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.
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
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
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
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.
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.
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 .
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.
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.
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
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
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
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.
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.
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 .
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.
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 concepts. It defines a data warehouse as a collection of data marts representing historical data from different company operations. It discusses the top-down and bottom-up approaches to building a data warehouse, as well as considerations for data warehouse design including data content, metadata, data distribution, and tools. Finally, it briefly describes different architectures for mapping a data warehouse to a multiprocessor system, including shared memory, shared disk, and shared nothing architectures.
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.
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
Data warehousing involves integrating data from multiple sources into a single database to support analysis and decision making. It includes cleaning, integrating, and consolidating data. A data warehouse is subject-oriented, integrated, non-volatile, and time-variant. It differs from a transactional database by collecting extensive data for analytics rather than real-time transactions. A typical architecture includes data storage, an OLAP server for analysis, and front-end tools. Data is mined for patterns to devise sales and profit strategies. There are three main types: an enterprise data warehouse serving the whole organization, an operational data store refreshing in real-time, and departmental data marts.
The document discusses key concepts related to data warehousing including:
1) What data warehousing is, its main components, and differences from OLTP systems.
2) The typical architecture of a data warehouse including operational data sources, storage, and end-user access tools.
3) Important considerations like data flows, integration, management of metadata, and tools/technologies used.
4) Additional topics such as benefits, challenges, administration, and data marts.
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 lake stores all types of structured and unstructured data in its raw format to be analyzed later. This allows organizations to store large amounts of data cheaply without deciding upfront how it will be used. A data lake is useful for large organizations with many possible ways to analyze diverse data or those collecting data without a specific plan. In contrast, a data warehouse stores only structured data optimized for queries to support reporting and analysis.
Implementation of Data Marts in Data ware houseIJARIIT
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.).
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.
Unit-IV-Introduction to Data Warehousing .pptxHarsha Patel
Data warehousing combines data from multiple sources to ensure data quality and accuracy. It separates analytics processing from transactional databases. A data warehouse stores historical data and allows fast querying of all data, using OLAP, while a database stores current transactions for online processing using OLTP. A multidimensional data model organizes data into cubes with dimensions and facts to allow analyzing data from different perspectives. Key components of a data warehouse architecture include external data sources, a staging area using ETL, the data warehouse, and data marts containing subsets of warehouse data.
The document discusses building a data warehouse. It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used for decision making. It describes the components of a data warehouse including staging, data warehouse database, transformation tools, metadata, data marts, access tools and administration. It also discusses approaches to building a data warehouse, design considerations, implementation steps, extraction/transformation tools, and user levels. The benefits of a data warehouse include locating the right information, presentation of information, testing hypotheses, discovery of information, and sharing analysis.
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.
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.
This document discusses building a data warehouse. It defines key components of a data warehouse including the data warehouse database, transformation tools, metadata, access tools, and data marts. It describes two common approaches to building a data warehouse - top-down and bottom-up. Top-down involves building a centralized data warehouse first while bottom-up involves building departmental data marts initially. The document also outlines considerations for designing, implementing, and accessing a data warehouse.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
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.
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.
This document provides an overview of data warehousing concepts. It defines a data warehouse as a collection of data marts representing historical data from different company operations. It discusses the top-down and bottom-up approaches to building a data warehouse, as well as considerations for data warehouse design including data content, metadata, data distribution, and tools. Finally, it briefly describes different architectures for mapping a data warehouse to a multiprocessor system, including shared memory, shared disk, and shared nothing architectures.
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.
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
Data warehousing involves integrating data from multiple sources into a single database to support analysis and decision making. It includes cleaning, integrating, and consolidating data. A data warehouse is subject-oriented, integrated, non-volatile, and time-variant. It differs from a transactional database by collecting extensive data for analytics rather than real-time transactions. A typical architecture includes data storage, an OLAP server for analysis, and front-end tools. Data is mined for patterns to devise sales and profit strategies. There are three main types: an enterprise data warehouse serving the whole organization, an operational data store refreshing in real-time, and departmental data marts.
The document discusses key concepts related to data warehousing including:
1) What data warehousing is, its main components, and differences from OLTP systems.
2) The typical architecture of a data warehouse including operational data sources, storage, and end-user access tools.
3) Important considerations like data flows, integration, management of metadata, and tools/technologies used.
4) Additional topics such as benefits, challenges, administration, and data marts.
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 lake stores all types of structured and unstructured data in its raw format to be analyzed later. This allows organizations to store large amounts of data cheaply without deciding upfront how it will be used. A data lake is useful for large organizations with many possible ways to analyze diverse data or those collecting data without a specific plan. In contrast, a data warehouse stores only structured data optimized for queries to support reporting and analysis.
Implementation of Data Marts in Data ware houseIJARIIT
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.).
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.
Unit-IV-Introduction to Data Warehousing .pptxHarsha Patel
Data warehousing combines data from multiple sources to ensure data quality and accuracy. It separates analytics processing from transactional databases. A data warehouse stores historical data and allows fast querying of all data, using OLAP, while a database stores current transactions for online processing using OLTP. A multidimensional data model organizes data into cubes with dimensions and facts to allow analyzing data from different perspectives. Key components of a data warehouse architecture include external data sources, a staging area using ETL, the data warehouse, and data marts containing subsets of warehouse data.
The document discusses building a data warehouse. It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used for decision making. It describes the components of a data warehouse including staging, data warehouse database, transformation tools, metadata, data marts, access tools and administration. It also discusses approaches to building a data warehouse, design considerations, implementation steps, extraction/transformation tools, and user levels. The benefits of a data warehouse include locating the right information, presentation of information, testing hypotheses, discovery of information, and sharing analysis.
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.
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.
This document discusses building a data warehouse. It defines key components of a data warehouse including the data warehouse database, transformation tools, metadata, access tools, and data marts. It describes two common approaches to building a data warehouse - top-down and bottom-up. Top-down involves building a centralized data warehouse first while bottom-up involves building departmental data marts initially. The document also outlines considerations for designing, implementing, and accessing a data warehouse.
Data Warehouse – Introduction, characteristics, architecture, scheme and modelling, Differences between operational database systems and data warehouse.
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.
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.
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Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...ThinkInnovation
Objective
To identify the impact of speed limit restrictions in different constituencies over the years with the help of DID technique to conclude whether having strict speed limit restrictions can help to reduce the increasing number of road accidents on weekends.
Context*
Generally, on weekends people tend to spend time with their family and friends and go for outings, parties, shopping, etc. which results in an increased number of vehicles and crowds on the roads.
Over the years a rapid increase in road casualties was observed on weekends by the Government.
In the year 2005, the Government wanted to identify the impact of road safety laws, especially the speed limit restrictions in different states with the help of government records for the past 10 years (1995-2004), the objective was to introduce/revive road safety laws accordingly for all the states to reduce the increasing number of road casualties on weekends
* The Speed limit restriction can be observed before 2000 year as well, but the strict speed limit restriction rule was implemented from 2000 year to understand the impact
Strategies
Observe the Difference in Differences between ‘year’ >= 2000 & ‘year’ <2000
Observe the outcome from multiple linear regression by considering all the independent variables & the interaction term
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202406 - Cape Town Snowflake User Group - LLM & RAG.pdfDouglas Day
Content from the July 2024 Cape Town Snowflake User Group focusing on Large Language Model (LLM) functions in Snowflake Cortex. Topics include:
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Dive into the details of how to leverage these advanced features without leaving the Snowflake environment.
2. Introduction
• ETL is a processthat extractsthe datafrom different sourcesystems, then transforms
the data
• and finally loadsthe datainto the DataWarehousesystem. Full form of ETL is Extract,
TransformandLoad.
3. • The ETL process requiresactiveinputs from variousstakeholdersincluding
developers, analysts,testers, top executives and is technically challenging.
• ETL is a recurringactivity(daily,weekly,monthly) of a Datawarehousesystem
4. Extraction of data from source systems
• Source systems canbe RDBMS andfiles
• Datais extractedfromsource systems
• The mainobjectiveof this step isto retrieveall requireddatafrom sourcesystems
• The extractionstep should be designedin such a waythatit should not havenegative
effect on source systems
5. Data Transformation
• Thisstep includes cleaning,filtering,validatingandapplicationof rules to extracteddata
• The mainobjectiveof this step isto load the extracteddatainto targetdatabasewith clean
andgeneralformat
• The dataextractionisdone fromdifferentsources havingtheir ownformat
• E.g. dateformatsfrom twosources, dd/mm/yyyyand yyyy/mm/dd
6. Loading
• The third and final step of the ETL process is loading. In this step, the
transformed data is finally loaded into the data warehouse.
• Sometimes the data is updated by loading into the data warehouse very
frequently and sometimes it is done after longer but regular intervals.
• The rate and period of loading solely depends on the requirements and
varies from system to system.
7. Introduction of data warehouse
• A Data Warehouse is Built by combining data from multiple diverse sources
• Data Warehousing is a step-by-step approach for constructing and using a Data
Warehouse.
• After the data is loaded, it often cleansed, transformed, and checked for quality
8. What is data warehouse?
• A Data Warehouse is a collection of software tools that facilitates analysis of a
large set of business data used to help an organization make decisions.
• A large amount of data in data warehouses comes from numerous sources such
that internal applications like marketing, sales, and finance; customer-facing
apps.
• It is a centralized data repository for analysts that can be queried whenever
required for business benefits.
9.
10. What is data warehousing?
The process of creating data warehouses to store a large amount of data is
named Data Warehousing.
Data Warehousing helps to improve the speed and efficiency of accessing
different data sets .
and makes it easier for company decision-makers to obtain insights that will help
the business.
11. The main goal of data warehousing
To create a hoarded wealth of historical data that can be retrieved and analyzed
to supply helpful insight into the organization’s operations.
12. Need of data warehousing.
Data Warehousing is a progressively essential tool for business intelligence.
It allows organizations to make quality business decisions.
• Business Users
• Maintains consistency
• Make strategic decisions
• High response time
13. Characteristics of data warehouse
1. Subject Oriented: A data warehouse is often subject-oriented because it delivers
may be achieved on a
particular theme .These themes are often sales, distribution, selling. etc.
2. Integrated: A data warehouse is created by integrating data from numerous
different sources such that from mainframe computers and a relational database.
3. Non-volatile: The data residing in the data warehouse is permanent
means that the data in the data warehouse cannot be erased or deleted.
14. Latest tools and technologies for data
warehousing :
1. Amazon Redshift
2. Microsoft Azure
3. Google BigQuery
4. Snowflake
5. Micro Focus Vertica
6. Teradata
7. Amazon DynamoDB
8. PostgreSQL
9. Amazon RD
10. Amazon S3
15. What is data marts
A datamart isa simple form of datawarehousefocused on a single subject or line of
business.
Witha datamart,teams canaccessdataandgaininsightsfaster,because they don’t
have to spendtime searchingwithina more complex datawarehouseor manually
aggregatingdatafrom differentsources.
16. Why create a data mart?
A data mart provides easier access to data required by a specific team or
line of business within your organization.
Teams forced to locate data from various sources most often rely on
spreadsheets to share this data and collaborate.
This usually results in human errors, confusion, complex reconciliations,
and multiple sources of truth—the so-called “spreadsheet nightmare.”
17. A data warehouse is a data management system designed to support
business intelligence and analytics for an entire organization. Data
warehouses often contain large amounts of data, including historical data.
A data mart is a simple form of a data warehouse that is focused on a
single subject or line of business, such as sales, finance, or marketing.
18. The benefits of data mart
• A single source of truth.
• Quicker access to data.
• Faster insights leading to faster decision making.
• Simpler and faster implementation.
• Creating agile and scalable data management.
• Transient analysis.
19. Architecture and components of data
warehouse
Data warehouse architecture defines the comprehensive architecture of data processing and
presentation that will be useful for data analysis and decision making within the enterprise and
organization. Each organization has different data warehouses depending upon their need, but
all of them are characterized by some standard components.
Data Warehouse applications are designed to support the user’s data requirements, an example
of this is online analytical processing (OLAP). These include functions such as forecasting,
profiling, summary reporting, and trend analysis.
The architecture of the data warehouse mainly consists of the proper arrangement of its
elements, to build an efficient data warehouse with software and hardware components. The
elements and components may vary based on the requirement of organizations. All of these
depend on the organization’s circumstances.
20.
21. 1. Source data components
In the Data Warehouse, the source data comes from different places. They are
group into four categories:
• External Data
• Internal Data
• Operational System data
• Flat files
22. 2. Data staging
After the data is extracted from various sources, now it’s time to prepare the data files for storing in the
data warehouse. The extracted data collected from various sources must be transformed and made ready
in a format that is suitable to be saved in the data warehouse for querying and analysis.
23. The data staging contains three primary functions
that take place in this part:
• Data Extraction
• Data Transformation
• Data Loading
24. 3. Data storage in warehouse
Data storage for data warehousing is split into multiple repositories.
• Metadata: Metadata means data about data i.e. it summarizes basic details
regarding data, creating findings & operating with explicit instances of data.
• Raw Data: Raw data is a set of data and information that has not yet been
processed and was delivered from a particular data entity to the data supplier
and hasn’t been processed nonetheless by machine or human.
• Summary Data or Data summary: Data summary is an easy term for a brief
conclusion of an enormous theory or a paragraph. This is often one thing where
analysts write the code and in the end, they declare the ultimate end in the form
of summarizing data.
25. 4. Data marts:
Data marts are also the part of storage component in a data warehouse. It can
store the information of a specific function of an organization that is
single authority. There may be any number of data marts in a particular
organization depending upon the functions. In short, data marts contain subsets
of the data stored in data warehouses.
Now, the users and analysts can use data for various applications like reporting,
analyzing, mining, etc. The data is made available to them whenever required.
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