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Oracle Data Integrator (ODI) is an extract, load, transform (ELT) tool (in contrast with the ETL common approach) produced by Oracle that offers a graphical environment to build, manage and maintain data integration processes in business intelligence systems.
In February 2007, Oracle released Oracle E-Business Suite (EBS/e-BS) Release 12 (R12) – a bundling of several Oracle Applications. The release date coincided with new releases of other Oracle-owned products: JD Edwards EnterpriseOne, Siebel Systems and PeopleSoft.
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
Oracle Warehouse Builder was built from the ground up in Oracle, it was first released in January 2000 (release 2.0.4). The 3i release significantly enhanced the ETL mapping designer, then 9i in 2003 introduced the mapping debugger, process flow editing, integrated match/merging and name/address cleansing, multi-table insert, scripting, RAC certification to name a few.
An E-8 crew member entering data using an AN/PYQ-10 before a flight. The AN/PYQ-10 Simple Key Loader (SKL) is a ruggedized, portable, hand-held fill device, for securely receiving, storing, and transferring data between compatible cryptographic and communications equipment.
Oracle Database (commonly referred to as Oracle DBMS, Oracle Autonomous Database, or simply as Oracle) is a proprietary multi-model [4] database management system produced and marketed by Oracle Corporation. It is a database commonly used for running online transaction processing (OLTP), data warehousing (DW) and mixed (OLTP & DW) database ...
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed.
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format. This enables faster loading times.