Nine Best Practices for Efficient Database Archiving - Slide 5

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The number one concern for organizations implementing a data growth management solution is to ensure the integrity of the business application. Thus, the process of archiving must take into account the business context of the data as well as relationships between different types of data. Data management is rendered even more complex because transactional dependencies are often defined at the application layer rather than the database layer. This means that a data growth management tool cannot simply reverse engineer the data model at the time of implementation. And any auto-discovery process is bound to be insufficient because it will miss all of the relationships embedded in the application. These rules and relationships can become quite complicated in large prepackaged products, such as Oracle E-Business Suite, PeopleSoft Enterprise, and Siebel CRM, which may have tens of thousands of database objects and a large number of integrated modules.

Successfully archiving data in these solutions requires an in-depth understanding of how the application defines a database object — that is, where the data is located and what structured and unstructured data needs to be related — and the set of rules that operate against the data. Most in-house developers have a difficult time reverse engineering the data relationships in complex applications. A best-practices archiving solution includes prepackaged business rules that incorporate an in-depth understanding of the way a particular enterprise solution stores and structures data. By choosing a solution with prepackaged rules, organizations save the time and effort of determining which tables to archive.

Organizations that use prepackaged ERP/CRM, custom, and third-party applications are seeing their production databases grow exponentially. At the same time, business policies and regulations require them to retain structured and unstructured data indefinitely. Storing increasing amounts of data on production systems is a recipe for poor performance no matter how much hardware is added or how much an application is tuned. Organizations need a way to manage this growth effectively.

Over the past few years, the Storage Networking Industry Association (SNIA) has promoted the concept of Information Lifecycle Management (ILM) as a means of better aligning the business value of data with the most appropriate and cost-effective IT infrastructure — from the time information is added to the database until it can be destroyed.

While the SNIA defines what an ILM system should accomplish, it does not specify any particular technology for implementing application ILM. According to Informatica, archiving is one approach that can be particularly effective — if organizations follow archiving best practices to ensure the optimal management of data during its life cycle.

This slideshow features Informatica’s best-practices approach for implementing application ILM archiving.

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