Data Warehousing A QUICK SUMMARY Sushanthan Premanath & Indrajith Premanath CSCI 4707.

Slides:



Advertisements
Similar presentations
CHAPTER OBJECTIVE: NORMALIZATION THE SNOWFLAKE SCHEMA.
Advertisements

James Serra – Data Warehouse/BI/MDM Architect
Enterprise Data Warehousing (EDW) By: Jordan Olp.
ITEC 423 Data Warehousing and Data Mining Lecture 3.
Data Warehouse IMS5024 – presented by Eder Tsang.
The Data Warehouse and Technology. Some Technological  Manage large amounts of data  Manage data on a diverse media  Easily index and monitor data.
An Introduction to Dimensional Data Warehouse Design Presented by Joseph J. Sarna Jr. JJS Systems, LLC.
ITGS HL Presentation By: Victor Chee. Just In Time (JIT) Process Is a production strategy that improves return on investment (ROI) by reducing inventory.
1 © Prentice Hall, 2002 Chapter 11: Data Warehousing.
July 6, 2011 Martha Newton. Objectives Data Group staff Data storage concepts What is a Data Warehouse What’s in the Data Warehouse (Vanderbilt’s Enterprise.
Tanvi Madgavkar CSE 7330 FALL Ralph Kimball states that : A data warehouse is a copy of transaction data specifically structured for query and analysis.
Data Warehouse Toolkit Introduction. Data Warehouse Bill Inmon's paradigm: Data warehouse is one part of the overall business intelligence system. An.
Data Warehousing: Defined and Its Applications Pete Johnson April 2002.
Components of the Data Warehouse Michael A. Fudge, Jr.
Business Intelligence Instructor: Bajuna Salehe Web:
1 Brett Hanes 30 March 2007 Data Warehousing & Business Intelligence 30 March 2007 Brett Hanes.
Database Systems – Data Warehousing
Data Warehousing Seminar Chapter 5. Data Warehouse Design Methodology Data Warehousing Lab. HyeYoung Cho.
The Business Intelligence Side of Blue Mountain RAM Bill Lucas, IT Systems Architect and Senior Software Engineer.
Data Warehouse Architecture. Inmon’s Corporate Information Factory The enterprise data warehouse is not intended to be queried directly by analytic applications,
ETL Overview February 24, DS User Group - ETL - February ETL Overview “ETL is the heart and soul of business intelligence (BI).” -- TDWI ETL.
Intro. to Data Warehouse
2 Copyright © Oracle Corporation, All rights reserved. Defining Data Warehouse Concepts and Terminology.
The Data Warehouse “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of “all” an organisation’s data in support.
Data Warehousing.
Data Warehousing Lecture-1 1. Introduction and Background 2.
1 Reviewing Data Warehouse Basics. Lessons 1.Reviewing Data Warehouse Basics 2.Defining the Business and Logical Models 3.Creating the Dimensional Model.
PS Business Inteligence and Datawarehousing Presentation for study visit to NL.
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
Best Practices in Higher Education Student Data Warehousing Forum Northwestern University October 21-22, 2003 FIRST QUESTIONS Emily Thomas Stony Brook.
Datawarehouse A sneak preview. 2 Data Warehouse Approach An old idea with a new interest: Cheap Computing Power Special Purpose Hardware New Data Structures.
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
 Understand the basic definitions and concepts of data warehouses  Describe data warehouse architectures (high level).  Describe the processes used.
ระบบฐานข้อมูลขั้นสูง (Advanced Database Systems) Lecturer AJ. Suwan Janin Phone:
Two-Tier DW Architecture. Three-Tier DW Architecture.
Advanced Database Concepts
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
Recap of Day 1 1 Dr. Chaitali Basu Mukherji. 2 Which are our lowest/highest margin customers ? Who are my customers and what products are they buying?
Data Warehouse A place the information system department puts the data that is turned into information. Data must be properly prepared,organized,and presented.
Oracle 8i Data Warehousing (chapter 1, 2) Data Warehousing Lab. 석사 1 학기 HyunSuk Jung.
Business intelligence systems. Data warehousing. An orderly and accessible repositery of known facts and related data used as a basis for making better.
1 Management Information Systems M Agung Ali Fikri, SE. MM.
Data Warehouse Data Mart Elahe Soroush. Agenda  Data Warehouse definition  Concepts  Logical transformation  Physical transformation  DW components.
2 Copyright © 2006, Oracle. All rights reserved. Defining Data Warehouse Concepts and Terminology.
Data Modelling for Beginners. About Coeo Senior DBA Microsoft Certified Master SQL Server Studying MSc Data Science at Dundee University.
نمايندگي استان يزد. نمايندگي استان يزد طراحی کسب و کار الکترونیکی ارائه کننده : محسن افسر قره باغ.
DATA WAREHOUSING TECHNIQUES ROUNDTABLE Kathy Bronson Trevyn Bowden Clackamas Communtiy College 7/2016 Information Technology Forest Grove, Oregon NWEUG.
Advanced Applied IT for Business 2
Manajemen Data (2) PTI Pertemuan 6.
MIS5101: Extract, Transform, Load (ETL)
Introduction to Data Warehousing
Data Warehousing Business Intelligence
Data Warehousing and Data Mining By N.Gopinath AP/CSE
Designing Business Intelligence Solutions with Microsoft SQL Server
المحاضرة 4 : مستودعات البيانات (Data warehouse)
MIS5101: Extract, Transform, Load (ETL)
Data Warehouses, Dimensional Modeling, and the Laundromat
Components of the Data Warehouse Michael A. Fudge, Jr.
MIS5101: Extract, Transform, Load (ETL)
DATA WAREHOUSE: THE BUILDING BLOCKS
An Introduction to Data Warehousing
Data Warehouse Architecture
Data Warehouses, Dimensional Modeling, and the Laundromat
Data Warehouse A place the information system department puts the data that is turned into information. Data must be properly prepared,organized,and presented.
Warehouse Architecture
Data Warehouse Architecture
Technical Architecture
Data Warehouses, Dimensional Modeling, and the Laundromat
Data Warehousing.
Presentation transcript:

Data Warehousing A QUICK SUMMARY Sushanthan Premanath & Indrajith Premanath CSCI 4707

The Main Protagonists Bill Inmon  Born 1945  Father of Data Warehousing Ralph Kimball  Born 1944  Commercialized Data Warehousing

Big Data  The data should be de-normalized to 2NF.  This means you get data redundancy.  This means you need more storage.  The data can retrieved more quickly.  Data Warehousing is to provide aggregate data which is in a suitable format for decision making.

ETL and Data Marts  Extraction, Transformation and Loading (ETL)  E – Extraction: Get the data.  T – Transformation: Make it useful.  L – Loading : Save it to the warehouse.  Data Marts  Don’t mess with the data.  Keep it simple for the user.  Small problems are easier to solve.