This page was last edited on September 3, 2019, at 20:09.
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Extract, Transform, And Load
Also known as ETL. The ETL processes extract data from various data sources; transform the data into a format and structure that is suitable for subsequent business purposes; and load the data into a target data store (other database, data mart, or data warehouse).
You can view information about job history and status using either of the following methods:
Use Voice of Process functionality to access information related to the processing history of Genesys Info Mart jobs, including to:
The Genesys Info Mart database provides several service control tables, which in addition to existing administrative views, store the ETL processing history details.
The ADMIN_ETL_JOB_HISTORY administrative view is updated each time a job is executed. By monitoring this view, administrators can quickly assess the current state of all jobs.
The ADMIN_ETL_JOB_HISTORY administrative view provides the following information related to the jobs:
A row is added to the ADMIN_EXTRACT_HISTORY administrative view when Job_ExtractICON successfully completes extracting a source data table. Administrators can closely track the progress of the data extract cycle by monitoring this view.
The ADMIN_EXTRACT_HISTORY provides the following information related to the data extraction job, including:
Administrators can closely track the progress of the data transform cycle by viewing the contents of the CTL_TRANSFORM_HISTORY table.
The CTL_TRANSFORM_HISTORY provides the following information related to the data transformation job:
In Info Mart’s dimensional model, every table that receives inserts has a CREATE_AUDIT_KEY service field. Every table that can also receive updates has an additional UPDATE_AUDIT_KEY service field. Both these fields contain a reference to a row in the CTL_AUDIT_LOG table.
By linking fact data to the CTL_AUDIT_LOG table, administrators can determine:
Additionally, fields CREATE_AUDIT_KEY and UPDATE_AUDIT_KEY can be used to identify newly arriving data for subsequent aggregation or other processing. For more information about any of the Info Mart tables, views, and fields, see the Physical Data Model for your RDBMS: