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Attic WindowIn order to manage the data warehouse, which is primarily used by reporting, you must perform maintenance tasks on data warehouse jobs.

For example, you can view their status, pause and resume, set a schedule, enable and disable schedules, and troubleshoot data warehouse jobs. You can perform all of these maintenance tasks by using Windows PowerShell cmdlets.

In addition, you can perform some of these tasks through the Service Manager console. As a result of that action, management pack deployment started and MPSyncJob started.

There are seven data warehouse jobs that run at various times to maintain the data warehouse, as listed in the following table. The schedule for a job defines when a job starts. Frequency refers to how Load Data Resume Yes the job runs after Load Data Resume Yes has started. Regardless of schedule and frequency, a job does not run unless the schedule for that job has been enabled. Except for the Entity Grooming job, each job has a default scheduled start time, which is midnight. The following table lists the scheduled start time, frequency, and default schedule setting.

In this release of Service Manager, grooming functions are handled as a workflow. Settings for this job are not configurable. The Service Manager Windows PowerShell module contains cmdlets that are used in this scenario to manage data warehouse functions just click for source the server that hosts the data warehouse.

You must run all Windows PowerShell cmdlets as an administrator. To view the Windows PowerShell Help, type the get-help command, followed by the name of the cmdlet for which you want help. The following cmdlets are used in this scenario:. Job schedules are disabled by default. This job can take several hours to complete its initial run.

When this job is complete, you can see two extract jobs listed in the Data Warehouse Jobs pane. When both of these extract jobs appear, you know that the initial run of the MPSyncJob is complete and that you can now proceed with the subsequent maintenance tasks.

Data warehouse module deployment in Service Manager starts when a Service Manager management server is registered to a data warehouse management server. The following sections describe module parts, functions, and schedule. Management pack synchronization is the process by which the data warehouse discovers what classes and relationships exist in source systems. This process is also referred to as MPSync.

For every management pack that defines a class or relationship, the data warehouse creates extract job modules to retrieve the data for that class or relationship from the corresponding source.

Such management packs and their associated jobs are synchronized between the systems.

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Only sealed management packs, and their corresponding data, are synchronized into the data warehouse. If you alter a management pack, you must increase the version number and you cannot introduce any changes that might cause errors; otherwise, the management pack will fail to import.

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For example, you cannot remove classes, remove properties, or remove relationships. Similarly, you cannot change data types in unsupported ways. For example, you cannot modify a string property to become a numeric property.

It is possible Load Data Resume Yes multiple sources may refer to the same management pack. The version in the source system must be the same or higher version than that in the data warehouse, otherwise registration will fail.

It is possible to remove management packs from the data warehouse. However, keep the following points in mind:. If you reimport a management http://uht.me/essay-help/i-want-a-wife-thesis.php after you have removed the corresponding management pack, the historical data is exposed once again. Only sealed management packs are synchronized from Service Manager to the data warehouse.

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An exception to this is list items, also known as enumerations. Groups or queues are synchronized to the data warehouse, regardless of whether they are in a sealed or unsealed management pack.

Management packs that are imported from Service Manager are Service Manager-specific and data warehouse specific. The Service Manager management packs provide awareness of what the Service Manager database is structured like, and the data warehouse management packs drive the structure and processes of the data warehouse databases. The management pack synchronization process imports management packs from Service Manager, and it defines how those management packs shape the structure, move the data, and copy reports for the data warehouse and reporting.

After those management packs are synchronized between Service Manager and the data warehouse, the data is retrieved and reports are deployed for user consumption. Management packs that contain only Service Manager-specific information do not cause the deployment activities to execute.

They are only be triggered for new data warehouse and reporting-specific elements. After the data warehouse schema and reports are deployed, the DWDataMart database is populated with actual data for reporting purposes. This is done by the ETL processes. These three processes Load Data Resume Yes serve their own specific purpose:.

One of the main reasons for having three different databases is so that you can optimize your hardware environment more easily. At a high level, ETL occurs in the processes described in the following sections. If you plan on authoring management packs that are used for custom reporting, you will probably need to know more about these processes in depth.

The extract process starts on a scheduled interval.

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Extract is the process that retrieves raw data from your online transaction processing system OLTP store, which in this case is the Service Manager database. The transform process starts on a scheduled interval. Transform is the process that moves the raw data from the DWStagingandConfig database. It also does any cleansing, reformatting, and aggregation that is required to alter the raw data into the final format for reporting. This transformed data is written into the DWRepository database.

The load process starts on a scheduled interval. The load process queries for the data from the DWRepository database. The DWDatamart is the database that is used for all end-user reporting needs. By default, data is stored in the data warehouse for 3 years for fact tables and for an unlimited period for dimension and outrigger tables. However, you can modify the retention period if you want to retain data longer or groom it out more aggressively.

The default global retention period for data stored in the Service Manager data warehouse is 3 years, so all fact tables use 3 years as the default retention setting. Any subsequently-created fact Load Data Resume Yes use this setting when created for their individual retention setting. Individual fact tables inherit the global retention value when created, or you can customize them to a value that differs from the default global setting.

You can configure the default individual fact tables that were created during installation, individually with a specific retention value as needed. During development and testing of management packs that contain reports that access data warehouse information, you might need to remove the management packs and then reimport them later.

However, after a management pack is uninstalled from the data warehouse, if the new management pack contains the same dimension, Load Data Resume Yes, or cube name Load Data Resume Yes a schema that is different from the original, you must delete the dimension or fact table from the DWRepository and DWDataMart databases manually and also delete any referencing cube from the SQL Server Analysis Services SSAS database.

In addition, if a dimension or fact is already referenced by an existing click cube, you must also delete the management pack that contains the data cube and the data cube itself before Load Data Resume Yes the new management pack.

Because Service Manager does not remove the dimension or fact table from the DataSourceView and because dimensions are not removed from SSAS database, you must manually delete information that a data cube references. In this situation, you should use SQL Server Management Studio to remove any custom data cube that you created with the management pack from the DWASDatabase before you reregister or reinstall an updated management pack.

In general, you should avoid having the same dimension, fact, and cube name in differing schemas.

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Service Manager does not support this condition. Use the following procedure to enable the schedule for the ETL jobs as needed; you can use this procedure to enable the schedule for any of the data warehouse jobs. By default, the schedules for the extract, transform, and load ETL jobs are enabled. You can use the following procedure to disable the schedule for the extract, transform, and load ETL jobs; however, you can use this procedure to disable the schedule for any data warehouse job.

In this release of Service Manager, you can disable the schedules only by using Load Data Resume Yes PowerShell cmdlets. You can stop and start data warehouse jobs that are running in Service Manager. For example, you might have to stop all of the data warehouse jobs that are running to ensure that a security update to the data warehouse management server does not interfere with any jobs that might run.

After the server has been updated and restarted, you resume all the data warehouse jobs. You can article source and then start jobs by using the Service Manager console or by using Windows PowerShell cmdlets.

In this example, only the extract, transform, and load ETL jobs are running. You could use this procedure in a here where a schedule for the data warehouse jobs has been defined in Service Manager.

You want to change the schedule for the data warehouse jobs to define standard maintenance windows for the Service Manager database and for the data warehouse. For example, the following Load Data Resume Yes define a daily or weekly schedule:. In the following procedure, you configure a schedule for the Transform job to run every 45 minutes, starting at 2: However, you can modify the commands to set your own schedule.

You can process all the dimensions in the data warehouse in one operation using Load Data Resume Yes PowerShell cmdlets, instead of processing each dimension individually.

Be sure to specify the fully qualified server name. You can type each command separately, or you can save them all as a Windows PowerShell script. A history of data warehouse jobs is collected as they run in Service Manager.

You can view this history to determine how long a job ran or to determine the last time the job ran successfully. When you display the data warehouse job history, you display the number of entries that you Load Data Resume Yes by using the NumberOfBatches parameter.

Use the following procedure to view the last five entries in the history of a data warehouse job. You can use the following procedures to view the status of a data warehouse job in Service Manager to determine whether a job is running, stopped, or failed.

In Service Manager, you may encounter problems related to data warehouse jobs. After the Data Warehouse Registration Wizard completes and after Reporting becomes available in the Service Manager console, you can start running reports.

If, for example, the incident management report you run doesn't show updated data, you can use Windows PowerShell cmdlets to troubleshoot the problem.

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