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Synchronizing Analytics

This chapter includes the following topics:

 


Overview of Analytics Synchronization

Analytics synchronization is a process by which object dimension data is sent from the remote database to the dimension tables of the Analytics database, where the data is stored. Dimension data is transferred through a JDBC connection.

If you are using Streaming Portal Mode, use the command line to run Analytics sync jobs. To schedule jobs, create scheduled tasks (Windows) or cron jobs (UNIX/Linux). An Analytics sync job exists for each type of portal object. For the commands you use to run sync jobs, see Analytics Sync Jobs. For synchronization scheduling guidelines, see Strategizing Synchronization Scheduling.

You can find details of sync jobs in the portal's job history log or in Analytics's sync.log file. For the location of the Analytics sync.log file, see Overview of Logs.

Analytics Sync Jobs

Analytics is delivered with sync jobs that synchronize your portal's objects with the Analytics database. You can either run all three of the sync jobs at the same time using one command, or run each sync job separately. Separate sync jobs exist for synchronizing desktop, page, and portlet objects with Analytics.

Analytics Sync Jobs (Windows)

To synchronize desktop, page, and portlet objects at the same time, type the following command in the Windows Command Prompt:

Following are the commands you use to run each sync job separately:

To schedule a sync job, create a scheduled task and use one of the above commands appropriate to the sync job you are scheduling.

Analytics Sync Jobs (UNIX/Linux)

To synchronize desktop, page, and portlet objects at the same time, type the following command in the command line:

Following are the commands you use to run each sync job separately:

To schedule a sync job, create a cron job and use one of the above commands appropriate to the sync job you are scheduling.

 


Strategizing Synchronization Scheduling

Because some dimension data in you environment might change more frequently than other dimension data, we recommend that you adopt a job scheduling strategy that reflects your environment's unique circumstances.

Following are some examples of scheduling strategies that you can adopt:


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