Banking Software System in Data warehousing

Introduction

Over the last 20 years, $1 trillion has been invested in new computer systems to gain competitive advantage. The vast majority of these systems have automated business processes, to make them faster, cheaper, and more responsive to the customer. Electronic point of sales (EPOS) at supermarkets, itemized billing at telecommunication companies (telcos), and mass market mailing at catalog companies are some examples of such “Operational Systems”. These systems computerized the day-to-day operations of business organizations. Some characteristics of the operational systems are as follows:

·       Most organizations have a number of individual operational systems (databases, applications)

·       On-Line Transaction Processing (OLTP) systems capture the business transactions that occur.

·       An Operational System is a system that is used daily (perhaps constantly) to perform routine operations - part of the normal business processes.

·       Examples: Order Entry, Purchasing, Stock/Bond trading, bank operations.

·       Users make short term, localized business decisions based on operational data. e.g., "Can I fill this order based on the current units in inventory?"

Presently almost all businesses have operational systems and these systems are not giving them any competitive advantage. These systems have gathered a vast amount of “data” over the years. The companies are now realizing the importance of this “hidden treasure” of information. Efforts are now on to tap into this information that will improve the quality of their decision-making.

A “data warehouse” is nothing but a repository of data collected from the various operational systems of an organization. This data is then comprehensively analyzed to gain competitive advantage. The analysis is basically used in decision making at the top level.

Process

A typical data warehouse building will follow SDLC life cycle

It can be described as a Software development process in systems engineering, information systems and software engineering, is a process of creating or altering information systems, and the models and methodologies that people use to develop these system.

A Systems Development Life Cycle (SDLC) adheres to important phases that are essential for developers, such as planning, analysis, design, and implementation, and are explained in the section below. A number of system development life cycle (SDLC) models have been created: waterfall, fountain, spiral, build and fix, rapid prototyping, incremental, and synchronize and stabilize


Systems analysis, requirements definition: Defines project goals into defined functions and operation of the intended application. Analyzes end-user information needs.

Systems design: Describes desired features and operations in detail, including screen layouts, business rules, process diagrams, pseudo code and other documentation.

Development: The real code is written here.

 Integration and testing: Brings all the pieces together into a special testing environment, then checks for errors, bugs and interoperability.

Acceptance, installation, deployment: The final stage of initial development, where the software is put into production and runs actual business.

 Maintenance: What happens during the rest of the software's life: changes, correction, additions, moves to a different computing platform and more. This is often the longest of the stages.

Data warehouse

A data warehouse is a database used for reporting and analysis. The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting.

                                     A video Explaining Typical data ware house

A data warehouse is a database used for reporting and analysis. The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting.

Data Warehouse Architecture

Data Bases used in banking Software

Oracle

SQL Server

IBM DB2

Sybase

MS Access

Hadoop

Tools used for Data Integration in Banking DW Software

Informatica - Power Center

IBM - Websphere DataStage(Formerly known as Ascential DataStage)

SAP - BusinessObjects Data Integrator

IBM - Cognos Data Manager (Formerly known as Cognos DecisionStream)

Microsoft - SQL Server Integration Services

Oracle - Data Integrator (Formerly known as Sunopsis Data Conductor)

SAS - Data Integration Studio

Oracle - Warehouse Builder

Tools used for Business intelligence in Banking DW Software

Business Objects Enterprise XI SAP

IBM Cognos Series 10  IBM

JasperSoft (open source) JasperSoft

Microsoft BI tools   Microsoft

Microstrategy   Microstrategy

Hardware required in building Data warehouse for a Banking DW Software

Operating System:

Unix server:

AIX, HP-UX, IRIX, Solaris, Tru64 ,A/UX, Mac OS X

Mircrosoft Server:

Windows Server 2008

Windows Server 2003

Hard Drive:

30TB to over 2PB and performance up to 32GB/sec (typically)

Processor:

Processor with more than 4-20 CPUs

Estimate

Project Completion Time: 2-3 years (With Maintenance)

No of team members: 30-50

Cost:     Two year wages for each team member

                Licensing cost for Data ware house tools

                Server and Hardware Pricing

Data Warehouse Design Considerations

Data warehousing is one of the more powerful tools available to support a business enterprise. Data warehouses support business decisions by collecting, consolidating, and organizing data for reporting and analysis with tools such as online analytical processing (OLAP) and data mining. Although data warehouses are built on relational database technology, the design of a data warehouse database differs substantially from the design of an online transaction processing system (OLTP) database.