A data mart is a persistent physical store of operational and aggregated data statistically processed data that supports businesspeople in making decisions based primarily on analyses of past activities and results. A data mart contains a predefined subset of enterprise data organized for rapid analysis and reporting. Data warehousing has come into being because the file structure of the large mainframe core business systems is inimical to information retrieval. The purpose of the data warehouse is to combine core business and data from other sources in a format that facilitates reporting and decision support. In just a few years, data warehouses have evolved from large, centralized data repositories to subject specific, but independent, data marts and now to dependent marts that load data from a central repository of Data Staging files that has previously extracted data from the institution’s operational business systems (e.g., student record, finance and human resource systems, etc.).
A Computer database is a collection of logically related data that is stored in a computer system,so that a computer program or person using a query language can use it to answer queries. An operational database (OLTP) contains up-to-date, modifiable application specific data. A data warehouse (OLAP) is a subject-oriented, integrated, time-variant and non-volatile collection of data used to make business decisions. Hadoop Distributed File System (HDFS) allows storing large amount of data on a cloud of
machines. In this paper, we surveyed the literature related to operational databases, data warehouse and hadoop technology.
This document outlines the key issues in data warehousing and online analytical processing (OLAP) in e-business. It defines data warehousing as a centralized repository that extracts and integrates relevant information from multiple sources to support decision making. OLAP enables multi-dimensional analysis of data stored in a database. The problem is that operational databases are not optimized for analytical reporting. This leads to slow response times for reports needed by top management. The research aims to develop a tool to convert operational databases into a star schema structure for faster multi-dimensional analysis and reporting.
The document presents information on data warehousing. It defines a data warehouse as a repository for integrating enterprise data for analysis and decision making. It describes the key components, including operational data sources, an operational data store, and end-user access tools. It also outlines the processes of extracting, cleaning, transforming, loading and accessing the data, as well as common management tools. Data marts are discussed as focused subsets of a data warehouse tailored for a specific department.
The document defines a data warehouse as a copy of transaction data structured specifically for querying and reporting. Key points are that a data warehouse can have various data storage forms, often focuses on a specific activity or entity, and is designed for querying and analysis rather than transactions. Data warehouses differ from operational systems in goals, structure, size, technologies used, and prioritizing historic over current data. They are used for knowledge discovery through consolidated reporting, finding relationships, and data mining.
This document provides an overview of data warehousing and data mining. It discusses key topics such as the definition of a data warehouse, its characteristics and architectures. It also describes how data is stored and modeled in a data warehouse. The document then covers data mining, outlining its process and elements. It discusses advantages of both data warehousing and data mining such as enhanced business intelligence and improved decision making. Disadvantages like privacy and security issues are also presented.
The document discusses data mining and provides an overview of key concepts. It describes data mining as the process of discovering patterns in large data sets involving techniques like classification, clustering, association rule mining, and outlier detection. It also discusses different types of data that can be mined, including transactional data and text data. Additionally, it presents different classifications of data mining systems based on the type of data, knowledge discovered, and techniques used.
The document discusses data warehouses and their advantages. It describes the different views of a data warehouse including the top-down view, data source view, data warehouse view, and business query view. It also discusses approaches to building a data warehouse, including top-down and bottom-up, and steps involved including planning, requirements, design, integration, and deployment. Finally, it discusses technologies used to populate and refresh data warehouses like extraction, cleaning, transformation, load, and refresh tools.
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.
A Computer database is a collection of logically related data that is stored in a computer system,so that a computer program or person using a query language can use it to answer queries. An operational database (OLTP) contains up-to-date, modifiable application specific data. A data warehouse (OLAP) is a subject-oriented, integrated, time-variant and non-volatile collection of data used to make business decisions. Hadoop Distributed File System (HDFS) allows storing large amount of data on a cloud of
machines. In this paper, we surveyed the literature related to operational databases, data warehouse and hadoop technology.
This document outlines the key issues in data warehousing and online analytical processing (OLAP) in e-business. It defines data warehousing as a centralized repository that extracts and integrates relevant information from multiple sources to support decision making. OLAP enables multi-dimensional analysis of data stored in a database. The problem is that operational databases are not optimized for analytical reporting. This leads to slow response times for reports needed by top management. The research aims to develop a tool to convert operational databases into a star schema structure for faster multi-dimensional analysis and reporting.
The document presents information on data warehousing. It defines a data warehouse as a repository for integrating enterprise data for analysis and decision making. It describes the key components, including operational data sources, an operational data store, and end-user access tools. It also outlines the processes of extracting, cleaning, transforming, loading and accessing the data, as well as common management tools. Data marts are discussed as focused subsets of a data warehouse tailored for a specific department.
The document defines a data warehouse as a copy of transaction data structured specifically for querying and reporting. Key points are that a data warehouse can have various data storage forms, often focuses on a specific activity or entity, and is designed for querying and analysis rather than transactions. Data warehouses differ from operational systems in goals, structure, size, technologies used, and prioritizing historic over current data. They are used for knowledge discovery through consolidated reporting, finding relationships, and data mining.
This document provides an overview of data warehousing and data mining. It discusses key topics such as the definition of a data warehouse, its characteristics and architectures. It also describes how data is stored and modeled in a data warehouse. The document then covers data mining, outlining its process and elements. It discusses advantages of both data warehousing and data mining such as enhanced business intelligence and improved decision making. Disadvantages like privacy and security issues are also presented.
The document discusses data mining and provides an overview of key concepts. It describes data mining as the process of discovering patterns in large data sets involving techniques like classification, clustering, association rule mining, and outlier detection. It also discusses different types of data that can be mined, including transactional data and text data. Additionally, it presents different classifications of data mining systems based on the type of data, knowledge discovered, and techniques used.
The document discusses data warehouses and their advantages. It describes the different views of a data warehouse including the top-down view, data source view, data warehouse view, and business query view. It also discusses approaches to building a data warehouse, including top-down and bottom-up, and steps involved including planning, requirements, design, integration, and deployment. Finally, it discusses technologies used to populate and refresh data warehouses like extraction, cleaning, transformation, load, and refresh tools.
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.
This document provides an overview of data warehousing, OLAP, data mining, and big data. It discusses how data warehouses integrate data from different sources to create a consistent view for analysis. OLAP enables interactive analysis of aggregated data through multidimensional views and calculations. Data mining finds hidden patterns in large datasets through techniques like predictive modeling, segmentation, link analysis and deviation detection. The document provides examples of how these technologies are used in industries like retail, banking and insurance.
This document discusses a student's assignment submission on the topics of data warehousing and data mining. It provides definitions and explanations of key concepts related to data warehousing such as the three layers of a data warehouse (staging, integration, access), ETL processes, dimensional vs normalized data storage approaches, and top-down vs bottom-up design methodologies. For data mining, it outlines the typical processes of pre-processing, data mining tasks like classification and clustering, and results validation. Sample applications are also listed for both data warehousing and data mining.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
The document 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 data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
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.
The document defines and describes key concepts related to data warehousing. It provides definitions of data warehousing, data warehouse features including being subject-oriented, integrated, and time-variant. It discusses why data warehousing is needed, using scenarios of companies wanting consolidated sales reports. The 3-tier architecture of extraction/transformation, data warehouse storage, and retrieval is covered. Data marts are defined as subsets of the data warehouse. Finally, the document contrasts databases with data warehouses and describes OLAP operations.
The document provides information about data warehousing concepts. It defines a data warehouse as a relational database designed for query and analysis rather than transactions. It contains historical data from various sources and separates analysis from transaction workloads. The goals of a data warehouse are to provide a single source of integrated information, give users direct access to data without relying on IT, and allow predictive modeling. Factors like significant user requests for related historical data and advanced decision support needs should be considered when implementing a data warehouse.
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 key components of a data warehouse are the source data component, data staging component, data storage component, information delivery component, meta-data component, and management and control component. The source data component includes production data, internal data, archived data, and external data. The data staging component involves extracting, transforming through processes like handling synonyms and homonyms, and loading the data. The information delivery component provides access and reports to different user types from novice to senior executives.
The document provides an overview of database management systems including data warehousing, data mining, data definition language, data control language, and data manipulation language. It defines each concept and provides examples. For data warehousing, it describes the purpose, components, architecture, evolution of use, advantages, and disadvantages. For data mining, it discusses the introduction, definition, goal, process, tools, and advantages/disadvantages. It also explains the CREATE, ALTER, DROP statements for data definition language, the GRANT and REVOKE commands for data control language, and the INSERT, SELECT, UPDATE, DELETE commands for data manipulation language.
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.
The document discusses data warehouses and their characteristics. A data warehouse integrates data from multiple sources and transforms it into a multidimensional structure to support decision making. It has a complex architecture including source systems, a staging area, operational data stores, and the data warehouse. A data warehouse also has a complex lifecycle as business rules change and new data requirements emerge over time, requiring the architecture to evolve.
This document provides an overview of the 3-tier data warehouse architecture. It discusses the three tiers: the bottom tier contains the data warehouse server which fetches relevant data from various data sources and loads it into the data warehouse using backend tools for extraction, cleaning, transformation and loading. The bottom tier also contains the data marts and metadata repository. The middle tier contains the OLAP server which presents multidimensional data to users from the data warehouse and data marts. The top tier contains the front-end tools like query, reporting and analysis tools that allow users to access and analyze the data.
This document provides an overview of key concepts related to decision support systems (DSS) and data warehousing. It defines DSS as interactive computer systems that help decision makers use data, documents, models and communication technologies to identify and solve problems. It then discusses operational databases and how they differ from data warehouses in areas like data type, focus, users and more. Finally, it defines key characteristics of a data warehouse as being subject-oriented, integrated, time-variant and non-volatile to support management decision making.
The Data Warehouse is a database which merges, summarizes and analyzes all data sources of a company/organization. Users can request particular data from the system (such the number of sales within a certain period) and will be provided with the respective information.
With the help of the Data Warehouse, you can quickly access different systems and look at historic data. Due to the vast amount of data it provides, the Data Warehouse is an essential tool when making management decisions.
This document is about Data Warehouse Tools such as:
OLAP (On – line Analytical Processing)
OLTP (On – Line Transaction Processing)
Business Intelligence
Driving Force
Data Mart
Meta Data
Lecture 03 - The Data Warehouse and Design phanleson
The chapter discusses the design process for building a data warehouse, including designing the interface from operational systems and designing the data warehouse itself. It covers topics like beginning with operational data, data and process models, data warehouse data models at different levels, normalization and denormalization, and managing complexity in transformations and integrations between operational and warehouse systems. The goal is to extract, transform, and load relevant data from operational sources into the data warehouse in a way that supports analysis and decision-making.
A data warehouse integrates data from multiple sources into a central repository used for creating trend reports for senior management. It stores current and historical data. Data marts extract specific data from the data warehouse to provide quicker access to frequently used data for groups of users, improving response time at a lower cost than a full data warehouse.
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 discusses implementing a data warehouse for an insurance company called Axiom Care. It outlines the problems with the company's current inability to provide strategic information to decision makers. The proposed solution is to create a data warehouse with schemas representing policy creation and claims processing data. It provides a work breakdown structure and job schedule for the project. Risks are also identified such as unrealistic timelines, budget issues, and technical challenges around data quality and scalability.
This document provides an overview of data warehousing, OLAP, data mining, and big data. It discusses how data warehouses integrate data from different sources to create a consistent view for analysis. OLAP enables interactive analysis of aggregated data through multidimensional views and calculations. Data mining finds hidden patterns in large datasets through techniques like predictive modeling, segmentation, link analysis and deviation detection. The document provides examples of how these technologies are used in industries like retail, banking and insurance.
This document discusses a student's assignment submission on the topics of data warehousing and data mining. It provides definitions and explanations of key concepts related to data warehousing such as the three layers of a data warehouse (staging, integration, access), ETL processes, dimensional vs normalized data storage approaches, and top-down vs bottom-up design methodologies. For data mining, it outlines the typical processes of pre-processing, data mining tasks like classification and clustering, and results validation. Sample applications are also listed for both data warehousing and data mining.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
The document 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 data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
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.
The document defines and describes key concepts related to data warehousing. It provides definitions of data warehousing, data warehouse features including being subject-oriented, integrated, and time-variant. It discusses why data warehousing is needed, using scenarios of companies wanting consolidated sales reports. The 3-tier architecture of extraction/transformation, data warehouse storage, and retrieval is covered. Data marts are defined as subsets of the data warehouse. Finally, the document contrasts databases with data warehouses and describes OLAP operations.
The document provides information about data warehousing concepts. It defines a data warehouse as a relational database designed for query and analysis rather than transactions. It contains historical data from various sources and separates analysis from transaction workloads. The goals of a data warehouse are to provide a single source of integrated information, give users direct access to data without relying on IT, and allow predictive modeling. Factors like significant user requests for related historical data and advanced decision support needs should be considered when implementing a data warehouse.
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 key components of a data warehouse are the source data component, data staging component, data storage component, information delivery component, meta-data component, and management and control component. The source data component includes production data, internal data, archived data, and external data. The data staging component involves extracting, transforming through processes like handling synonyms and homonyms, and loading the data. The information delivery component provides access and reports to different user types from novice to senior executives.
The document provides an overview of database management systems including data warehousing, data mining, data definition language, data control language, and data manipulation language. It defines each concept and provides examples. For data warehousing, it describes the purpose, components, architecture, evolution of use, advantages, and disadvantages. For data mining, it discusses the introduction, definition, goal, process, tools, and advantages/disadvantages. It also explains the CREATE, ALTER, DROP statements for data definition language, the GRANT and REVOKE commands for data control language, and the INSERT, SELECT, UPDATE, DELETE commands for data manipulation language.
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.
The document discusses data warehouses and their characteristics. A data warehouse integrates data from multiple sources and transforms it into a multidimensional structure to support decision making. It has a complex architecture including source systems, a staging area, operational data stores, and the data warehouse. A data warehouse also has a complex lifecycle as business rules change and new data requirements emerge over time, requiring the architecture to evolve.
This document provides an overview of the 3-tier data warehouse architecture. It discusses the three tiers: the bottom tier contains the data warehouse server which fetches relevant data from various data sources and loads it into the data warehouse using backend tools for extraction, cleaning, transformation and loading. The bottom tier also contains the data marts and metadata repository. The middle tier contains the OLAP server which presents multidimensional data to users from the data warehouse and data marts. The top tier contains the front-end tools like query, reporting and analysis tools that allow users to access and analyze the data.
This document provides an overview of key concepts related to decision support systems (DSS) and data warehousing. It defines DSS as interactive computer systems that help decision makers use data, documents, models and communication technologies to identify and solve problems. It then discusses operational databases and how they differ from data warehouses in areas like data type, focus, users and more. Finally, it defines key characteristics of a data warehouse as being subject-oriented, integrated, time-variant and non-volatile to support management decision making.
The Data Warehouse is a database which merges, summarizes and analyzes all data sources of a company/organization. Users can request particular data from the system (such the number of sales within a certain period) and will be provided with the respective information.
With the help of the Data Warehouse, you can quickly access different systems and look at historic data. Due to the vast amount of data it provides, the Data Warehouse is an essential tool when making management decisions.
This document is about Data Warehouse Tools such as:
OLAP (On – line Analytical Processing)
OLTP (On – Line Transaction Processing)
Business Intelligence
Driving Force
Data Mart
Meta Data
Lecture 03 - The Data Warehouse and Design phanleson
The chapter discusses the design process for building a data warehouse, including designing the interface from operational systems and designing the data warehouse itself. It covers topics like beginning with operational data, data and process models, data warehouse data models at different levels, normalization and denormalization, and managing complexity in transformations and integrations between operational and warehouse systems. The goal is to extract, transform, and load relevant data from operational sources into the data warehouse in a way that supports analysis and decision-making.
A data warehouse integrates data from multiple sources into a central repository used for creating trend reports for senior management. It stores current and historical data. Data marts extract specific data from the data warehouse to provide quicker access to frequently used data for groups of users, improving response time at a lower cost than a full data warehouse.
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 discusses implementing a data warehouse for an insurance company called Axiom Care. It outlines the problems with the company's current inability to provide strategic information to decision makers. The proposed solution is to create a data warehouse with schemas representing policy creation and claims processing data. It provides a work breakdown structure and job schedule for the project. Risks are also identified such as unrealistic timelines, budget issues, and technical challenges around data quality and scalability.
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.
This document provides information on designing a data warehouse, including key concepts like star schemas, snowflake schemas, dimensions, facts, and granularity. It discusses why an OLTP database may not be suitable for reporting and emphasizes the importance of auditing in a data warehouse to track changes over time through lineage information. Dimension tables contain attributes and keys while fact tables contain measures and foreign keys to dimensions.
This document discusses data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-variant collection of data used to support management decision making. It extracts data from multiple sources to create a consistent view. A data mart is a limited scope data warehouse. The document also describes the need for data warehousing to provide an integrated company-wide view and separate operational and informational systems. It outlines the ETL process to extract, transform and load data into a data warehouse.
Bài 1: Làm quen với SQL Server 2008 - Giáo trình FPTMasterCode.vn
Truy cập tới CSDL qua mạng
Hỗ trợ mô hình Client/Server
Kho dữ liệu (Data WareHouse)
Tương thích với chuẩn ANSI/ISO SQL -92
Hỗ trợ tìm kiếm Full- Text (Full- Text Search)
Hỗ trợ tìm kiếm thông tin trực tuyến (Books Online)
Các kiểu dữ liệu mới và các hàm thư viện làm việc với
các kiểu dữ liệu này như XML, Các kiểu dữ liệu giá trị lớn
(lưu ảnh, video…)
Hỗ trợ FileStream để thao tác với các đối tượng nhị phân
lớn (BLOB)
Language-Integrated Query (LINQ)
Hỗ trợ DotNet 3.5
……
Một số tính năng của SQL Server 2008
Các kiểu dữ liệu mới và các hàm thư viện làm việc với
các kiểu dữ liệu này như XML, Các kiểu dữ liệu giá trị lớn
(lưu ảnh, video…)
Hỗ trợ FileStream để thao tác với các đối tượng nhị phân
lớn (BLOB)
Language-Integrated Query (LINQ)
Hỗ trợ DotNet 3.5
……
The document discusses data warehousing concepts including:
1) A data warehouse is a subject-oriented, integrated, and non-volatile collection of data used for decision making. It stores historical and current data from multiple sources.
2) The architecture of a data warehouse is typically three-tiered, with an operational data tier, data warehouse/data mart tier for storage, and client access tier. OLAP servers allow analysis of stored data.
3) ROLAP and MOLAP refer to relational and multidimensional approaches for OLAP. ROLAP dynamically generates data cubes from relational databases, while MOLAP pre-calculates and stores aggregated data in multidimensional structures.
Bài 2: Các khái niệm trong CSDL quan hệ - Giáo trình FPTMasterCode.vn
Tìm hiểu các bước thiết kế CSDL quan hệ
Tìm hiểu các khái niệm trong thiết kế CSDL quan hệ:
Các khái niệm trong thiết kế CSDL mức khái niệm
Các khái niệm trong thiết kế CSDL mức vật lý
Làm quen với hệ quản trị CSDL Microsoft Access
Tạo các bảng và truy vấn trong Microsoft Access.
The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.
Este documento presenta una guía técnica sobre el uso seguro de escaleras manuales en la industria de la construcción. Explica los materiales apropiados para construir escaleras, los peligros asociados con su uso y recomendaciones generales para prevenir accidentes, como fijar la escalera, usar ambas manos al subir y bajar, y no sobrepasar la altura máxima recomendada.
This document discusses software and tools used in a robotics laboratory, including ROS, MORSE simulator, Blender, Python, and Protege ontology software. It also outlines concepts in ontologies like instances, subclasses, parts, and roles. Finally, it mentions Apache UIMA and webmini frameworks.
A data warehouse consists of several key components:
- Current detail data from operational systems of record which is stored for analysis.
- Integration and transformation programs that convert operational data into a common format for the data warehouse.
- Summarized and archived data used for reporting and analysis over time.
- Metadata that describes the structure and meaning of the data.
Data warehouses are used for standard reporting, queries on summarized data, and data mining of patterns in large datasets to gain business insights.
This document discusses key concepts in data warehousing and modeling. It describes a multitier architecture for data warehousing consisting of a bottom tier warehouse database, middle tier OLAP server, and top tier front-end client tools. It also discusses different data warehouse models including enterprise warehouses, data marts, and virtual warehouses. The document outlines the extraction, transformation, and loading process used to populate data warehouses and the role of metadata repositories.
A data warehouse is a central repository of historical data from an organization's various sources designed for analysis and reporting. It contains integrated data from multiple systems optimized for querying and analysis rather than transactions. Data is extracted, cleaned, and loaded from operational sources into the data warehouse periodically. The data warehouse uses a dimensional model to organize data into facts and dimensions for intuitive analysis and is optimized for reporting rather than transaction processing like operational databases. Data warehousing emerged to meet the growing demand for analysis that operational systems could not support due to impacts on performance and limitations in reporting capabilities.
UNIT 2 DATA WAREHOUSING AND DATA MINING PRESENTATION.pptxshruthisweety4
The document discusses data warehousing and data warehouse architectures. It defines a data warehouse as a system that aggregates data from different sources into a consistent data store to support analysis and machine learning on huge volumes of historical data. It describes three common types of data warehouses and characteristics like being subject-oriented, integrated, and time-variant. It then outlines common data warehouse architectures including single tier, two tier, and three tier architectures and discusses components like the source layer, data staging, data warehouse layer, and analysis layer. Finally, it discusses properties of data warehouse architectures like separation of analytical and transactional processing and scalability.
The document discusses two common data warehouse architectures: independent data marts and a three-layer approach. With independent data marts, data is extracted from source systems into separate data marts, each with their own ETL process. This can result in redundant work and inconsistent data across marts. The three-layer approach includes an enterprise data warehouse, operational data store, and dependent data marts filled from the warehouse, allowing for consistent, consolidated data and easier analysis across subjects.
- A data warehouse is a central repository for an organization's historical data that is used to support management reporting and decision making. It contains data from multiple sources integrated into a consistent structure.
- Data warehouses are optimized for querying and analysis rather than transactions. They use a dimensional model and denormalized structures to improve query performance for business users.
- There are two main approaches to data warehouse design - the dimensional model advocated by Kimball and the normalized model advocated by Inmon. Both have advantages and disadvantages for query performance and ease of use.
Top 60+ Data Warehouse Interview Questions and Answers.pdfDatacademy.ai
This is a comprehensive guide to the most frequently asked data warehouse interview questions and answers. It covers a wide range of topics including data warehousing concepts, ETL processes, dimensional modeling, data storage, and more. The guide aims to assist job seekers, students, and professionals in preparing for data warehouse job interviews and exams.
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 .
The document provides information about data warehousing fundamentals. It discusses key concepts such as data warehouse architectures, dimensional modeling, fact and dimension tables, and metadata. The three common data warehouse architectures described are the basic architecture, architecture with a staging area, and architecture with staging area and data marts. Dimensional modeling is optimized for data retrieval and uses facts, dimensions, and attributes. Metadata provides information about the data in the warehouse.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
The document discusses databases versus data warehousing. It notes that databases are for operational purposes like storage and retrieval for applications, while data warehouses are used for informational purposes like business reporting and analysis. A data warehouse contains integrated, subject-oriented data from multiple sources that is used to support management decisions.
The document discusses emerging trends in database systems, including data warehousing and data mining. It provides definitions and characteristics of data warehousing, including that it involves centralizing organizational data in a central repository for analysis. It contrasts operational and transactional databases with data warehouses, noting that data warehouses are designed for analysis rather than transactions and contain historical, aggregated data. It also discusses data marts, ETL processes, and the components of a typical data warehouse architecture.
The document discusses emerging trends in database systems, including data warehousing and data mining. It provides definitions and characteristics of data warehousing, including that it involves centralizing organizational data in a central repository for analysis. It contrasts operational and transactional databases with data warehouses, noting that data warehouses are designed for analysis rather than transactions and contain historical, summarized data. It also discusses data marts, ETL processes, and the components of a typical data warehouse architecture.
ETL processes , Datawarehouse and Datamarts.pptxParnalSatle
The document discusses ETL processes, data warehousing, and data marts. It defines ETL as extracting data from source systems, transforming it, and loading it into a data warehouse. Data warehouses integrate data from multiple sources to support business intelligence and analytics. Data marts are focused subsets of data warehouses that serve specific business functions or departments. The document outlines the key components and architecture of data warehousing systems, including source data, data staging, data storage in warehouses and marts, and analytical applications.
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,
Data Bases, Data Warehousing, Data Mining, Decision Support System (DSS), OLAP, OLTP, MOLAP, ROLAP, Data Mart, Meta Data, ETL Process, Drill Up, Roll Down, Slicing, Dicing, Star Schema, SnowFlake Scheme, Dimentional Modelling
A data mart is a smaller subset of data from a data warehouse that is tailored to a specific business unit or function. It provides faster access to relevant data than searching an entire data warehouse. There are three main types of data marts - dependent, which get data from a data warehouse; independent, which access data directly from sources; and hybrid, which integrate multiple data sources. Data marts use either a star or snowflake schema to logically structure the data in dimension and fact tables for analysis. Implementing a data mart involves designing it, constructing the logical and physical structures, transferring data using ETL tools, configuring access, and ongoing management.
The document provides an overview of data warehousing. It defines a data warehouse as a repository of information gathered from multiple sources and organized under a unified schema for analysis and reporting. It describes the typical architecture of a data warehouse including data sources, extraction/transformation/loading, the data repository, reporting tools, and metadata. It also covers dimensional modeling, normalization, advantages like increased access and consistency, and concerns around extraction/loading time and compatibility.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
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Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
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#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
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- Steps:
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- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
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- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
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Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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