ILM with Automatic Data Optimization, Flashback Data Archive and In-Database Archiving


Information Lifecycle Management (ILM) is the practice of applying policies for the effective management of information throughout its useful life. ILM includes every phase of a “row” from its beginning to its end, and consists of the policies, processes, practices, and tools used to align the business value of information with the most appropriate and cost effective IT infrastructure, from the time information is conceived, through its final disposition. Automatic Data Optimization (ADO) creates policies, and automates actions based on those policies, to implement your ILM strategy utilizing the table usage statistics collected by Heat Map. Flashback Data Archive provides the ability to track and store transactional changes to a table over its lifetime -- very useful for compliance with record stage policies and audit reports. In-Database Archiving allows users and applications to set the archive state for individual rows. Rows that have been marked as archived stay where they are, but are not visible unless the session is enabled to see archived data.

Heat Map and Automatic Data Optimization


Heat Map gathers and maintains system-generated data usage statistics at the block and segment levels – information to automate the compression of data, and movement of tables, in order to reduce storage costs, improve performance and optimize database storage. Heat Map, used in conjunction with Automatic Data Optimization, can automate compression and storage tiering policies based on the actual usage of the data. Segment level Heat Map tracks the time of last modification and access of tables and partitions. Row level Heat Map tracks modification times for individual rows (aggregated to the block level). The statistics collected are used to define compression and storage policies that are automatically maintained throughout the lifecycle of the data. Heat Map ignores internal access done for system tasks -- automatically excluding Stats Gathering, DDLs or Table Redefinitions. All ADO operations are performed automatically and in the background, with no user intervention required.

Flashback Data Archive


Flashback Data Archive (FDA) provides a mechanism for tracking changes to production databases that is secure, efficient, easy to use and application transparent. FDA allows organizations to configure historical data capture in a matter of minutes, providing a centralized and seamlessly queryable historical data store. Prior to Flashback Data Archive, one of the approaches was to capture and manage historical data at the application level. Flashback Data Archive consists of one or more tablespaces or parts thereof. Flashback Data Archive is configured with retention time and data archived in the Flashback Data Archive is retained for the retention time specified when the Flashback Data Archive was created. Use Flashback Data Archive in the same manner as other Flashback features to view or restore the data as of a time (or time range) in the past.

In-Database Archiving


In-Database Archiving allows users and applications to set the archive state for individual rows. Rows that have been marked as archived stay where they are, but are not visible, unless the session is enabled to see archived data. Enabling in-database archiving causes the addition of a system generated hidden column. By default, this column is populated with the value '0' – making that row visible to applications (these row are active and visible via a standard query). To make rows invisible to applications, simply update the system generated hidden column with the value '1' -- making those records invisible to applications. In-database archiving utilizes an added hidden column to a table to specify the visibility of each row – and then for every query and DML, Oracle injects a predicate to limit the query/DML to only the visible rows.

Conclusion


Heat Map and Automatic Data Optimization make Oracle Database ideal for implementing ILM. There are no specialized data stores to manage, they operate independent of any hardware and they have proven performance benefits. Heat Map and Automatic Data Optimization provide total flexibility for the automated management of data compression and movement – enabling organizations to easily adapt to changes in data usage and data retention requirements. Flashback Data Archive provides a centralized, and seamlessly queryable, historical data store and In-Database Archiving allows users and applications to set the archive state for individual rows. Together, all these features support the evolution of an organization's Information Lifecycle Management strategy.


Key Benefits

  • Heat Map automatically tracks modification and access at the row and segment levels.
  • ADO automatically moves, and compresses, data according to user-defined policies based on Heat Map gathered information.
  • ADO enables automated storage tiering and compression tiering.
  • Flashback Data Archive securely tracks and stores transactional changes to a table over its lifetime.
  • In-Database Archiving archives rows in-place, rows that have been marked as archived stay where they are but are not visible unless the session is enabled to see archived data.

White Papers and Customer Case Studies

red-arrow-box Heat Map and Automatic Data Optimization
red-arrow-box Flashback Data Archive
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red-arrow-box Case Study: Yapı Kredi Bank uses Automatic Data Optimization for ILM
red-arrow-box ADO Case Study: Korea Deposit Insurance Corporation
red-arrow-box ADO Case Study: Hyosung FMS Inc.
red-arrow-box Implementing ILM with Oracle Database
red-arrow-box Using SAP NetWeaver with Oracle Database Automatic Data Optimization and Information Lifecycle Management

eSeminar & Webcasts

red-arrow-box Oracle Database 12c Storage Optimization - Karen Ambrose, Wellcome Trust Sanger Institute

Related Technologies

red-arrow-box Advanced Compression

Database Storage Management

red-arrow-box Oracle

Discussion Forums/Blogs

red-arrow-box Advanced Compression
red-arrow-box ADO/Heat Map
red-arrow-box Database Storage Optimization Blog
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