Abstract: Embodiments relate to data archiving utilizing an existing big data platform (e.g., HADOOP) as a cost-effective target infrastructure for storage. Particular embodiments construct a logical structure (hereafter, “vault”) in the big data platform so that a source, type, and context of the data is maintained, and metadata can be added to aid searching for snapshots according to a given time, version, and other considerations. A vaulting process transforms relationally stored data in an object view to allow for object-based retrieval or object-wise operations (such as destruction due to legal data privacy reasons), and provide references to also store unstructured data (e.g., sensor data, documents, streams) as attachments. A legacy archive extractor provides extraction services for existing archives, so that extracted information is stored in the same vault. This allows for cross queries over legacy data and data from other sources, facilitating the application of new analysis techniques by data scientists.

Abstract: Archiving may be integrated into an organization's existing data management systems instead of being configured as a separate system. This integration may occur by adding an archiving table to an existing database of the organization containing data that is to be archived. The archiving table may include multiple records, each record storing a binary large object representing a complete, self-contained archive of the organization's data at a preselected time. Incorporating archiving and archived data into the organization's existing data management systems in this manner may eliminate the need for a dedicated archiving system and may eliminate synchronization and interconnectivity issues with an independent archiving system.

Abstract: In one embodiment, a method determines data stored in a plurality of tables in a first database for archiving. The data is combined from the plurality of tables into a set of objects in a readable format where an object in the set of objects includes terms from the plurality of tables associated with the object. The method then stores the set of objects in the text format in a second database where a search query processor is able to search terms in the set of objects in the readable format. Then, the data stored in the plurality of tables in the first database is deleted in response to the archiving.

Abstract: Embodiments relate to data archiving utilizing an existing big data platform (e.g., HADOOP) as a cost-effective target infrastructure for storage. Particular embodiments construct a logical structure (hereafter, “vault”) in the big data platform so that a source, type, and context of the data is maintained, and metadata can be added to aid searching for snapshots according to a given time, version, and other considerations. A vaulting process transforms relationally stored data in an object view to allow for object-based retrieval or object-wise operations (such as destruction due to legal data privacy reasons), and provide references to also store unstructured data (e.g., sensor data, documents, streams) as attachments. A legacy archive extractor provides extraction services for existing archives, so that extracted information is stored in the same vault. This allows for cross queries over legacy data and data from other sources, facilitating the application of new analysis techniques by data scientists.

Abstract: In one embodiment, a method determines data stored in a plurality of tables in a first database for archiving. The data is combined from the plurality of tables into a set of objects in a readable format where an object in the set of objects includes terms from the plurality of tables associated with the object. The method then stores the set of objects in the text format in a second database where a search query processor is able to search terms in the set of objects in the readable format. Then, the data stored in the plurality of tables in the first database is deleted in response to the archiving.

Abstract: Archiving may be integrated into an organization's existing data management systems instead of being configured as a separate system. This integration may occur by adding an archiving table to an existing database of the organization containing data that is to be archived. The archiving table may include multiple records, each record storing a binary large object representing a complete, self-contained archive of the organization's data at a preselected time. Incorporating archiving and archived data into the organization's existing data management systems in this manner may eliminate the need for a dedicated archiving system and may eliminate synchronization and interconnectivity issues with an independent archiving system.