Fundamentals, Design, and Implementation, 9/e by David M. Kroenke Lecture 23: Sharing Enterprise Data Chapter 15 BSA206 Database Management Systems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 2 Copyright © 2004 Database Processing Architectures  System architectures for enterprise database processing: –Teleprocessing system –Client-server system –File-sharing system –Distributed system

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 3 Copyright © 2004 Teleprocessing Systems  Classic architecture for multi-user database processing  Users operate dumb terminals or PC that emulate dumb terminals –User interface is usually simple and primitive  A single centralized computer processes communications control program, application programs, DBMS, and operating system

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 4 Copyright © 2004 Teleprocessing Systems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 5 Copyright © 2004 Client-Server Systems  A client-server system consists of a network of computers connected via a LAN  Clients are personal computers used to process application programs  Servers are PCs or mainframes that stores DBMS and the data-management portion of the operating system

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 6 Copyright © 2004 Client-Server Systems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 7 Copyright © 2004 File-Sharing Systems  This architecture was developed before the client- server architecture  File server and user computers are connected through LAN –File server provides access to files and other resources –User computers must contain a copy of DBMS and application programs  DBMS on user’s computer sends requests to the data management portion of the operating system on the file server for file-level processing –This cause more traffic across LAN than the client-server system

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 8 Copyright © 2004 File-Sharing Systems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 9 Copyright © 2004 Distributed Database Systems  Distributed database systems use multiple computers to process the same database  Distributed processing: use multiple computers for applications or DBMS processing –E.g., file-sharing, client-server, and distributed database system  Distributed database processing: distribute database to multiple computers –E.g., distributed database system

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 10 Copyright © 2004 Distributed Database Systems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 11 Copyright © 2004 Database Partitioning  A vertical partition, or vertical fragment, refers to a table that is broken into two or more sets of columns  A horizontal partition, or horizontal fragment, refers to a table that is broken into two or more sets of rows  Mixed partition refers to a database broken into both horizontal and vertical partitions

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 12 Copyright © 2004 Types of Distributed Databases  Types of distributed database: –Nonpartitioned, nonreplicate –Partitioned, nonreplicated –Nonpartitioned, replicated –Partitioned, replicated  The greater the degree of partitioning and replication –The greater the flexibility, independence, and reliability –The greater the expense, control difficulty, and security problems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 13 Copyright © 2004 Types of Distributed Databases

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 14 Copyright © 2004 Comparing DB Distribution Alternatives

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 15 Copyright © 2004 Distributed Processing Techniques  Three types of distributed database processing  Downloading of read-only data: only one computer updates data, but multiple computers are sent copies to process  Updating by a designated computer: allows data update requests to originate on multiple computers, but to transmit those update requests to a designated computer for processing –Database at the designated computers must be periodically synchronized

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 16 Copyright © 2004 Distributed Processing Techniques (cont.)  Updating by multiple computers: allows multiple updates on the same data at multiple sites –Three types of distributed update conflict can occur: Loss of uniqueness Lost updates due to concurrent transactions Updates of deleted data  Coordinating distributed atomic transactions is difficult and requires a two-phase commit  The OLE Distributed Transaction Server and Java Enterprise Beans are two technologies for dealing with these problems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 17 Copyright © 2004 Downloading Data  Powerful personal computers enable user to download enterprise data for local processing  Users can query and report on downloaded data using DBMS products on their machines  Normally, users are not allowed to update and return data to prevent data integrity problems  A Web server can be used to publish downloaded data

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 18 Copyright © 2004 Potential Problems of Downloaded Databases  Coordination –Downloaded data must conform to database constraints –Local updates must be coordinated with downloads  Consistency –In general, downloaded data should not be updated –Applications need features to prevent updating –Users should be made aware of possible problems

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 19 Copyright © 2004 Potential Problems of Downloaded Databases (cont.)  Access Control –Data may be replicated on many computers –Procedures to control data access are more complicated  Potential for Computer Crime –Illegal copying is difficult to prevent –Diskettes and illegal online access are easy to conceal –Risk may prevent the development of downloaded data applications

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 20 Copyright © 2004 Processing Downloaded Data with a Web Server

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 21 Copyright © 2004 OLAP  On Line Analytical Processing (OLAP) is a new way of presenting information  With it, data is viewed in cubes that have axes, dimensions, measures, slices, and levels  Cube refers to –Underlying semantic structure that is used to interpret data –A particular materialization of data in such a semantic structure

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 22 Copyright © 2004 Example: Relational Source Data

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 23 Copyright © 2004 Example: OLAP Cube

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 24 Copyright © 2004 OLAP Terminology  OLAP hypercube: means a data display with an unlimited number of axes

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 25 Copyright © 2004 OLAP Schema Structures  Star schema: every dimension table is adjacent to the table storing the measure values –These tables may or may not be normalized  Snowflake schema: there can be multilevel, normalized tables  In general, the star schema requires greater storage, but it is faster to process than the snowflake schema

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 26 Copyright © 2004 Example: Star Schema

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 27 Copyright © 2004 Example: Snowflake Schema

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 28 Copyright © 2004 OLAP Storage Alternatives  Three different means for storing OLAP data  ROLAP (relational OLAP): relational DBMS with extensions is sufficient to meet OLAP requirements  MOLAP (multidimensional OLAP): a specialized multidimensional processor is necessary to produce acceptable OLAP performance  HOLAP (hybrid OLAP): both DBMS products and specialized OLAP engines have a role and can be used to advantage

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 29 Copyright © 2004 Data Warehouse  A data warehouse is a store of enterprise data that is designed to facilitate management decision-making  Goal: to increase the value of the organization’s data asset  Role: to store extracts from operational data and make those extracts available to users in a useful format –Data can be extracts from databases, files, images, recordings, photos, external data, etc.

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 30 Copyright © 2004 Data Warehouse

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 31 Copyright © 2004 Data Warehouse Components  Data extraction tools  Extracted data  Metadata of warehouse contents  Warehouse DBMS(s) and OLAP servers  Warehouse data management tools  Data delivery programs  End-user analysis tools  User training courses and materials  Warehouse consultants

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 32 Copyright © 2004 Data Warehouse Requirements  Queries and reports with variable structure  User-specified data aggregation  User-specified drill down  Graphical outputs  Integration with domain-specific programs

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 33 Copyright © 2004 Challenges for Data Warehouses  Inconsistent data –When data are integrated, inconsistencies can develop due to timing and domain differences –Solution: create metadata to describe both timing and domains of source data  Tool Integration –Because of the many tools required in a data warehouse, tools will have different user interfaces and inconsistent means of importing and exporting data, and it may be difficult to obtain technical support

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 34 Copyright © 2004 Challenges for Data Warehouses  Lack of tools for managing the data warehouse –The organization may have to develop its own tools for managing non-relational data and for maintaining appropriate metadata. Such development is difficult and expensive  Ad hoc nature of requirements –Such requests are difficult to satisfy –Solution: create datamart, i.e,, limited-scope data warehouses

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 35 Copyright © 2004 Data Marts  A data mart is a limited-scope data warehouse  A data mart is easier to manage than the enterprise data warehouse because –It has a much smaller domain –It can be restricted To a particular type of input data To a particular business function To a particular business unit or geographic area

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 36 Copyright © 2004 Enterprise Data Sharing Continuum

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 37 Copyright © 2004 Data Administration  Data are an important organizational asset that can support both operations and management decision making  The purpose of offices of data administration is to guard and protect the data and to ensure that they are used effectively

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 38 Copyright © 2004 Data Administration Challenges  Many types of data exist  Basic categories of data are not obvious  The same data can have many names  The same data can have many descriptions and formats  Data are changed often concurrently  Political-organizational issues complicate operational issues

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 39 Copyright © 2004 Functions of Data Administration  Marketing –Communicate existence of data administration to organization –Explain reason for existence of standards, policies, and guidelines –Describe in a positive light the services provided  Data Standards –Establish standard means for describing data items. Standards include name, definition, description, processing restrictions, etc. –Establish data proponents

Database Processing: Fundamentals, Design, and Implementation, 9/e by David M. KroenkeLecture 23 / Slide 40 Copyright © 2004 Functions of Data Administration  Data Policies –Establish organization-wide data policy, e.g., security, data proponency, and distribution  Forum for Data Conflict Resolution –Establish procedures for reporting conflicts –Provide means for hearing all perspectives and views –Have authority to make decision to resolve conflict  Return on Organization's Data Investment –Focus attention on value of data investment –Investigate new methodologies and technologies –Take proactive attitude toward information management