The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 27, 2001

Filed:

Jul. 15, 1998
Applicant:
Inventors:

Sankaran Suresh, Santa Clara, CA (US);

Jyotindra Pramathnath Gautam, Fremont, CA (US);

Girish Pancha, San Francisco, CA (US);

Frank Joseph DeRose, Fremont, CA (US);

Mohan Sankaran, Union City, CA (US);

Assignee:

Informatica Corporation, Palo Alto, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 1/730 ;
U.S. Cl.
CPC ...
G06F 1/730 ;
Abstract

A computer software architecture to automatically optimize the throughput of the data extraction/transformation/loading (ETL) process in data warehousing applications. This architecture has a componentized aspect and a pipeline-based aspect. The componentized aspect refers to the fact that every transformation used in this architecture is built up with transformation components selected from an extensible set of transformation components. Besides simplifying source code maintenance and adjustment for the data warehouse users, these transformation components also provide these users the building blocks to effectively construct pertinent and functionally sophisticated transformations in a pipelined manner. Within a pipeline, each transformation component automatically stages or streams its data to optimize ETL throughput. Furthermore, each transformation either pushes data to another transformation component, pulls data from another transformation component, or performs a push/pull operation on the data. Thereby, the pipelining; staging/streaming; and pushing/pulling features of the transformation components effectively optimizes the throughput of the ETL process.


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