The Phoenix Information Repository

An efficient, structured, searchable environment for managing and storing digital assets, the Phoenix Information Repository is the heart of Phoenix. By ingesting eligible data assets and associated metadata into secondary storage, and managing them, the Information Repository maximizes primary storage and reduces management burdens. Phoenix is the answer to your digital asset management requirements. 

Within the Information Repository, ingested digital assets and metadata is stored on media that is housed within vaults. Digital assets are managed utilizing Data Service Polices that automatically store, process, and preserve ingested data in according to its service level requirements and recovery time objectives.

Vaults

Vaults are central to the Phoenix Information Repository. They are independent, self-contained storage resources deployed throughout the Information Repository, which utilize media of various types. A vault comprises a host processor, resources for the various metadata catalogues, and a mass storage device used for data storage.

Vaults are automatically recognized as viable storage resources as soon as they come on line, so they can be added, moved, or removed from a configuration without serious management intervention.

Media

Phoenix vaults can utilize a wide variety of media technologies to ensure that data retrieval time objectives (RTO) and storage longevity requirements are met. Supported media types include all types of hard disk, most digital tape technologies, as well as magneto optical (MO) and DVD glass media.

Media may be utilized on an individual basis, or several media units may be grouped together to form pools. Media pools can span vaults and can even comprise multiple media technologies, which provides resource fail-over and other capabilities.

Metadata Processing

As data is ingested into the  Information Repository, files are assigned an identification (ID) and a digital fingerprint. Next, context metadata is extracted from the data and indexed into a catalog, which reside both in the vault and on the media storing the data.

Context metadata, such as file name, ownership information, permissions, etc., and content information in the form of key words and phrases are used for filtering parameters within Data Service Policies and during search and discovery processes.

Component-assigned Processing

The level of metadata extracted during ingest is managed via a tunable parameter. Processes such as metadata extraction and de-duplication can be part of Data Service Policies. This provides a great deal of flexibility and efficiency to a Phoenix solution by enabling CPU intensive processing to be conducted only where it is needed within the Information Repository. For example, in cases where network impact needs to be kept to an absolute minimum, Phoenix can be set to extract only a minimal amount of metadata during ingest. Once in the Information Repository, data can be consolidated to media where additional metadata, such as content, is then extracted and stored for use in policy filtering at the vault or media level.

Data Service Policies manage data throughout the Information Repository. They may be very simple or highly complex, depending on the requirements of the data being managed. A very simple Data Service Policy might be used to move data initially stored on a disk-based vault to one based on tape, as time or access level parameters are met. More complex Data Service Policies can be deployed to migrate information through a hierarchy of storage that continually meets data's access performance, RTO, storage longevity, and other service level requirements. Filters can be utilized to work within classification and consolidation policies that move information to pre-defined storage pools. Policies may also be designed to perform end of life operations on data that is no longer relevant.

Benefits provided by the Phoenix Information Repository:

• Digital Asset management
• Protective storage of network data
• Digital fingerprinting
• Indexing of stored data for easy searching and retrieval
• Virtual tiered structuring of data for hierarchical data migration and replication
• Automatic elimination of eligible data