IST722 Data Warehousing



Similar documents
Part 22. Data Warehousing

BUILDING BLOCKS OF DATAWAREHOUSE. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA

SAS BI Course Content; Introduction to DWH / BI Concepts

Bussiness Intelligence and Data Warehouse. Tomas Bartos CIS 764, Kansas State University

14. Data Warehousing & Data Mining

Data Warehousing Systems: Foundations and Architectures

Turkish Journal of Engineering, Science and Technology

Data Warehouse Overview. Srini Rengarajan

Emerging Technologies Shaping the Future of Data Warehouses & Business Intelligence

Fluency With Information Technology CSE100/IMT100

DATA WAREHOUSING - OLAP

Lection 3-4 WAREHOUSING

Republic Polytechnic School of Information and Communications Technology C355 Business Intelligence. Module Curriculum

Data Warehouse: Introduction

DATA WAREHOUSING AND OLAP TECHNOLOGY

Data Warehousing Concepts

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing

Data Warehousing and Data Mining

Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006

BUSINESS ANALYTICS AND DATA VISUALIZATION. ITM-761 Business Intelligence ดร. สล ล บ ญพราหมณ

Data Warehousing. Read chapter 13 of Riguzzi et al Sistemi Informativi. Slides derived from those by Hector Garcia-Molina

Master Data Management and Data Warehousing. Zahra Mansoori

Copyright 2007 Ramez Elmasri and Shamkant B. Navathe. Slide 29-1

Meta-data and Data Mart solutions for better understanding for data and information in E-government Monitoring

Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach

Understanding Data Warehousing. [by Alex Kriegel]

Sterling Business Intelligence

OLAP & DATA MINING CS561-SPRING 2012 WPI, MOHAMED ELTABAKH

Trends in Data Warehouse Data Modeling: Data Vault and Anchor Modeling

Data Warehousing and Data Mining in Business Applications

A Service-oriented Architecture for Business Intelligence

DATA WAREHOUSE CONCEPTS DATA WAREHOUSE DEFINITIONS

DATA WAREHOUSING APPLICATIONS: AN ANALYTICAL TOOL FOR DECISION SUPPORT SYSTEM

Data Testing on Business Intelligence & Data Warehouse Projects

Week 3 lecture slides

CONCEPTUALIZING BUSINESS INTELLIGENCE ARCHITECTURE MOHAMMAD SHARIAT, Florida A&M University ROSCOE HIGHTOWER, JR., Florida A&M University

Introduction to Data Warehousing. Ms Swapnil Shrivastava

A Critical Review of Data Warehouse

Overview. DW Source Integration, Tools, and Architecture. End User Applications (EUA) EUA Concepts. DW Front End Tools. Source Integration

2074 : Designing and Implementing OLAP Solutions Using Microsoft SQL Server 2000

Business Intelligence Solutions. Cognos BI 8. by Adis Terzić

B.Sc (Computer Science) Database Management Systems UNIT-V

Data Warehousing. Overview, Terminology, and Research Issues. Joachim Hammer. Joachim Hammer

Ramesh Bhashyam Teradata Fellow Teradata Corporation

OLAP Theory-English version

Building Cubes and Analyzing Data using Oracle OLAP 11g

<Insert Picture Here> Enhancing the Performance and Analytic Content of the Data Warehouse Using Oracle OLAP Option

IT0457 Data Warehousing. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT

資 料 倉 儲 (Data Warehousing)

HYPERION MASTER DATA MANAGEMENT SOLUTIONS FOR IT

A Design and implementation of a data warehouse for research administration universities

Week 13: Data Warehousing. Warehousing

INTERACTIVE DECISION SUPPORT SYSTEM BASED ON ANALYSIS AND SYNTHESIS OF DATA - DATA WAREHOUSE

Providing real-time, built-in analytics with S/4HANA. Jürgen Thielemans, SAP Enterprise Architect SAP Belgium&Luxembourg

Data Warehousing, OLAP, and Data Mining

Data W a Ware r house house and and OLAP Week 5 1

B. 3 essay questions. Samples of potential questions are available in part IV. This list is not exhaustive it is just a sample.

Applied Business Intelligence. Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA

Technology-Driven Demand and e- Customer Relationship Management e-crm

Building a Data Warehouse

Business Intelligence: Effective Decision Making

Building an Effective Data Warehouse Architecture James Serra

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Lecture 2: Introduction to Business Intelligence. Introduction to Business Intelligence

SAS Business Intelligence Online Training

Master Data Management. Zahra Mansoori

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

European Archival Records and Knowledge Preservation Database Archiving in the E-ARK Project

Research on Airport Data Warehouse Architecture

Hybrid Support Systems: a Business Intelligence Approach

University of Gaziantep, Department of Business Administration

PREFACE INTRODUCTION MULTI-DIMENSIONAL MODEL. Chris Claterbos, Vlamis Software Solutions, Inc.

Data warehousing/dimensional modeling/ SAP BW 7.3 Concepts

70-467: Designing Business Intelligence Solutions with Microsoft SQL Server

Data Warehousing and OLAP Technology for Knowledge Discovery

Welcome To Today s Webinar: Dynamics Insights SM for Microsoft Dynamics AX

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES

Data Warehousing. Jens Teubner, TU Dortmund Winter 2015/16. Jens Teubner Data Warehousing Winter 2015/16 1

Published by: PIONEER RESEARCH & DEVELOPMENT GROUP ( 28

BENEFITS OF AUTOMATING DATA WAREHOUSING

A Comparative Study on Operational Database, Data Warehouse and Hadoop File System T.Jalaja 1, M.Shailaja 2

Designing a Dimensional Model

Data Warehousing. Outline. From OLTP to the Data Warehouse. Overview of data warehousing Dimensional Modeling Online Analytical Processing

SAP Business Objects BO BI 4.1

Migrating a Discoverer System to Oracle Business Intelligence Enterprise Edition

Unit -3. Learning Objective. Demand for Online analytical processing Major features and functions OLAP models and implementation considerations

Speeding ETL Processing in Data Warehouses White Paper

The Design and the Implementation of an HEALTH CARE STATISTICS DATA WAREHOUSE Dr. Sreèko Natek, assistant professor, Nova Vizija,

Business Intelligence for Everyone

Data Warehousing: A Technology Review and Update Vernon Hoffner, Ph.D., CCP EntreSoft Resouces, Inc.

<Insert Picture Here> Extending Hyperion BI with the Oracle BI Server

Transcription:

IST722 Data Warehousing Components of the Data Warehouse Michael A. Fudge, Jr.

Recall: Inmon s CIF The CIF is a reference architecture

Understanding the Diagram The CIF is a reference architecture

CIF Components

External World & Applications The CIF is a reference architecture

External World & Applications External World the people and systems that generate operational data. Applications the systems which provide the source for the operational data. Examples: ERP s, Business Applications, Internet data, external data streams. These are the inputs and data sources for the CIF. OLTP Systems Operational data, transactionoriented.

Integration & Transformation Layer The CIF is a reference architecture

Integration & Transformation Layer I&T layer takes un-integrated data from multiple sources and integrates and consolidates it. Computer programs are written to transform data from the external world into corporate data. The data come from a variety of sources and in both structured and un-structured formats. Today s Database Management Systems provide tooling to assist with this process. This is the most difficult and time-consuming component of the CIF. Two approaches: ETL and ELT

ETL Extract Transform Load The data transformation occurs over staged data. The source data is not stored in the warehouse.

ELT Extract Load Transform The data transformation occurs over warehoused data. The staged data is stored in the warehouse.

Operational Data Store The CIF is a reference architecture

Operational Data Store Integrated, detailed, and current data from the External World and Applications. Consolidated from disparate sources. Does not grow over time. Performs similarly to a transactional database. Structured differently than a data warehouse, and therefore should be stored as a separate database. Receives data from I&T layer sends data to the data warehouse. The data warehouse can populate it, too. Think of it as a consolidated operational database.

Enterprise Data Warehouse The CIF is a reference architecture

Enterprise Data Warehouse Subject-oriented, integrated, summarized, and current data from the External World and Applications. Optimized for query performance. Structured differently than operational data, typically in a dimensional model. Receives data from I&T layer and the ODS. Use as a source for data marts and decision support systems. Grows in size over time due to historical data. The heart of the CIF.

ODS vs. EDW Characteristic Operational Data Store Data Warehouse Primary Purpose Design Goal Primary Users Run the business on a current basis Performance throughput, availability Clerks, salespersons, administrators Subject Oriented Yes Yes Integrated Yes Yes Detailed Data Yes Yes Summary Data No Yes Support managerial decision making Easy reporting and analytics Managers, business analysis, customers Time of Data Current data Historical snapshots Updates Frequent small updates Periodic batch updates Queries Simple queries on a few rows Complex queries on several rows

Why No ODS in the EDW? I need fast updates! I need query performance! You can t have both!

Data Marts The CIF is a reference architecture

Data Marts A collection of data tailored to the informational needs of a department or business process. Easy to control, low cost, and customizable due to their limited scope. Receive their inputs from the Enterprise Data Warehouse. Are source data for Online Analytical Processing (OLAP) engines.

OLAP ROLAP Uses a Relational Database Management System Data design is the Star Schema Built on well-known relational concepts In the EDW. MOLAP Uses a Multi- Dimensional Database Management System Data design is the Cube Highly flexible. Data Marts Typical implementations have the ROLAP star schema feed the MOLAP cube

ROLAP Star Schema Stored in a relational DBMS Fact table is M-M relationship among dimensions.

Stored in a Multi- Dimensional DBMS Facts are preaggregated across all dimensions for improved performance. MOLAP Cube

DSS Applications The CIF is a reference architecture

Decision Support Systems Business Intelligence. Front-ends to ROLAP and OLAP Engines. Help us explore and visualize information at a high level

In Summary The CIF is a reference architecture for building out an information ecosystem. Applications from the external world are inputs into the CIF. The Integration & Transformation Layer transforms transactional data into corporate data. The Operational Data Store contains consolidated, non-historical data. The Enterprise Data Warehouse contains consolidated historical data. Data marts are tailored to the informational needs of a department or business process.

IST722 Data Warehousing Components of the Data Warehouse Michael A. Fudge, Jr.