Datawarehouse Overview
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The document discusses advances in database querying and summarizes key topics including data warehousing, online analytical processing (OLAP), and data mining. It describes how data warehouses integrate data from various sources to enable decision making, and how OLAP tools allow users to analyze aggregated data and model "what-if" scenarios. The document also covers data transformation techniques used to build the data warehouse.
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Datawarehousing
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.
Dataware housing
The document provides an overview of data warehousing and data mining. It discusses what a data warehouse is, how it is structured, and how it can help organizations make better decisions by integrating data from multiple sources and facilitating online analytical processing (OLAP). It also covers key components of a data warehousing architecture like the data manager, data acquisition, metadata repository, and middleware that connect the data warehouse to operational databases and analytical tools.
ITReady DW Day2
The document outlines the agenda for a data warehousing training course. The agenda covers topics such as data warehouse structure and modeling, extract transform load (ETL) processes, dimensional modeling, aggregation, online analytical processing (OLAP), and data marts. Time is allocated to discuss loading, refreshing, and querying the data warehouse.
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The document provides an overview of data warehousing and discusses key concepts such as:
1) Data warehouses address the problem of data being scattered across different sources by creating a single, consistent store of data from various sources for analysis.
2) Dimensional modeling techniques like star schemas and snowflake schemas are used to structure data warehouses for analysis.
3) Data transformation is required to integrate data from different sources and achieve a single version of truth.
Refreshing and querying the data warehouse are also discussed.
IT Ready - DW: 1st Day
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.
Data Warehouse
The document discusses decision support, data warehousing, and online analytical processing (OLAP). It outlines the evolution of decision support from batch reporting in the 1960s to modern data warehousing with OLAP engines. Key aspects covered include the differences between OLTP and OLAP systems, data warehouse architecture including star schemas, and approaches to OLAP including relational and multidimensional servers.
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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.
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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.
Datawarehouse & bi introduction
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.
Datawarehouse & bi introduction
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.
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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.
Data warehouse
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.
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The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
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The document provides an overview of key data warehousing concepts. It defines a data warehouse as a single, consistent store of data obtained from various sources and made available to users in a format they can understand for business decision making. The document outlines some common questions end users may have that a data warehouse can help answer. It also discusses the differences between online transaction processing (OLTP) systems and data warehouses, including that data warehouses integrate historical data from various sources and are optimized for analysis rather than transactions.
Overview of business intelligence
Why BI ?
Performance management
Identify trends
Cash flow trend
Fine-tune operations
Sales pipeline analysis
Future projections
business Forecasting
Decision Making Tools
Convert data into information
How to Think ?
What happened?
What is happening?
Why did it happen?
What will happen?
What do I want to happen?
Introduction To Msbi By Yasir
The document discusses data warehousing and how it helps businesses make informed decisions by integrating data from various sources into a centralized repository. A data warehouse allows businesses to analyze historical data to understand trends, identify high/low performing customers, determine which products or promotions are most effective, and more. It provides a single, complete view of business data that users can easily access and understand to gain insights.
Become BI Architect with 1KEY Agile BI Suite - OLAP
Business intelligence uses applications and technologies to analyze data and help users make better business decisions. Online transaction processing (OLTP) is used for daily operations like processing, while online analytical processing (OLAP) is used for data analysis and decision making. Data warehouses integrate data from different sources to provide a centralized system for analysis and reporting. Dimensional modeling approaches like star schemas and snowflake schemas organize data to support OLAP.
3dw
The document discusses data warehousing and OLAP technology for data mining. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data that supports management decision making. It describes how data warehouses use a multi-dimensional data model with dimensions and facts to organize data into cubes that can be sliced, diced, and aggregated. It also discusses how data warehouse architecture, implementation, indexing techniques, and metadata repositories help optimize online analytical processing queries on historical and summarized data to support data mining.
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Datawarehouse Overview
- 1. Advances in Database Querying Satish Bobba Sr.Informatica Developer [email_address]
- 2. Acknowledgments Pankaj Gangshettiwar Anit Jha CGI AMS
- 4. Part 1: Data Warehouses
- 7. Why Data Warehousing? Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel?
- 14. Data Warehouse Architecture Relational Databases Legacy Data Purchased Data Data Warehouse Engine Optimized Loader Extraction Cleansing Analyze Query Metadata Repository
- 15. From the Data Warehouse to Data Marts Departmentally Structured Individually Structured Data Warehouse Organizationally Structured Less More History Normalized Detailed Data Information
- 16. Users have different views of Data Organizationally structured OLAP Explorers: Seek out the unknown and previously unsuspected rewards hiding in the detailed data Farmers: Harvest information from known access paths Tourists: Browse information harvested by farmers
- 29. Levels of Granularity Operational 60 days of activity account activity date amount teller location account bal account month # trans withdrawals deposits average bal amount activity date amount account bal monthly account register -- up to 10 years Not all fields need be archived Banking Example
- 30. Data Integration Across Sources Trust Credit card Savings Loans Same data different name Different data Same name Data found here nowhere else Different keys same data
- 32. Data Transformation Example encoding unit field appl A - balance appl B - bal appl C - currbal appl D - balcurr appl A - pipeline - cm appl B - pipeline - in appl C - pipeline - feet appl D - pipeline - yds appl A - m,f appl B - 1,0 appl C - x,y appl D - male, female Data Warehouse
- 43. Operational data Detailed transactional data Data warehouse Merge Clean Summarize Direct Query Reporting tools Mining tools Decision support tools Oracle SAS Relational DBMS+ e.g. Redbrick IMS Crystal reports Essbase Intelligent Miner Bombay branch Delhi branch Calcutta branch Census data OLAP GIS data
- 48. Part 2: OLAP
- 57. OLAP: 3 Tier DSS Data Warehouse Database Layer Store atomic data in industry standard Data Warehouse. OLAP Engine Application Logic Layer Generate SQL execution plans in the OLAP engine to obtain OLAP functionality. Decision Support Client Presentation Layer Obtain multi-dimensional reports from the DSS Client.
- 62. Part 3: Data Mining
- 78. Application Areas Industry Application Finance Credit Card Analysis Insurance Claims, Fraud Analysis Telecommunication Call record analysis Transport Logistics management Consumer goods promotion analysis Data Service providers Value added data Utilities Power usage analysis
- 86. Part 4: Speeding up Query Processing