Designing and managing data growth processes

The data growth component of the IBM® Optim™ solution allows you to extract sets of relational data from one or more databases and store the data for future use.

You can manage data growth from two environments, Optim for distributed platforms and Optim Designer.

The data growth component provides everything you need to create and manage archives of relationally intact data from databases with any number of tables, interconnected with any number of DBMS and application‑managed relationships, regardless of their complexity.

After creating an archive file, Archive selectively removes data from the production database, according to your instructions, to maximize database performance and response time. An indexing feature allows you to quickly search archive files for needed information and, if necessary, restore all or a precisely selected, referentially intact, portion of the data.

Archiving data is a simple two-step process. You first specify the tables and relationships that define the set of referentially intact data to be archived. In the access definition, you also indicate any data to be deleted from the production database after archiving, set up indexing parameters, and define archive actions. In the second step, the archive process copies the data described in the access definition to an archive file, executes appropriate actions, creates indexes used subsequently to find archived data, and deletes the selected data from the database.

The powerful, yet safe, delete feature resolves the problem of deleting production data. Using standard facilities for all operations, the data growth component quickly and accurately deletes all or a portion of the archived data. For example, you might want to archive data for customers that have been inactive for the past year. You can create an archive file of all data pertaining to the inactive customers and delete only the order and payment history from the production database, leaving intact the master account information, such as name and address.

Search facilities allow you to search archive files, specifying criteria to narrow the search. Search results can be presented in an interactive display, allowing you to browse archived data without having to restore it to your production system, a method useful in many situations – for example, to answer customer inquiries.

If archived data must be restored, Archive can reassemble data from hundreds of tables (across platforms, if necessary), identify the pertinent set of related rows, and restore them. The restore process allows you to restore archived data to the production database or to a separate database and accommodates data model changes during the restoration. In addition, you can find or restore data using criteria that differ completely from that used to create the archive.

For organizations that have developed a comprehensive archiving strategy, archive and restore processes can be automated, with archiving occurring on a regularly scheduled basis and restoration triggered by applications that provide the criteria for data to be restored.

The data growth component addresses a critical operational need for organizations with large, complex databases. Old data can be archived in a precise manner and production databases optimized for peak performance. Archived data can be browsed or selectively restored as needed. With the data growth component, an enterprise can maximize its investment in its applications and operational platform. At the same time, the level of service is maximized because production database searches and response times are minimized. Programmers and DBAs are not required to spend hours writing and debugging complex archive and one-time restoration programs.

Intelligent window handling technology allows you to display multiple dialogs, pop-up windows, context-sensitive online help, and tutorials.