Chapter 3 Distributed Data Processing Business Data Communications

Chapter 3 : Distributed Data Processing Business Data Communications, 5 e

Centralized Data Processing • Centralized computers, processing, data, control, support • What are the advantages? – Economies of scale (equipment and personnel) – Lack of duplication – Ease in enforcing standards, security • What are the disadvantages? ? ?

Distributed Data Processing • Computers are dispersed throughout organization • Allows greater flexibility in meeting individual needs • More redundancy • More autonomy

Why is DDP Increasing? • Dramatically reduced workstation costs • Improved user interfaces and desktop power • Ability to share data across multiple servers

DDP Pros & Cons • There are no “one-size-fits-all” solutions • Key issues – How does it affect end-users? – How does it affect management? – How does it affect productivity? – How does it affect bottom-line?

Benefits of DDP • Responsiveness • Availability • Correspondence to Org. Patterns • Resource Sharing • Incremental Growth • Increased User Involvement & Control • End-user Productivity • Distance & location independence • Privacy and security • Vendor independence • Flexibility

Drawbacks of DDP • More difficulty test & failure diagnosis • More components and dependence on communication means more points of failure • Incompatibility of components • Incompatibility of data • More complex management & control • Difficulty in control of corporate information resources • Suboptimal procurement • Duplication of effort

Client/Server Architecture • Combines advantages of distributed and centralized computing • Cost-effective, achieves economies of scale • Flexible, scalable approach

Intranets • Uses Internet-based standards & TCP/IP • Content is accessible only to internal users • A specialized form of client/server architecture • Can be managed (unlike Internet)

Extranets • Similar to intranet, but provides access to controlled number of outside users – Vendors/suppliers – Customers

Distributed applications • Vertical partitioning – One application dispersed among systems – Example: Retail chain POS, inventory, analysis • Horizontal partitioning – Different applications on different systems – One application replicated on systems – Example: Office automation

Other forms of DDP • Distributed devices – Example: ATM machines • Network management – Centralized systems provide management and control of distributed nodes

Distributed data • Centralized database – Pro: No duplication of data – Con: Contention for access • Replicated database – Pro: No contention – Con: High storage and data reorg/update costs • Partitioned database – Pro: No duplication, limited contention – Con: Ad hoc reports more difficult to assemble

Networking Implications • Connectivity requirements – What links between components are necessary? • Availability requirements – Percentage of time application or data is available to users • Performance requirements – Response time requirements