In this series of modules, you will use IBM InfoSphere FastTrack to create an application that identifies customers with high value to your business. You will. InfoSphere FastTrack provides capabilities to automate the workflow of your data integration project. Users can track and automate multiple. IBM InfoSphere FastTrack accelerates the design time to create source-to-target mappings and to automatically generate jobs. Mappings and jobs are then.
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Generate jobs that are used to build applications Generate reports to view mapping specifications statistics and characteristics. Create mapping specifications that map data from the source to the target tables.
System requirements The following components and applications must be installed on your system. Business analysts use InfoSphere FastTrack to translate business requirements into a set of specifications, which data integration specialists then use to produce a data integration application that incorporates the business requirements.
Extract customer information from the BANK1 database In this module, you begin to consolidate relevant customer data into a table that follows the standard model of the company.
You can also export mapping specifications into Microsoft Excel spreadsheets and. The need for speed – accelerating data integration projects This white paper explains how InfoSphere FastTrack can help enterprises accelerate the deployment of data integration projects by simplifying and improving the communication process between the business analyst and the developer.
The IT team was faced with supporting disparate data environments for its core banking system, which made customer data difficult to manage. InfoSphere FastTrack assets You can use the -fasttrack option of the istool command in a command-line interface to move InfoSphere FastTrack projects and related assets across different installations of InfoSphere Information Server.
The messages listed in this section describe the errors, explain why they occurred, and suggests actions that address the messages. You can also view detailed properties information including an expandable view of the artifacts in the IBM InfoSphere Information Server metadata services repository. These tasks are required to build the application that identifies high-value customers:: Integrate data from Bank 3 While you resolved issues with data in the Bank 1 and Bank 2 subsidiaries, the executive board at First Midwest acquired a new bank, Bank 3.
HTML Creating mapping specifications These topics describe how to create mapping specifications that consist of source-to-target mappings, and how to use imported InfoSphere DataStage shared containers as mapping components.
IBM InfoSphere FastTrack
Now you must integrate the customer data from Bank 3. You will access this database in Module 1, in Lesson 1.
These actions are reflected in Figure 1. By automating the flow of information and increasing collaboration, development time is reduced. Therefore, account balances for both checking and savings accounts must be aggregated to compute the total account balance for a customer.
Users can track and automate multiple data integration tasks, shortening the time between developing business requirements and implementing a solution. Now the priority of First Midwest is to regain its customer base by improving its view of customer data.
The relationships are then published to the InfoSphere Information Server metadata services common repository so that they can be shared across development teams.
You use a simple user interface to complete business requirements, track work, and then translate the specifications directly into integration code for use by the InfoSphere Information Server data integration engine. First Midwest has these subsidiaries: Time required In the first module, you set up your environment, and the time required depends on your current environment.
First Midwest is a financial institution that grew by acquisition. Where InfoSphere FastTrack fits in the suite architecture You can use InfoSphere FastTrack to track and automate efforts that span multiple data integration tasks from analysis to code generation, shortening the time from business requirements to solution implementation. By using InfoSphere FastTrackthe IT team specified data relationships and transformations that the business analysts used to create specifications, which consist of source-to-target mappings.
In this module, you perform a join, add source columns based on lookup operators, and define business rules before you extract the customer information.
Bank 2 also keeps track of demographic data about customers in a separate table, BANK2. Customizable spreadsheet view Provides the ability to annotate column mappings with business rules and transformation logic.
Getting started in IBM InfoSphere FastTrack
You can also delete metadata from the metadata repository. The business analysts can also use InfoSphere FastTrack to discover and optimize mapping by using existing data profiling results from the metadata repository. The issues documented in this section provide descriptions of the problems and steps to correct them. First Midwest defines two levels of high-value customers.
Scenario for IBM InfoSphere FastTrack
As First Midwest focused on acquisition activities, their competition pulled away several of their high-value customers. First Midwest created a standard customer database that its subsidiaries use to represent customer data. Message reference These topics describe warning and validation messages, explain why they occur, and recommend actions to take.
The remaining modules each take about minutes to complete. You can also manage views and customize your environment. Setting or checking status You can indicate the status of mapping specifications and specific source-to-target mappings by using the mapping editor.
InfoSphere FastTrack InfoSphere FastTrack generates messages inofsphere to errors that occur during importing, validation, mapping, and job generation. BANK 2 Holds checking and savings accounts. Updating metadata or deleting metadata from the metadata repository You add, update or change the metadata in the metadata repository by performing an import. You can also save the job in the metadata repository.
Identify gold customers as level B and platinum customers as level A. You can also use a discover function to find column matches and create relationships between columns. You can also manage projects and change your server password.
Imported metadata is viewable by all suite users and can be used by all suite components.