NCR CORPORATION Presented by: Dave Raspberry Cheng Murray-Khoo Eric Braun A Data Warehousing Solutions Provider A Data Warehousing Solutions Provider.

Slides:



Advertisements
Similar presentations
Supervisor : Prof . Abbdolahzadeh
Advertisements

BY LECTURER/ AISHA DAWOOD DW Lab # 2. LAB EXERCISE #1 Oracle Data Warehousing Goal: Develop an application to implement defining subject area, design.
Jaros Jaros Overview. Jaros Overview - History Founded 1999 as consulting company GE Medical Systems IT Sigma Aldrich Smurfit-Stone Container Transitioned.
10 REASONS Why it makes a good option for your DB IN-MEMORY DATABASES Presenter #10: Robert Vitolo.
High Performance Analytical Appliance MPP Database Server Platform for high performance Prebuilt appliance with HW & SW included and optimally configured.
Data Warehousing M R BRAHMAM.
Polymorph Technologies Pte Ltd “ The Leader in Information Technology” ACCPOL (Point-of-Sales)
Business Intelligence Michael Gross Tina Larsell Chad Anderson.
Components and Architecture CS 543 – Data Warehousing.
Supply Chain Management
Data Warehousing - 3 ISYS 650. Snowflake Schema one or more dimension tables do not join directly to the fact table but must join through other dimension.
Chapter 14 The Second Component: The Database.
Lecture-9/ T. Nouf Almujally
Designing a Data Warehouse
Data Warehousing: Defined and Its Applications Pete Johnson April 2002.
Microsoft Dynamics Nav (Navision) Training
SharePoint Portal Server 2003 JAMES WEIMHOLT WEIDER HAO JUAN TURCIOS BILL HUERTA BRANDON BROWN JAMES WEIMHOLT INTRODUCTION OVERVIEW IMPLEMENTATION CASE.
Data Conversion to a Data warehouse Presented By Sanjay Gunasekaran.
An Introduction to Infrastructure Ch 11. Issues Performance drain on the operating environment Technical skills of the data warehouse implementers Operational.
By N.Gopinath AP/CSE. Why a Data Warehouse Application – Business Perspectives  There are several reasons why organizations consider Data Warehousing.
Designing a Data Warehouse Issues in DW design. Three Fundamental Processes Data Acquisition Data Storage Data a Access.
Ch 4. The Evolution of Analytic Scalability
6/1/2001 Supplementing Aleph Reports Using The Crystal Reports Web Component Server Presented by Bob Gerrity Head.
M icrosoft Data Warehousing - SQL Server State of the Technology Presentation by Sujata Angara Nakul Johri Sang Ho Park.
The Worlds of Database Systems Chapter 1. Database Management Systems (DBMS) DBMS: Powerful tool for creating and managing large amounts of data efficiently.
Best Practices for Data Warehousing. 2 Agenda – Best Practices for DW-BI Best Practices in Data Modeling Best Practices in ETL Best Practices in Reporting.
Activity Running Time DurationIntro0 2 min Setup scenario 2 2 min SQL BI components & concepts 4 5 min Data input (Let’s go shopping) 9 7 min Whiteboard.
1 INTRODUCTION TO DATABASE MANAGEMENT SYSTEM L E C T U R E
Data Warehouse Architecture. Inmon’s Corporate Information Factory The enterprise data warehouse is not intended to be queried directly by analytic applications,
Oracle Challenges Parallelism Limitations Parallelism is the ability for a single query to be run across multiple processors or servers. Large queries.
Data Warehousing at Acxiom Paul Montrose Data Warehousing at Acxiom Paul Montrose.
AN OVERVIEW OF DATA WAREHOUSING
OnLine Analytical Processing (OLAP)
Data warehousing and online analytical processing- Ref Chap 4) By Asst Prof. Muhammad Amir Alam.
Data Warehouse Fundamentals Rabie A. Ramadan, PhD 2.
OLAP & DSS SUPPORT IN DATA WAREHOUSE By - Pooja Sinha Kaushalya Bakde.
Database Design and Management CPTG /23/2015Chapter 12 of 38 Functions of a Database Store data Store data School: student records, class schedules,
Technology Industry Four Largest Computer Hardware Makers IBM DELL Gateway Hewlett Packard.
CISB594 – Business Intelligence
 2009 Calpont Corporation 1 Calpont Open Source Columnar Storage Engine for Scalable MySQL Data Warehousing April 22, 2009 MySQL User Conference Santa.
CISB594 – Business Intelligence Data Warehousing Part II.
Data Management for Decision Support Session-4 Prof. Bharat Bhasker.
By N.Gopinath AP/CSE.  The data warehouse architecture is based on a relational database management system server that functions as the central repository.
Infrastructure for Data Warehouses. Basics Of Data Access Data Store Machine Memory Buffer Memory Cache Data Store Buffer Bus Structure.
ORCALE CORPORATION:-Company profile Oracle Corporation was founded in the year 1977 and is the world’s largest s/w company and the leading supplier for.
1 Database Systems, 8 th Edition 1 Chapter 13 Business Intelligence and Data Warehouses Objectives In this chapter, you will learn: –How business intelligence.
Copyright© 2014, Sira Yongchareon Department of Computing, Faculty of Creative Industries and Business Lecturer : Dr. Sira Yongchareon ISCG 6425 Data Warehousing.
Session id: Darrell Hilliard Senior Delivery Manager Oracle University Oracle Corporation.
1 Copyright © 2009, Oracle. All rights reserved. Oracle Business Intelligence Enterprise Edition: Overview.
Last Updated : 27 th April 2004 Center of Excellence Data Warehousing Group An Introduction to Teradata.
Relational Database Systems Bartosz Zagorowicz. Flat Databases  Originally databases were flat.  All information was stored in a long text file, called.
1 Copyright © Oracle Corporation, All rights reserved. Business Intelligence and Data Warehousing.
Platinum DecisionBase1 DW Product Platinum - Computer AssociatesDecisionBase Hyunsook Lim Database Laboratory Dept. of CSE.
Building the Corporate Data Warehouse Pindaro Demertzoglou Data Resource Management.
Teradata Overview. 2 The Teradata Difference What We Do >Establish an enterprise view of the business >Integrate detailed, enterprise-wide data >Provide.
Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 8: Data Warehousing.
JET INFOSYSTEMS The main approach to Big Data parallel processing: Oracle way Aleksey Struchenko Database Department Leader.
Supervisor : Prof . Abbdolahzadeh
Polymorph Technologies Pte Ltd “ The Leader in Information Technology”
Operation Data Analysis Hints and Guidelines
Advanced Applied IT for Business 2
THE COMPELLING NEED FOR DATA WAREHOUSING
of our Partners and Customers
Data warehouse and OLAP
What is the Azure SQL Datawarehouse?
Ch 4. The Evolution of Analytic Scalability
Introduction to Teradata
King Saud University College of Engineering IE – 462: “Industrial Information Systems” Fall – 2018 (1st Sem H) Chapter 2 Information System.
Data Warehousing Concepts
Data Warehouse and OLAP Technology
Presentation transcript:

NCR CORPORATION Presented by: Dave Raspberry Cheng Murray-Khoo Eric Braun A Data Warehousing Solutions Provider A Data Warehousing Solutions Provider

NCR Overview n Founded in 1884 by John Patterson n Began as a cash register company, entered the computer business in 1960 n Bought in 1990 by AT&T, later divested n Purchased Teradata Corporation in the late eighties

NCR Overview n Based on Teradata and products based on the Intel chip, began the platform known as the WorldMark series n Subsequently, Data Warehousing is becoming the dominant business in NCR’s product line n Now pursuing an across-the-board approach to customers--large and small

How is NCR different? n Utilizes Parallel Processing and ER logical modeling –the only fully parallel solution on the market featuring parallel loading, processing, and archiving n Strategic approach based on three factors: –Performance –Scalability –Ease of Setup and Support

Parallel Processing- some lessons learned n Divide the rows evenly n Optimize the SQL requests n Provide a scalable interconnect n Load and restore data n Provide for easy system administration

Scalable Data Warehouse Framework n When building a data warehouse, NCR’s approach involves areas throughout the organization, IT resources, the technologies, the processes, and the businesses n SDW accommodates independent data marts or directly building a DW with dependent data marts Recommends beginning with a small scalable data warehouse, focusing on one or two area. Recommends beginning with a small scalable data warehouse, focusing on one or two area.

Scalable Data Warehouse Framework n There are four dimensions of the scalable system –The ability to input and extract data with consistent response times –The number of users or queries that can be run simultaneously –The environmental complexity of the data model and the queries being run against the model –The degree of support needed to maintain scalability

NCR’s Logical Data Model Philosophy n NCR believes star schema’s limit business intelligence n During modeling process, prefers Third Normal Form n Denormalizing helps the DBMS, but hurts the quality of information n Third normal form avoids data integrity compromises

NCR’s Logical Data Model n The four hardest things for a database to do: –Join Tables –Aggregate Data –Sort Data –Scan large amounts of data

WorldMark Servers n Four generations of large and medium scale servers n Optimized for Teradata architecture and Bynet technology n WorldMark 4800 and 5200 operate on Intel chip technology n 4800 is scalable, and designed to run on applications from 50 GB to 1 TB n Can be upgraded to the WorldMark 5200 n Designed for large scale data warehousing, 400GB to 100TB

Teradata Relational Database Management System (RDBMS) n Most powerful decision support parallel relational database. n Realistically support data warehouses in excess 500 gigabytes of user data. WHAT MAKE NCR TERADATA SO SPECIAL?

Teradata runs on Symmetric Multiprocessing (SMP) hardware platform Each access module process (AMP) as one processor. Each AMP executes the query functions and data management. Each AMP controls and maintains a portion of database stored on the disks, TASKS CAN PERFORM IN PARALLEL as assigned by VNET. Each AMP acts as a “unit of parallelism”.

Teradata’s Unconditional Parallelism NCR’s Teradata patented “shared nothing” architecture. AMP

Teradata runs on Massively Parallel Processing (MPP) hardware platform Interconnect that coordinates and synchronizes the activities of a large number of SMP nodes

Scalability - the cornerstone of Teradata RDBMS BYNET design can linearly scale to support up to 4096 SMP nodes on a single system. Database Capacity: n 128 Terabytes of data n 1, 024 SMP nodes n 32, 768 physical processors

NCR Scalable Data Warehouse n NCR handles the world’s biggest data warehouse for decision support without compromising the data integrity by de- normalizing the tables. n NCR is the only vendor in the market utilizing parallel processing with ER logical modeling.

NCR’s Alliance Partners

NCR’s Strategies n Customer Privacy –Claims to be the first solutions firm to place customer privacy at the center of its strategy –New database products from NCR will enable marketers to add a customer's personal preference on how their data should be used n Neighborhood Retailing n Customer Management Solutions n Customer Relationship Management

How Do Customers Benefit From NCR? n Retailers analyze their profitability on a product, customer, and store-level basis n Banks use NCR's data warehousing to separate the profitable customers from the freeloaders n Supermarkets are working on an automated pricing system for every item in the store n In the communications industry, NCR provides much needed information management and the ability to bring new products to market quickly.

NCR Customer’s Complaints n Databases from Oracle, IBM and Microsoft are quickly adding all the features that perform sophisticated requests on multiterabyte data-warehouses n Changing ownership and leadership n NCR’s size

Competition and Problems n Competition: Andersen Consulting, EDS, IBM, and Unisys n Still trails Oracle, Compaq Computer, IBM and Hewlett- Packard in data warehouse sales n Core business in computer systems is declining by almost 30% a year and data warehouse sales haven’t picked up the slack n NCR relies on international markets for 60% of its revenue n Stock price has dropped nearly 50% over the last four months

Conclusion and Future Outlook n Now dedicated to becoming the leader in data warehousing n Focused on putting together a solid strategy to help businesses understand who their customers are and how best to serve them n Outsources to Solectron nearly everything it used to manufacture itself, resulting in great cost savings n Forecast six to ten acquisitions in the $20 million to $100 million range in the near future n Possess the needed technology and assets n Planning initiatives in e-commerce

NCR Questions…?