides a unified view of data from various sources. A data warehouse is defined as a subject-oriented, integrated collection of historical data used for analysis and decision making. It differs from operational databases in aspects like data volume, volatility, and usage. The document outlines the extract-transform-load process and common architecture of data warehousing.
Data Warehouses & Deployment By Ankita dubeyAnkita Dubey
This document contains the notes about data warehouses and life cycle for data warehouse deployment project. This can be useful for students or working professionals to gain the basic knowledge about Data warehouses.
A data warehouse is a pool of data structured to support decision making. It integrates data from multiple sources and is time-variant and nonvolatile. Data warehouses can take the form of enterprise data warehouses, used across an organization for decision support, or data marts designed for a specific department. The data warehousing process involves extracting data from sources, transforming and loading it into a comprehensive database, and using middleware tools and metadata. Real-time data warehousing allows for information-based decision making using up-to-date data.
Business intelligence and data warehousesDhani Ahmad
This chapter discusses business intelligence and data warehouses. It covers how operational data differs from decision support data, the components of a data warehouse including facts, dimensions and star schemas, and how online analytical processing (OLAP) and SQL extensions support analysis of multidimensional decision support data. The chapter also discusses data mining, requirements for decision support databases, and considerations for implementing a successful data warehouse project.
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
This document defines key concepts in data warehousing including data warehouses, data marts, and ETL (extract, transform, load). It states that a data warehouse is a non-volatile collection of integrated data from multiple sources used to support management decision making. A data mart contains a single subject area of data. ETL is the process of extracting data from source systems, transforming it, and loading it into a data warehouse or data mart.
The document discusses different types of data marts:
- Dependent data marts draw data directly from a centralized data warehouse, allowing for unified data access but with a focus on a specific group's needs.
- Independent data marts are standalone systems built from direct access to operational or external data sources without using a centralized warehouse. They are suitable for smaller groups.
- Hybrid data marts can integrate data from both a centralized warehouse and other sources, providing flexibility for ad hoc integration needs.
This document discusses data warehousing and online analytical processing (OLAP) technology. It defines a data warehouse, compares it to operational databases, and explains how OLAP systems organize and present data for analysis. The document also describes multidimensional data models, common OLAP operations, and the steps to design and construct a data warehouse. Finally, it discusses applications of data warehouses and efficient processing of OLAP queries.
Introduction to Data Warehousing: Introduction, Necessity, Framework
of the datawarehouse, options, developing datawarehouses, end points.
Data Warehousing Design Consideration and Dimensional Modeling:
Defining Dimensional Model, Granularity of Facts, Additivity of Facts,
Functional dependency of the Data, Helper Tables, Implementation manyto-
many relationships between fact and dimensional modelling.
The document discusses the need for data warehousing and provides examples of how data warehousing can help companies analyze data from multiple sources to help with decision making. It describes common data warehouse architectures like star schemas and snowflake schemas. It also outlines the process of building a data warehouse, including data selection, preprocessing, transformation, integration and loading. Finally, it discusses some advantages and disadvantages of data warehousing.
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALASaikiran Panjala
This document discusses data warehouses, including what they are, how they are implemented, and how they can be further developed. It provides definitions of key concepts like data warehouses, data cubes, and OLAP. It also describes techniques for efficient data cube computation, indexing of OLAP data, and processing of OLAP queries. Finally, it discusses different approaches to data warehouse implementation and development of data cube technology.
This document discusses data warehousing concepts and technologies. It defines a data warehouse as a subject-oriented, integrated, non-volatile, and time-variant collection of data used to support management decision making. It describes the data warehouse architecture including extract-transform-load processes, OLAP servers, and metadata repositories. Finally, it outlines common data warehouse applications like reporting, querying, and data mining.
- Data warehousing aims to help knowledge workers make better decisions by integrating data from multiple sources and providing historical and aggregated data views. It separates analytical processing from operational processing for improved performance.
- A data warehouse contains subject-oriented, integrated, time-variant, and non-volatile data to support analysis. It is maintained separately from operational databases. Common schemas include star schemas and snowflake schemas.
- Online analytical processing (OLAP) supports ad-hoc querying of data warehouses for analysis. It uses multidimensional views of aggregated measures and dimensions. Relational and multidimensional OLAP are common architectures. Measures are metrics like sales, and dimensions provide context like products and time periods.
The document discusses various concepts related to data warehousing including:
1. The key characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-updatable.
2. Common data warehouse architectures including two-level, independent data marts, dependent data marts with an operational data store, logical data marts with an active warehouse, and a three-layer architecture.
3. The Extract, Transform, Load (ETL) process and data reconciliation to integrate and transform data from source systems into the data warehouse.
The document discusses data warehousing, including its history, types, security, applications, components, architecture, benefits and problems. A data warehouse is defined as a subject-oriented, integrated, time-variant collection of data to support management decision making. In the 1990s, organizations needed timely data but traditional systems were too slow. Data warehouses now provide competitive advantages through improved decision making and productivity. They integrate data from multiple sources to support applications like customer analysis, stock control and fraud detection.
This document provides an overview of key concepts in data warehousing including:
1. The need for data warehousing to consolidate data from multiple sources and support decision making.
2. Common data warehouse architectures like the two-tier architecture and data marts.
3. The extract, transform, load (ETL) process used to reconcile data and populate the data warehouse.
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.
The document discusses data warehousing and data marts. It defines a data warehouse as a database designed for business intelligence and analysis rather than transactions, containing historical data from multiple sources. A data mart has a narrower scope, serving a department. The key characteristics of a data warehouse are that data is structured for simplicity and speed, contains large amounts of historical data, and involves data from multiple sources undergoing extraction, transformation and loading.
The document discusses different data warehouse architectures that can vary based on the layers included. A basic data warehouse architecture contains five main layers: the system operation layer, metadata layer, data source layer, ETL layer, and data warehouse/storage layer. Other common layers seen in more complex architectures include the staging layer, extraction layer, data mart layer, data logic layer, and data presentation layer.
This document provides an introduction to data warehousing. It defines key concepts like data, databases, information and metadata. It describes problems with heterogeneous data sources and fragmented data management in large enterprises. The solution is a data warehouse, which provides a unified view of data from various sources. A data warehouse is defined as a subject-oriented, integrated collection of historical data used for analysis and decision making. It differs from operational databases in aspects like data volume, volatility, and usage. The document outlines the extract-transform-load process and common architecture of data warehousing.
Data Warehouses & Deployment By Ankita dubeyAnkita Dubey
This document contains the notes about data warehouses and life cycle for data warehouse deployment project. This can be useful for students or working professionals to gain the basic knowledge about Data warehouses.
A data warehouse is a pool of data structured to support decision making. It integrates data from multiple sources and is time-variant and nonvolatile. Data warehouses can take the form of enterprise data warehouses, used across an organization for decision support, or data marts designed for a specific department. The data warehousing process involves extracting data from sources, transforming and loading it into a comprehensive database, and using middleware tools and metadata. Real-time data warehousing allows for information-based decision making using up-to-date data.
Business intelligence and data warehousesDhani Ahmad
This chapter discusses business intelligence and data warehouses. It covers how operational data differs from decision support data, the components of a data warehouse including facts, dimensions and star schemas, and how online analytical processing (OLAP) and SQL extensions support analysis of multidimensional decision support data. The chapter also discusses data mining, requirements for decision support databases, and considerations for implementing a successful data warehouse project.
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
This document defines key concepts in data warehousing including data warehouses, data marts, and ETL (extract, transform, load). It states that a data warehouse is a non-volatile collection of integrated data from multiple sources used to support management decision making. A data mart contains a single subject area of data. ETL is the process of extracting data from source systems, transforming it, and loading it into a data warehouse or data mart.
The document discusses different types of data marts:
- Dependent data marts draw data directly from a centralized data warehouse, allowing for unified data access but with a focus on a specific group's needs.
- Independent data marts are standalone systems built from direct access to operational or external data sources without using a centralized warehouse. They are suitable for smaller groups.
- Hybrid data marts can integrate data from both a centralized warehouse and other sources, providing flexibility for ad hoc integration needs.
This document discusses data warehousing and online analytical processing (OLAP) technology. It defines a data warehouse, compares it to operational databases, and explains how OLAP systems organize and present data for analysis. The document also describes multidimensional data models, common OLAP operations, and the steps to design and construct a data warehouse. Finally, it discusses applications of data warehouses and efficient processing of OLAP queries.
Introduction to Data Warehousing: Introduction, Necessity, Framework
of the datawarehouse, options, developing datawarehouses, end points.
Data Warehousing Design Consideration and Dimensional Modeling:
Defining Dimensional Model, Granularity of Facts, Additivity of Facts,
Functional dependency of the Data, Helper Tables, Implementation manyto-
many relationships between fact and dimensional modelling.
The document discusses the need for data warehousing and provides examples of how data warehousing can help companies analyze data from multiple sources to help with decision making. It describes common data warehouse architectures like star schemas and snowflake schemas. It also outlines the process of building a data warehouse, including data selection, preprocessing, transformation, integration and loading. Finally, it discusses some advantages and disadvantages of data warehousing.
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALASaikiran Panjala
This document discusses data warehouses, including what they are, how they are implemented, and how they can be further developed. It provides definitions of key concepts like data warehouses, data cubes, and OLAP. It also describes techniques for efficient data cube computation, indexing of OLAP data, and processing of OLAP queries. Finally, it discusses different approaches to data warehouse implementation and development of data cube technology.
This document discusses data warehousing concepts and technologies. It defines a data warehouse as a subject-oriented, integrated, non-volatile, and time-variant collection of data used to support management decision making. It describes the data warehouse architecture including extract-transform-load processes, OLAP servers, and metadata repositories. Finally, it outlines common data warehouse applications like reporting, querying, and data mining.
- Data warehousing aims to help knowledge workers make better decisions by integrating data from multiple sources and providing historical and aggregated data views. It separates analytical processing from operational processing for improved performance.
- A data warehouse contains subject-oriented, integrated, time-variant, and non-volatile data to support analysis. It is maintained separately from operational databases. Common schemas include star schemas and snowflake schemas.
- Online analytical processing (OLAP) supports ad-hoc querying of data warehouses for analysis. It uses multidimensional views of aggregated measures and dimensions. Relational and multidimensional OLAP are common architectures. Measures are metrics like sales, and dimensions provide context like products and time periods.
This document provides an overview of data warehousing. It defines a data warehouse as a subject-oriented, integrated collection of data used to support management decision making. The benefits of data warehousing include high returns on investment and increased productivity. A data warehouse differs from an OLTP system in its design for analytics rather than transactions. The typical architecture includes data sources, an operational data store, warehouse manager, query manager and end user tools. Key components are extracting, cleaning, transforming and loading data, and managing metadata. Data flows include inflows from sources and upflows of summarized data to users.
The document discusses data warehousing and OLAP technology. 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 aspects of a data warehouse including its multi-dimensional schema with fact and dimension tables and use of data cubes to enable OLAP. It also contrasts data warehouses with operational databases and discusses ETL, architecture, and design approaches.
This document provides an introduction to data warehousing. It defines a data warehouse as a single, consistent store of data from various sources made available to end users in a way they can understand and use in a business context. Data warehouses consolidate information, improve query performance, and separate decision support functions from operational systems. They support knowledge discovery, reporting, data mining, and analysis to help answer business questions and make better decisions.
Data Warehousing is a topic on Management of Information Technology that would help students on their subject matter and as reference for their assigned report.
This document provides an overview of data warehousing and related concepts. It defines a data warehouse as a centralized database for analysis and reporting that stores current and historical data from multiple sources. The document describes key elements of data warehousing including Extract-Transform-Load (ETL) processes, multidimensional data models, online analytical processing (OLAP), and data marts. It also outlines advantages such as enhanced access and consistency, and disadvantages like time required for data extraction and loading.
This document provides an overview of business intelligence, data warehousing, data marts, and data mining presented by Mr. Manish Tripathi. It defines business intelligence as a process for analyzing data to help business decisions. Data warehousing is described as a centralized repository for storing historical data from various sources to support analysis and reporting. Data marts are subsets of data warehouses focused on specific business units or teams. Common business intelligence tools and the benefits of these systems are also summarized.
A data warehouse uses a multi-dimensional data model to consolidate data from multiple sources and support analysis. It uses a star schema with fact and dimension tables or a snowflake schema that normalizes dimensions. This allows for interactive exploration of data through OLAP operations like roll-up, drill-down, slice and dice to gain business insights. The document provides an overview of data warehousing concepts like schemas, cubes, measures and hierarchies to model and analyze historical data for decision making.
Various Applications of Data Warehouse.pptRafiulHasan19
The document discusses various applications of data warehousing. It begins by describing problems with traditional transactional systems and how data warehouses address these issues. It then defines key components of a data warehouse including the extraction, transformation, and loading of data from various sources. The document outlines how online analytical processing (OLAP) tools, metadata repositories, and data mining techniques analyze and explore the collected data. Finally, it weighs the benefits of a data warehouse against the costs of implementation and maintenance.
Data warehousing is an architectural model that gathers data from various sources into a single unified data model for analysis purposes. It consists of extracting data from operational systems, transforming it, and loading it into a database optimized for querying and analysis. This allows organizations to integrate data from different sources, provide historical views of data, and perform flexible analysis without impacting transaction systems. While implementation and maintenance of a data warehouse requires significant costs, the benefits include a single access point for all organizational data and optimized systems for analysis and decision making.
Data Mining Concept & Technique-ch04.pptMutiaSari53
This chapter discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented collection of integrated and nonvolatile data used for analysis. Key concepts covered include the multidimensional data cube model used to organize warehouse data, ETL processes for loading data into the warehouse, and star and snowflake schemas for conceptual modeling. The chapter also distinguishes between OLTP and OLAP systems and operations.
SAP HANA Architecture Overview | SAP HANA TutorialZaranTech LLC
We are a team of Senior IT consultants with a wide array of knowledge in different domains, methodologies, Tools and platforms.We strive to develop and deliver highly qualified IT consultants to the market.
We differentiate our training and development program by delivering Role-specific traininginstead of Product-based training. Ultimately, our goal is to deliver the best IT consultants to our clients. - http://www.zarantech.com/
A data warehouse is a central repository for storing historical and integrated data from multiple sources to be used for analysis and reporting. It contains a single version of the truth and is optimized for read access. In contrast, operational databases are optimized for transaction processing and contain current detailed data. A key aspect of data warehousing is using a dimensional model with fact and dimension tables. This allows for analyzing relationships between measures and dimensions in a multi-dimensional structure known as a data cube.
This document provides an overview of data warehousing. It defines a data warehouse as a central database that includes information from several different sources and keeps both current and historical data to support management decision making. The document describes key characteristics of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. It also discusses common data warehouse architectures and applications.
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
Master data management and data warehousingZahra Mansoori
This document discusses master data management (MDM) and its role in data warehousing. It describes how MDM can consolidate and cleanse master data from various transactional systems to create a single version of truth. This unified master data is then used to support both operational and analytical initiatives. The document also provides an overview of key components of a data warehouse, including the extraction, transformation, and loading of data from operational systems. It notes that the ideal information architecture places an MDM component between operational and analytical systems to ensure consistent, high-quality master data is available throughout the organization.
This document provides an introduction to data warehouses. It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data to support management decision making. The key features of a data warehouse are that it is subject-oriented, integrated, time variant and non-volatile. It also distinguishes between operational databases used for transaction processing and data warehouses used for analytical processing and decision making.
This document provides an overview of data mining and data warehousing concepts. It defines data mining as the process of identifying patterns in data. The data mining process involves tasks like classification, clustering, and association rule mining. It also discusses data warehousing concepts like dimensional modeling using star schemas and snowflake schemas to organize data for analysis. Common data mining techniques like decision trees, neural networks, and association rule mining are also summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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2. Overview
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What is data warehouse?
Why data warehouse?
Data reconciliation – ETL process
Data warehouse architectures
Star schema – dimensional modeling
Data analysis
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3. What is Data Warehouse?
• Defined in many different ways, but not rigorously.
– A decision support database that is maintained separately from
the organization’s operational database
– Support information processing by providing a solid platform of
consolidated, historical data for analysis.
• “A data warehouse is a subject-oriented, integrated, timevariant, and nonvolatile collection of data in support of
management’s decision-making process.”—W. H. Inmon
• Data warehousing:
– The process of constructing and using data warehouses
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4. Data Warehouse—SubjectOriented
• Organized around major subjects, such as
customer, product, sales
• Focusing on the modeling and analysis of data
for decision makers, not on daily operations or
transaction processing
• Provide a simple and concise view around
particular subject issues by excluding data that
are not useful in the decision support process
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5. Data Warehouse—Integrated
• Constructed by integrating multiple,
heterogeneous data sources
– relational databases, flat files, on-line transaction
records
• Data cleaning and data integration techniques
are applied.
– Ensure consistency in naming conventions,
encoding structures, attribute measures, etc. among
different data sources
• E.g., Hotel price: currency, tax, breakfast covered, etc.
– When data is moved to the warehouse, it is
converted.
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6. Data Warehouse—Time Variant
• The time horizon for the data warehouse is
significantly longer than that of operational
systems
– Operational database: current value data
– Data warehouse data: provide information from a
historical perspective (e.g., past 5-10 years)
• Every key structure in the data warehouse
– Contains an element of time, explicitly or implicitly
– But the key of operational data may or may not
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contain “time element”
7. Data Warehouse—Nonvolatile
• A physically separate store of data transformed
from the operational environment
• Operational update of data does not occur in the
data warehouse environment
– Does not require transaction processing, recovery,
and concurrency control mechanisms
– Requires only two operations in data accessing:
• initial loading of data and access of data
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8. Trends in Organisations that encourage
the need for data warehousing
• No single system of record
• Multiple systems are not synchronized
• Organisations want to analyse the activities in
a balanced way
• Customer relationship management
• Supplier relationship management
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9. Need for Data Warehousing
• Integrated, company-wide view of high-quality
information (from different databases)
• Separation of operational and informational systems
and data (for improved performance)
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10. Operational & Informational System
The need to separate operational and informational
systems is based on three primary factors:
• A data warehouse centralizes data that are scattered
throughout disparate operational systems and make them
a available for decision support applications
• A properly designed data warehouse adds value to data
by improving their quality
• A separate data warehouse eliminates much of contention
for resources that result when informational application
confounded with operational processing
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12. Data Reconciliation
• Typical operational data is:
– Transient – not historical
– Not normalised (perhaps due to denormalisation for
performance)
– Restricted in scope – not comprehensive
– Sometimes poor quality – inconsistencies and errors
• After ETL (Extract, Transform, Load), data
should be:
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–
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Detailed – not summarized yet
Historical – periodic
Normalised – 3rd normal form or higher
Comprehensive – enterprise-wide perspective
Timely – data should be current enough to assist decisionmaking
– Quality controlled – accurate with full integrity
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13. The ETL Process/ Data
Reconciliation Main Steps
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Capture/Extract
Scrub or data cleansing
Transform
Load and Index
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14. Static extract = capturing a
Incremental extract =
snapshot of the source data at a point
in time
capturing changes that have
occurred since the last static extract
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16. Record-level:
Field-level:
Selection – data partitioning
Joining – data combining
Aggregation – data summarization
single-field – from one field to one field
multi-field – from many fields to one, or
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one field to many
17. Refresh mode: bulk rewriting of
target data at periodic intervals
Update mode: only changes in
source data are written to data
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warehouse
18. Data Warehouse Architectures
• Generic Two-Level Architecture
• Independent Data Mart
• Dependent Data Mart and Operational
Data Store
• Logical Data Mart and @ctive
Warehouse
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20. Independent data mart
Data marts:
Mini-warehouses, limited in scope
L
T
E
Separate ETL for each
independent data mart
Data access complexity
due to multiple data marts
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21. Dependent data mart with
operational data store
ODS provides option for
obtaining current data
L
T
E
Single ETL for
enterprise data warehouse (EDW)
Dependent data marts
loaded from21
EDW
22. ODS and data warehouse
are one and the same
L
T
E
Near real-time ETL for
@active Data Warehouse
Data marts are NOT separate
databases, but logical views of the
data warehouse
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Easier to create new data marts
23. Data Characteristics
Status vs. Event Data
Status
Event – a database action
(create/update/delete) that
results from a transaction
Status
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24. Data Characteristics
Transient vs.
Periodic Data
Changes to existing
records are written
over previous
records, thus
destroying the
previous data content
Data are never
physically altered or
deleted once they
have been added to
the store
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25. star schema
Fact tables contain
factual or quantitative
data
1:N relationship
between dimension
tables and fact
tables
Dimension tables
are denormalized to
maximize
performance
Dimension tables contain
descriptions about the
subjects of the business
Star Schema: Simple database design in
which dimensional data are separated from
fact data. Excellent for queries, but bad for
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online transaction processing
26. Star schema example
Fact table provides statistics for sales broken
down by product, period and store dimensions
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28. On-Line Analytical Processing (OLAP)
• The use of a set of graphical tools that
provides users with multidimensional views of
their data and allows them to analyze the
data using simple windowing techniques
• Relational OLAP (ROLAP)
– Traditional relational representation
• Multidimensional OLAP (MOLAP)
– Cube structure
• OLAP Operations
– Cube slicing – come up with 2-D view of data
– Drill-down – going from summary to more
detailed views
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29. Data Warehouse vs. Operational
DBMS
• OLTP (on-line transaction processing)
– Major task of traditional relational DBMS
– Day-to-day operations: purchasing, inventory, banking,
manufacturing, payroll, registration, accounting, etc.
• OLAP (on-line analytical processing)
– Major task of data warehouse system
– Data analysis and decision making
• Distinct features (OLTP vs. OLAP):
– User and system orientation: customer vs. market
– Data contents: current, detailed vs. historical, consolidated
– Database design: ER + application vs. star + subject
– View: current, local vs. evolutionary, integrated
– Access patterns: update vs. read-only but complex queries
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30. OLTP vs. OLAP
OLTP
OLAP
users
clerk, IT professional
knowledge worker
function
day to day operations
decision support
DB design
application-oriented
subject-oriented
data
current, up-to-date
detailed, flat relational
isolated
repetitive
historical,
summarized, multidimensional
integrated, consolidated
ad-hoc
lots of scans
unit of work
read/write
index/hash on prim. key
short, simple transaction
# records accessed
tens
millions
#users
thousands
hundreds
DB size
100MB-GB
100GB-TB
metric
transaction throughput
query throughput, response
usage
access
complex query
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33. Data Warehouse Usage
• Three kinds of data warehouse applications
– Information processing
• supports querying, basic statistical analysis, and reporting
using crosstabs, tables, charts and graphs
– Analytical processing
• multidimensional analysis of data warehouse data
• supports basic OLAP operations, slice-dice, drilling, pivoting
– Data mining
• knowledge discovery from hidden patterns
• supports associations, constructing analytical models,
performing classification and prediction, and presenting the
mining results using visualization tools
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