A data warehouse is an appliance for storing and analyzing data, and reporting.
Central database that includes information from several different sources.
Used to produce reports to
assist in decision-making
and management.
We architect and build Financial Applications - Advisory, Trading, Wealth Management. We provide SAAS services, host on Amazon, Rackspace, Azure and provide24x7 Application/IT support.
Use latest technology to solve Traditional Supply Chain Problems - a brainsto...Vinodh Soundarajan
This document discusses how technology is transforming supply chain management. It notes that the right information needs to get into the right hands at the right time by processing data from various sources like suppliers, inventory, and demand signals. It also outlines some key technology trends like mobile workforce, cloud computing, and multi-channel operations. Specific technologies that could help include streaming analytics, data visualization dashboards, cloud infrastructure, and UI frameworks. Potential supply chain problems are also listed that these technologies may help address, such as collaborative planning, logistics tracking, and inventory optimization.
Removing barriers to innovation by Mark Ryder, Prime Visionprimevision
Post-Expo Asia Presentation
The postal world met in Hong Kong last week at the inaugural Post-Expo Asia Pacific conference and Prime Vision’s Mark Ryder contributed on the themes of removing barriers to innovation and introduction of new technologies.
Mark adds ”The postal market is becoming more exciting than ever, and in some ways more fragmented away from the traditional universal service. Posts are coming up with some very innovative ways to maximise the benefits of their USPs in new ways, whether at the doorstep or behind the scenes services. Meanwhile consumers of postal services also expect great things from these trusted “mega-brands”, meaning that the relevance of the postal operator in the public space is assured. The key is whether individual posts respond to this chaotic and opportunistic environment with technology and services that deliver the required innovations. There can be some major barriers to progress in this area and I explored ways that posts can (and are) eliminating these barriers to respond to market opportunities."
Business Intelligence (BI) enables businesses to make fact-based decisions by aggregating data from various sources, enriching it with context and analysis, and presenting it in reports, dashboards and other formats. BI is becoming increasingly important, ranking as a top 5 priority for businesses. Emerging trends in BI include mobile access, cloud deployment, advanced analytics like predictive modeling, and leveraging social and unstructured data sources. The future of BI will focus more on real-time insights and event-driven analysis to anticipate outcomes.
The document provides an overview of business intelligence (BI) including definitions, typical architectures, and key concepts. It describes how data is extracted from operational systems via ETL processes and loaded into data warehouses to support OLAP and business analytics. Different data modeling approaches are covered, including star schemas, snowflake schemas, and fact constellations. Dimensional modeling techniques are outlined to transform enterprise data models into structures optimized for analysis and reporting.
Cloud expo the new normal for data centersEric Tachibana
The document summarizes the challenges facing data centers as the industry evolves from a focus on availability to one focused on agility. It notes that traditional management approaches may no longer be effective and that the supply and demand curve for data center resources will flip. Regulations are also tightening. The second half introduces Smart Technology Solutions and describes their services related to cloud adoption, infrastructure management, information security, and managing organizational change for technology transformations.
Transaction processing systems (TPS) are computerized systems that perform and record routine daily business transactions. There are two types of TPS: batch processing, which accumulates transactions and processes them in batches, and online transaction processing (OLTP), which processes each transaction immediately. TPS aim to ensure data integrity, produce timely documents, and increase efficiency. The processing cycle of a TPS involves data entry, database maintenance, document/report generation, and inquiry processing to keep business databases up-to-date and provide operational reports.
We architect and build Financial Applications - Advisory, Trading, Wealth Management. We provide SAAS services, host on Amazon, Rackspace, Azure and provide24x7 Application/IT support.
Use latest technology to solve Traditional Supply Chain Problems - a brainsto...Vinodh Soundarajan
This document discusses how technology is transforming supply chain management. It notes that the right information needs to get into the right hands at the right time by processing data from various sources like suppliers, inventory, and demand signals. It also outlines some key technology trends like mobile workforce, cloud computing, and multi-channel operations. Specific technologies that could help include streaming analytics, data visualization dashboards, cloud infrastructure, and UI frameworks. Potential supply chain problems are also listed that these technologies may help address, such as collaborative planning, logistics tracking, and inventory optimization.
Removing barriers to innovation by Mark Ryder, Prime Visionprimevision
Post-Expo Asia Presentation
The postal world met in Hong Kong last week at the inaugural Post-Expo Asia Pacific conference and Prime Vision’s Mark Ryder contributed on the themes of removing barriers to innovation and introduction of new technologies.
Mark adds ”The postal market is becoming more exciting than ever, and in some ways more fragmented away from the traditional universal service. Posts are coming up with some very innovative ways to maximise the benefits of their USPs in new ways, whether at the doorstep or behind the scenes services. Meanwhile consumers of postal services also expect great things from these trusted “mega-brands”, meaning that the relevance of the postal operator in the public space is assured. The key is whether individual posts respond to this chaotic and opportunistic environment with technology and services that deliver the required innovations. There can be some major barriers to progress in this area and I explored ways that posts can (and are) eliminating these barriers to respond to market opportunities."
Business Intelligence (BI) enables businesses to make fact-based decisions by aggregating data from various sources, enriching it with context and analysis, and presenting it in reports, dashboards and other formats. BI is becoming increasingly important, ranking as a top 5 priority for businesses. Emerging trends in BI include mobile access, cloud deployment, advanced analytics like predictive modeling, and leveraging social and unstructured data sources. The future of BI will focus more on real-time insights and event-driven analysis to anticipate outcomes.
The document provides an overview of business intelligence (BI) including definitions, typical architectures, and key concepts. It describes how data is extracted from operational systems via ETL processes and loaded into data warehouses to support OLAP and business analytics. Different data modeling approaches are covered, including star schemas, snowflake schemas, and fact constellations. Dimensional modeling techniques are outlined to transform enterprise data models into structures optimized for analysis and reporting.
Cloud expo the new normal for data centersEric Tachibana
The document summarizes the challenges facing data centers as the industry evolves from a focus on availability to one focused on agility. It notes that traditional management approaches may no longer be effective and that the supply and demand curve for data center resources will flip. Regulations are also tightening. The second half introduces Smart Technology Solutions and describes their services related to cloud adoption, infrastructure management, information security, and managing organizational change for technology transformations.
Transaction processing systems (TPS) are computerized systems that perform and record routine daily business transactions. There are two types of TPS: batch processing, which accumulates transactions and processes them in batches, and online transaction processing (OLTP), which processes each transaction immediately. TPS aim to ensure data integrity, produce timely documents, and increase efficiency. The processing cycle of a TPS involves data entry, database maintenance, document/report generation, and inquiry processing to keep business databases up-to-date and provide operational reports.
The document discusses the evolution of business intelligence from traditional to real-time and closed-loop systems. It describes moving from batch-based transactional data warehousing to analytical applications with real-time feeds and embedded business intelligence, enabled by technologies like BPM, EAI and decision rules. This creates a more intelligent and flexible solution.
IntelliMagic is a software development company that creates storage performance monitoring software for large datacenters. They started 10 years ago with 2 owners and have since grown to 38 employees through organic growth while maintaining profitability. Their software provides consolidated views and analytics of disk storage systems to help large companies with critical IT operations better manage their petabytes of data storage. Their goal is to continue innovating their software while providing a stable and enjoyable work environment for their employees.
Data marts are collections of subject areas organized for decision support based on the needs of a specific department like marketing, sales, or HR. They are created to provide easy access to frequent data, improve end user response times, and be less costly than a single large data warehouse. There are three types of data marts: dependent marts which draw from an existing data warehouse, independent marts which draw directly from operational or external sources, and hybrid marts which can take data from warehouses or operational systems. Data marts are advantageous as they are simpler, more focused, flexible, and lower cost to build than a single large warehouse.
Chit Fund software is simple and easy to understand. A person having basic computer knowledge can master this software in very less Time. As a whole this is efficient for all organization those organize chits. This software is designed to systemize the work of the chit that is being organized by the person who conducts that chit. More http://chitfundsoftware.in
http://mychitfundfeatures.blogspot.in/
http://mychitbusiness.blogspot.in/
http://mychitfund.blogspot.in/
http://mychitcompanies.blogspot.in/
The document discusses how data analytics and financial technology (fintech) companies are revolutionizing the banking industry. It provides definitions of key terms like fintech and describes how fintech companies exploit inherent risks in banking through data-driven lending. Several use cases of data analytics in banking are outlined, along with some of the risks to traditional banks from these new competitors. Techniques of data science that can be applied in banking are listed. The document aims to outline how data analytics is transforming financial services.
Retailinstruments solution for SHOPPING MALLS Jelisei Lokotar
RetailInstruments provides a decision support system using consumer behavior analytics collected from WiFi signals in stores and shopping malls to analyze metrics like foot traffic, capture rates, loyalty, dwell times, and cross-shopping between stores; this data is captured anonymously and aggregated to identify patterns and optimize aspects like tenant mix, marketing campaigns, and store layouts to increase revenues and customer engagement. The system collects WiFi MAC addresses and signal strengths using routers installed in stores to analyze consumer behavior.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
The document discusses the role and importance of information technology in the retail sector. It notes that IT plays a key role in managing complex retail operations and provides competitive advantages through market knowledge and data control. Specifically, IT helps retailers respond quickly to markets, analyze customer data, work across stores/borders efficiently, and speed up processes to reduce costs. However, retailers face challenges in managing large amounts of customer data, ensuring transparency across systems, and synchronizing global data in real-time. The document also defines point-of-sale systems and describes their various hardware and software components that are used to automate retail transactions.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with and for providing high customer satisfaction through its interactive and performant platform.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with and for providing high customer satisfaction through its interactive and performant platform.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with and for providing high customer satisfaction through its interactive and performant platform.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with minimal IT help and for consistently high customer satisfaction and value.
The company was established in 1988 under strategic partnership with TAL Apparel Ltd and has grown to include over 30 department stores, 1,000 specialty retailers, and 15 e-commerce retailers. To address challenges around adding more customer value, accessibility, and improving operations, the company decided to change its business model and IT environment by moving to a single-sourced, multi-platform integration tool called EXTOL Business Integrator to save time, increase efficiency and improve visibility across its business.
The document discusses strategies for expanding a hosting business through mergers and acquisitions (M&A). It outlines typical M&A structures like consolidating deals that integrate a seller's customers onto the buyer's infrastructure or platform deals that allow a seller to remain largely independent. Price multiples for private market deals in hosting range from 3-5x EBITDA for smaller deals to 5-10x EBITDA for larger deals, with premiums for growth, market position, and key assets. The process involves finding prospects, letters of intent, due diligence, legal documentation, and post-closing integration. Common issues include verifying capabilities and customer stability, and setting realistic goals.
DDMA 14 mei 2009 Business Intelligence case Ahold DDMA
Ahold zette een infrastructuur op die zes miljoen informatievragen uit negentig verschillende informatiebronnen in een tijdsbestek van één jaar afhandelt. De jury noemt de adoptie van BI binnen Ahold indrukwekkend: “Bij Ahold wordt stuurinformatie gebruikt op alle managementlagen. Een mooi voorbeeld zijn de winkelmanagers die iedere ochtend kijken hoe het ervoor staat.”
Big Data vs. Big Risk: Real-Time Trade Surveillance in Financial MarketsArcadia Data
Who’s winning the deep forensic analysis ‘arms race’ for compliance?
Real-time trade surveillance in global financial markets has created a data tsunami.
With greater volumes of data comes greater compliance risk. CNBC reports U.S. Banks have been fined over $200B since the financial crisis. How are compliance teams fighting back to make more of the data and stay out of regulatory hot water?
Rapid response to suspect trades means compliance teams need to access and visualize trade patterns, real time and historic data, to navigate the data in depth and flag possible violations.
Join Hortonworks and Arcadia for this live webinar: we’ll cover the use case at a top 50 Global Bank who now has deep forensic analysis of trade activity. The result: interactive, ad hoc data visualization and access across multiple platforms – without limits on historic data – to detect irregularities as they happen.
- RELEX provides supply chain management and retail planning solutions, including forecasting, replenishment, inventory management, and analytics. [SENTENCE 1]
- It was founded in 2006 by supply chain researchers and has grown to over 200 employees with offices worldwide, serving over 120 customers in 18 countries. [SENTENCE 2]
- RELEX uses an in-memory database for its solutions, providing much faster calculation performance than traditional disk-based systems, and has become a market leader in supply chain management software. [SENTENCE 3]
Financial services companies are undergoing a revolution driven by data analytics and new technologies. Non-traditional fintech companies have an advantage over traditional banks by exploiting inherent risks in banking like weak growth and low interest rates. Data-driven lending provides benefits like more customized products and services based on analyzing customer spending patterns and other data. Traditional banks will need to respond by adopting these new data and analytical approaches to remain competitive.
This document discusses sales systems from a global and local perspective. It begins with an introduction to the evolution of sales systems from pen and paper to modern CRM and business intelligence tools. It then covers the framework for how sales data is handled within organizations. The benefits of global and local approaches to sales systems are analyzed. Globally, there are benefits like an integrated view of business and leveraging investments across markets. Locally, systems can be faster to implement and tailored to unique needs. However, risks include lack of support globally and overkill for smaller markets locally. The document seeks to establish an understanding of data lifecycles and determine the appropriate level at which to collect sales data.
This document provides an overview of data warehousing and related concepts. It begins with definitions of key terms like data warehousing, data marts, and OLAP. It then covers the history and evolution of data warehousing in organizations. The document outlines the typical architecture of a data warehouse, including sources, integration, and metadata. It discusses benefits like providing a customer-centric view and removing barriers between functions. It also notes some disadvantages like latency and maintenance costs. Finally, it briefly touches on strategic uses, data mining, and text mining.
The document discusses the evolution of business intelligence from traditional to real-time and closed-loop systems. It describes moving from batch-based transactional data warehousing to analytical applications with real-time feeds and embedded business intelligence, enabled by technologies like BPM, EAI and decision rules. This creates a more intelligent and flexible solution.
IntelliMagic is a software development company that creates storage performance monitoring software for large datacenters. They started 10 years ago with 2 owners and have since grown to 38 employees through organic growth while maintaining profitability. Their software provides consolidated views and analytics of disk storage systems to help large companies with critical IT operations better manage their petabytes of data storage. Their goal is to continue innovating their software while providing a stable and enjoyable work environment for their employees.
Data marts are collections of subject areas organized for decision support based on the needs of a specific department like marketing, sales, or HR. They are created to provide easy access to frequent data, improve end user response times, and be less costly than a single large data warehouse. There are three types of data marts: dependent marts which draw from an existing data warehouse, independent marts which draw directly from operational or external sources, and hybrid marts which can take data from warehouses or operational systems. Data marts are advantageous as they are simpler, more focused, flexible, and lower cost to build than a single large warehouse.
Chit Fund software is simple and easy to understand. A person having basic computer knowledge can master this software in very less Time. As a whole this is efficient for all organization those organize chits. This software is designed to systemize the work of the chit that is being organized by the person who conducts that chit. More http://chitfundsoftware.in
http://mychitfundfeatures.blogspot.in/
http://mychitbusiness.blogspot.in/
http://mychitfund.blogspot.in/
http://mychitcompanies.blogspot.in/
The document discusses how data analytics and financial technology (fintech) companies are revolutionizing the banking industry. It provides definitions of key terms like fintech and describes how fintech companies exploit inherent risks in banking through data-driven lending. Several use cases of data analytics in banking are outlined, along with some of the risks to traditional banks from these new competitors. Techniques of data science that can be applied in banking are listed. The document aims to outline how data analytics is transforming financial services.
Retailinstruments solution for SHOPPING MALLS Jelisei Lokotar
RetailInstruments provides a decision support system using consumer behavior analytics collected from WiFi signals in stores and shopping malls to analyze metrics like foot traffic, capture rates, loyalty, dwell times, and cross-shopping between stores; this data is captured anonymously and aggregated to identify patterns and optimize aspects like tenant mix, marketing campaigns, and store layouts to increase revenues and customer engagement. The system collects WiFi MAC addresses and signal strengths using routers installed in stores to analyze consumer behavior.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
The document discusses the role and importance of information technology in the retail sector. It notes that IT plays a key role in managing complex retail operations and provides competitive advantages through market knowledge and data control. Specifically, IT helps retailers respond quickly to markets, analyze customer data, work across stores/borders efficiently, and speed up processes to reduce costs. However, retailers face challenges in managing large amounts of customer data, ensuring transparency across systems, and synchronizing global data in real-time. The document also defines point-of-sale systems and describes their various hardware and software components that are used to automate retail transactions.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with and for providing high customer satisfaction through its interactive and performant platform.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with and for providing high customer satisfaction through its interactive and performant platform.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with and for providing high customer satisfaction through its interactive and performant platform.
- QlikTech is a business intelligence company founded in 1993 that produces the QlikView software. It has over 10,500 customers in 92 countries and 700+ partners.
- QlikView is a business intelligence tool that offers dashboards, analysis, and reporting in one program. It allows for fast deployment and low costs compared to traditional BI solutions.
- QlikView has received accolades for being easy for business users to build solutions with minimal IT help and for consistently high customer satisfaction and value.
The company was established in 1988 under strategic partnership with TAL Apparel Ltd and has grown to include over 30 department stores, 1,000 specialty retailers, and 15 e-commerce retailers. To address challenges around adding more customer value, accessibility, and improving operations, the company decided to change its business model and IT environment by moving to a single-sourced, multi-platform integration tool called EXTOL Business Integrator to save time, increase efficiency and improve visibility across its business.
The document discusses strategies for expanding a hosting business through mergers and acquisitions (M&A). It outlines typical M&A structures like consolidating deals that integrate a seller's customers onto the buyer's infrastructure or platform deals that allow a seller to remain largely independent. Price multiples for private market deals in hosting range from 3-5x EBITDA for smaller deals to 5-10x EBITDA for larger deals, with premiums for growth, market position, and key assets. The process involves finding prospects, letters of intent, due diligence, legal documentation, and post-closing integration. Common issues include verifying capabilities and customer stability, and setting realistic goals.
DDMA 14 mei 2009 Business Intelligence case Ahold DDMA
Ahold zette een infrastructuur op die zes miljoen informatievragen uit negentig verschillende informatiebronnen in een tijdsbestek van één jaar afhandelt. De jury noemt de adoptie van BI binnen Ahold indrukwekkend: “Bij Ahold wordt stuurinformatie gebruikt op alle managementlagen. Een mooi voorbeeld zijn de winkelmanagers die iedere ochtend kijken hoe het ervoor staat.”
Big Data vs. Big Risk: Real-Time Trade Surveillance in Financial MarketsArcadia Data
Who’s winning the deep forensic analysis ‘arms race’ for compliance?
Real-time trade surveillance in global financial markets has created a data tsunami.
With greater volumes of data comes greater compliance risk. CNBC reports U.S. Banks have been fined over $200B since the financial crisis. How are compliance teams fighting back to make more of the data and stay out of regulatory hot water?
Rapid response to suspect trades means compliance teams need to access and visualize trade patterns, real time and historic data, to navigate the data in depth and flag possible violations.
Join Hortonworks and Arcadia for this live webinar: we’ll cover the use case at a top 50 Global Bank who now has deep forensic analysis of trade activity. The result: interactive, ad hoc data visualization and access across multiple platforms – without limits on historic data – to detect irregularities as they happen.
- RELEX provides supply chain management and retail planning solutions, including forecasting, replenishment, inventory management, and analytics. [SENTENCE 1]
- It was founded in 2006 by supply chain researchers and has grown to over 200 employees with offices worldwide, serving over 120 customers in 18 countries. [SENTENCE 2]
- RELEX uses an in-memory database for its solutions, providing much faster calculation performance than traditional disk-based systems, and has become a market leader in supply chain management software. [SENTENCE 3]
Financial services companies are undergoing a revolution driven by data analytics and new technologies. Non-traditional fintech companies have an advantage over traditional banks by exploiting inherent risks in banking like weak growth and low interest rates. Data-driven lending provides benefits like more customized products and services based on analyzing customer spending patterns and other data. Traditional banks will need to respond by adopting these new data and analytical approaches to remain competitive.
This document discusses sales systems from a global and local perspective. It begins with an introduction to the evolution of sales systems from pen and paper to modern CRM and business intelligence tools. It then covers the framework for how sales data is handled within organizations. The benefits of global and local approaches to sales systems are analyzed. Globally, there are benefits like an integrated view of business and leveraging investments across markets. Locally, systems can be faster to implement and tailored to unique needs. However, risks include lack of support globally and overkill for smaller markets locally. The document seeks to establish an understanding of data lifecycles and determine the appropriate level at which to collect sales data.
This document provides an overview of data warehousing and related concepts. It begins with definitions of key terms like data warehousing, data marts, and OLAP. It then covers the history and evolution of data warehousing in organizations. The document outlines the typical architecture of a data warehouse, including sources, integration, and metadata. It discusses benefits like providing a customer-centric view and removing barriers between functions. It also notes some disadvantages like latency and maintenance costs. Finally, it briefly touches on strategic uses, data mining, and text mining.
Traditional Data-warehousing / BI overviewNagaraj Yerram
Business intelligence (BI) refers to technologies that collect, analyze, and present business data to support decision-making. A traditional BI architecture extracts data from source systems, transforms it using ETL processes, and loads it into a data warehouse optimized for analysis (OLAP). Dimensional modeling techniques structure data warehouses into fact and dimension tables arranged in star or snowflake schemas to enable analysis of key business metrics over time and across different dimensions like product or location. This facilitates interactive exploration and reporting on historical, current, and predictive business insights for strategic planning and opportunities.
This document provides an agenda and overview for a data warehousing training session. The agenda covers topics such as data warehouse introductions, reviewing relational database management systems and SQL commands, and includes a case study discussion with Q&A. Background information is also provided on the project manager leading the training.
A data warehouse is a centralized database used for reporting and data analysis. It integrates data from multiple sources and stores current and historical data to assist management decision making. A data warehouse transforms data into timely information. It allows users to access specific types of data relevant to their needs through smaller data marts. While data warehouses provide benefits like increased access, consistency and productivity, they also present challenges such as lengthy data loads and compatibility issues.
Data warehousing involves collecting data from different sources and organizing it in a way that allows for analysis to make business decisions. It provides a single, complete view of data that end users can easily understand. A data warehouse stores integrated data from multiple sources and provides historical views of data to support analysis. It allows organizations to access critical information to support reporting, queries and decision making. Common applications of data warehousing include banking, healthcare, airlines and telecommunications.
Data warehousing and online analytical processing (OLAP) allow organizations to consolidate data from multiple sources and analyze it to answer business questions. A data warehouse stores integrated and subject-oriented data to support organizational decision making. OLAP transforms the data into meaningful information through operations like roll-ups, drills downs, slicing and dicing to analyze data. Data mining then identifies patterns and relationships in the warehoused data to provide intelligence for organizations.
Data warehousing and online analytical processing (OLAP) allow organizations to consolidate data from multiple sources and analyze it to answer business questions. A data warehouse stores integrated and subject-oriented data to support organizational decision making. OLAP transforms the data into meaningful information through operations like roll-ups, drills downs, slicing and dicing to enable interactive analysis. Data mining then identifies patterns and relationships in the warehoused data to provide intelligence for businesses.
Data warehousing and online analytical processing (OLAP) allow organizations to consolidate data from multiple sources and analyze it to answer business questions. A data warehouse stores integrated and subject-oriented data to support organizational decision making. OLAP transforms the data into meaningful information through operations like roll-ups, drills downs, slicing and dicing to enable interactive analysis. Data mining then identifies patterns and relationships in the warehoused data to provide intelligence for organizations.
This document discusses using data warehouses in retail and finance. It provides examples of how data warehouses are used in both industries, including for market basket analysis, product placement, supply chain management, and customer profiling. It also outlines some opportunities and challenges of implementing data warehouses, such as improved sales and customer loyalty but also large data volumes and data preparation difficulties. Specific company examples are given, like how Netflix uses customer streaming data and how Raymond James improved data backups and reporting with a new solution.
Presentation data warehouse easy and simple words.pptxshamsbhai495
The document discusses data warehouses. It defines a data warehouse as a type of data management system designed to support business intelligence activities like analytics. A data warehouse contains large amounts of historical data from different sources organized around subjects like customers and products. The data is integrated, time-variant, and non-volatile. Maintaining historical data enables tracking improvements over time and gaining insights essential for driving business decisions. Typical queries in a data warehouse access aggregated and historical data across multiple tables rather than transactional data from a single table as in OLTP systems.
Business intelligence uses data analysis processes to transform data into useful information that helps business users make better decisions. It involves gathering, storing, and analyzing data from various sources like a data warehouse. Data mining is the extraction of hidden predictive patterns from large datasets and is used along with tools like data warehouses and data analysis to help companies understand customer behavior and make strategic business decisions. Data warehouses store historical data from across an organization for analysis and security, and the analysis of this data can help identify trends, focus on decision making, and increase consistency.
Business Intelligence Data Warehouse SystemKiran kumar
This document provides an overview of data warehousing and business intelligence concepts. It discusses:
- What a data warehouse is and its key properties like being integrated, non-volatile, time-variant and subject-oriented.
- Common data warehouse architectures including dimensional modeling, ETL processes, and different layers like the data storage layer and presentation layer.
- How data marts are subsets of the data warehouse that focus on specific business functions or departments.
- Different types of dimensions tables and slowly changing dimensions.
- How business intelligence uses the data warehouse for analysis, querying, reporting and generating insights to help with decision making.
Gulabs Ppt On Data Warehousing And Mininggulab sharma
The document provides an overview of data warehousing, decision support, and OLAP. It discusses how a data warehouse can integrate data from various operational sources to provide a single point of access for analysis. It also compares the differences between operational databases designed for transactions versus data warehouses designed for analytics and decision making. Key points covered include data extraction, transformation and loading into the warehouse, as well as refresh strategies to propagate changes from source systems.
This document discusses the importance of data warehousing for multinational corporations (MNCs). It notes that MNCs often struggle to find, understand, and use the data they need because data is scattered across networks in many different versions and formats. A data warehouse integrates data from various sources into a single consistent store that is easy for end users to access and understand. It allows historical data analysis and "what if" scenario planning to help answer business questions and make better decisions. The document outlines key components and architectures of data warehouses and how they can provide summarized data to different departments through data marts.
This document provides an overview of a course on data warehousing and data mining. It discusses what a data warehouse is, how it differs from operational databases, and how it can be used for decision support and online analytical processing (OLAP). It also discusses data mining and how it can extract useful patterns and intelligence from large datasets. Examples are provided of how data warehousing and mining are used in various industries. The document outlines the course topics which will include data warehousing architecture, loading, modeling, and query processing, as well as decision support, OLAP, and data mining.
This document provides an overview of a course on data warehousing and data mining. It discusses key concepts like data warehousing, online analytical processing (OLAP), decision support systems, operational databases, and data mining. It explains how data warehousing involves extracting, cleaning and consolidating data from multiple sources to create a centralized decision support database. It also describes how data mining can extract useful patterns and intelligence from this warehoused data.
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.
This document provides an overview of a course on data warehousing, data mining, and decision support. It discusses what data warehousing is, how it differs from operational transaction processing systems, and the processes involved like data extraction, transformation, loading and refreshing the warehouse. It also covers warehouse architecture, design considerations, and multidimensional data modeling. Examples from Walmart's data warehouse implementation are provided to illustrate real-world warehouse concepts and capabilities.
House Price Prediction An AI Approach.Nahian Ahmed
Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history.
And we make this prediction using Machine Learning.
Home Automation :
Home automation is the automation process of home appliances and other home functions so that we can be controlled with your phone, computer, or even remotely.
Which are based on various microcontrollers, arduino, raspberry pi, etc.
The document presents on VLSM and Supernetting. It contains introductions to VLSM and Supernetting, their histories, basic concepts, implementation processes and examples. VLSM allows variable length subnet masking to efficiently divide a network into subnets of different sizes. Supernetting combines multiple networks or subnets into a larger single network to reduce routing table sizes. The document provides step-by-step explanations of VLSM and Supernetting techniques along with illustrative examples.
Android is a mobile operating system developed by Google, based on the Linux kernel and designed primarily for touchscreen mobile devices such as smartphones and tablets. Android's user interface is mainly based on direct manipulation, using touch gestures that loosely correspond to real-world actions, such as swiping, tapping and pinching, to manipulate on-screen objects, along with a virtual keyboard for text input. In addition to touchscreen devices, Google has further developed Android TV for televisions, Android Auto for cars, and Android Wear for wrist watches, each with a specialized user interface. Variants of Android are also used on notebooks, game consoles, digital cameras, and other electronics.
Presentation on DNA Sequencing ProcessNahian Ahmed
The document summarizes a presentation on bioinformatics and DNA sequencing. It includes 5 group members who each discuss an aspect of DNA sequencing. It describes how DNA stores genetic information and its structure. It then explains the history of DNA discovery and different sequencing methods, including the Sanger method. Modern applications of sequencing in forensics, medicine, and agriculture are outlined. The human genome project is summarized as a large international effort to sequence the entire human genome.
Delta modulation is an analog-to-digital conversion technique used to transfer data. It works by comparing an input signal to a reference signal and encoding the difference into a digital bitstream. A delta modulation system consists of a modulator that converts an analog signal to digital, and a demodulator that converts the digital signal back to analog. Delta modulation is simpler than pulse code modulation but can achieve high signal-to-noise ratios and variable bandwidth. However, it is limited by slope overload when signals change rapidly.
The document provides information on the 8086 microprocessor, including:
- It was designed by Intel in the late 1970s and was used in early PCs.
- It has a 16-bit architecture and 20-bit address bus, allowing access to 1MB of memory.
- The 8086 CPU logic is partitioned into a Bus Interface Unit and Execution Unit, with the BIU handling bus operations and the EU executing instructions.
- The BIU generates physical addresses from logical addresses using segment registers and the instruction pointer. It also contains an instruction queue and registers.
- The EU contains general purpose registers, flags, and an ALU for arithmetic and logical operations.
Nahian Ahmed, student ID 151-15-5137 from section G, will present on the application of numerical methods in computer science. The presentation will discuss curve fitting, which is a widely used analysis tool for examining relationships between predictors. Curve fitting can be used in MS Excel to generate curves and equations like y=ax+b that fit the provided data. It can also determine the best fit line for a data set. The presentation will cover Nahian's 4th semester project on this topic.
The document summarizes a presentation on exception handling given by the group "Bug Free". It defines what exceptions are, why they occur, and the exception hierarchy. It describes checked and unchecked exceptions, and exception handling terms like try, catch, throw, and finally. It provides examples of using try-catch blocks, multiple catch statements, nested try-catch, and throwing and handling exceptions.
This presentation summarizes different types of flip flops used in digital circuits. It is presented by a group called Bug Free and includes 4 members. The presentation defines a flip flop as an electronic circuit with two stable states that can serve as one bit of memory. It then describes 5 main types of flip flops - SR, Clocked SR, JK, T, and D flip flops. Examples of each type of flip flop are shown using logic gates. Applications of flip flops mentioned include memory circuits, logic control devices, counters, and registers. A master-slave edge-triggered flip flop is also summarized.
Game Architect is a career that the presenter has chosen to research because they find making games to be fun. They want to learn about career opportunities in the field and how to design successful games. Their plan is to complete their education, strengthen their programming and problem-solving skills, develop game ideas, and work on research projects. The presentation concludes that a career in game architecture can be both financially rewarding and personally enjoyable.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
2. A producer wants to know….
Which are our
lowest/highest margin
customers ?
Who are my customers
and what products
are they buying?
What is the most
effective distribution
channel?
What product prom-
-otions have the biggest
impact on revenue? What impact will
new products/services
have on revenue
and margins?
Which customers
are most likely to go
to the competition ?
3. What is a Data Warehouse??
• A data warehouse is an appliance for storing and analyzing data, and reporting.
• Central database that includes information from several different sources.
• Used to produce reports to
assist in decision-making
and management.
4. What is Data Warehousing?
A process of transforming data
into information and making it
available to users in a timely
enough manner to make a
difference
Data
Information
5. “Data Warehouse is a subject
oriented, integrated, time-
variant and non-volatile
collection of data in support of
management’s decision making
process.” – W. H. Inmon
Data
Warehouse
Subject
Oriented
Integrated
Time
Variant
Non-
volatile
6. Data Processing Technologies
• OLTP (on-line transaction processing)
- The major task is to perform on-line
transaction and query processing. Covers
most of the day-to-day operations of an
organization.
• OLAP(On-Line Analytical Processing)
- Serve knowledge workers(users) in the
role of data analysis and decision making.
- Organize and present data in various
formats to accommodate the diverse needs
of the different users.
Data Processing
Technologies
OLTP OLAP
7. To summarize ...
OLTP Systems are
used to “run” a business
The Data Warehouse helps
to “optimize” the business