Running the Microsoft Fabric collector on-premise
Note
The latest version of the Collector is 2.275. To view the release notes for this version and all previous versions, please go here.
Generating the command or YAML file
This section walks you through the process of generating the command or YAML file for running the collector from Windows or Linux or MAC OS.
To generate the command or YAML file:
On the Organization profile page, go to the Settings tab > Metadata collectors section.
Click the Add a collector button.
On the Choose metadata collector screen, select the correct metadata source. Click Next.
On the Choose where the collector will run screen, in the On-premise section, select if you will be running the collector on Windows or Mac OS or Linux. This will determine the format of the YAML and CLI that is generated in the end. Click Next.
On the On-prem collector setup prerequisites screen, read the pre-requisites and click Next.
On the Configure an on-premises Microsoft Fabric Collector screen, set the following properties and click Next.
On the next screen, set the following properties and click Next.
Table 2.Field name
Corresponding parameter name
Description
Required?
Microsoft Entra client ID
--client-id
The client ID of the registered application in Microsoft Entra.
Yes
Microsoft Entra client secret
--client-secret
The client secret for the registered application in Microsoft Entra.
Yes
Microsoft Entra Tenant ID
--tenant-id
The tenant ID that identifies the organization in Microsoft Entra.
Yes
On the next screen, set the following Power BI options and click Next.
Table 3.Field name
Corresponding parameter name
Description
Required?
Maximum Power BI Expression Length
--max-parseable-expression-length
Set the maximum number of characters in a Table expression (coming from Semantic Models and Dataflows) that will be parsed for lineage metadata. Expressions longer than this will be skipped. Default is 32000.
Datasource Name Mapping File
--datasource-mapping-file
Provide the location of the file, if you have configured source details in the datasources.yml file.
Note: You should have placed your datasources.yml file in the source directory of your host machine. The value in this field is relative to the mount location of the container (target). For example, if you have a mount target set to /dwcc-output, the value for --datasource-mapping-file will be /dwcc-output/datasources.yaml.
Skip harvesting lineage metadata
--disable-lineage-collection
Skip harvesting of intra-database lineage metadata. This applies only to database resources such as views.
Catalog report preview images
--image-collection
Specify if the collector should catalog preview images. The default setting is false. Ensure that you have met all pre-requisites for using this feature.
Disable max requests wait
--disable-max-requests-wait
Disable waiting up to an hour for the Power BI API endpoints to reset throttling limits (error code 429 - too many requests). When not disabled, the collector retries every 5 minutes for up to an hour. If this option is disabled, the Max retries and Retry delay options will be used instead.
On the next screen, set the following advanced properties and click Next.
Table 4.Field name
Corresponding parameter name
Description
Required?
Collect extended properties for SQL Database Resources
--collect-extended-properties
Harvest information about extended properties from SQL Server type databases.
No
JDBC properties
--jdbc-property
JDBC driver properties to pass through to driver connection, as name=value. Use the parameter multiple times for multiple properties. For example, . --jdbc-property property1=value1 --jdbc-property property2=value2
Note: by default the collector uses authentication=ActiveDirectoryServicePrincipal, and encrypt=true
Yes
Schemas to collect
Select from one of the following options:
Collect all schema, Specify which schemas to collect
Yes
Collect all schema
-A
--all-schemas
Catalog all schemas to which the user has access.
Yes
(if --schema is not set)
Specify which schemas to collect
-S= <databaseSchema>
--schema=<databaseSchema>
Select this option to specify the names of the database schema to be catalog.
Yes
(if --all-schema is not set)
Exclude Schema
--exclude-schema
Specify the name or regular expression of the database schema to be excluded. Applicable only if --all-schemas is specified.
No
Include Information Schema
--include-information-schema= <true/false>
Include the database's Information Schema in catalog collection.
No
On the next screen, set the following properties and click Next.
Important
If you are using Catalog Toolkit, make sure you select the relevant module for data profiling to use this feature.
Table 5.Field name
Corresponding parameter name
Description
Required?
Enable column statistics collection
--enable-column-statistics
To enable harvesting of column statistics (i.e., data profiling).
Note: Activating the profiling feature may extend the running time of the collector. This is because the collector needs to read the table data to be able to gather metadata for profiling.
No
Target sample size for column statistics
--target-sample-size
The number of rows sampled for computation of column statistics and string-value histograms.
No
Disable lineage collection
--disable-lineage-collection
Skip harvesting of intra-database lineage metadata.
No
Disable extended metadata collection
--disable-extended-metadata
Skip harvesting of extended metadata for resource types such as database, schema, table, columns functions, stored procedures, user defined types, synonyms. Basic metadata for these resource types will still be harvested.
No
Enable sample string values collection
--sample-string-values
Enable sampling and storage of sample values for string-valued columns.
No
Exclude system functions
--exclude-system-functions
Specify to exclude harvesting of built-in system functions.
No
Max retries
--api-max-retries
Specify the number of times to retry an API call which has failed. The default value is 5.
No
Retry delay
-api-retry-delay
Specify the amount of time in seconds to wait between retries of an API call which has failed. The default is to try with a delay of 2 seconds between each call.
No
On the next screen, provide the Collector configuration name. This is the name used to save the configuration details. The configuration is saved and made available on the Metadata collectors summary page from where you can edit or delete the configuration at a later point. Click Save and Continue.
On the Finalize your Microsoft Fabric Collector configuration screen, you are notified about the environment variables and directories you need to setup for running the collector. Select if you want to generate a Configuration file( YAML) or Command line arguments (CLI). Click Next
Important
You must ensure that you have set up these environment variables and directories before you run the collector.
The next screen gives you an option to download the YAML configuration file or copy the CLI command. Click Done. If you are generating a YAML file, click Next.
The Microsoft Fabric command screen gives you the command to use for running the collector using the YAML file.
You will notice that the YAML/CLI has following additional parameters that are automatically set for you.
Warning
Except for the collector version, you should not change the values of any of the parameter listed here.
Table 6.Parameter name
Details
Required?
-a= <agent>
--agent= <agent>
--account= <agent>
The ID for the data.world account into which you will load this catalog - this is used to generate the namespace for any URIs generated.
Yes
--site= <site>
This parameter should be set only for Private instances. Do not set it for public instances and single-tenant installations. Required for private instance installations.
Yes (required for private instance installations)
-U
--upload
Whether to upload the generated catalog to the organization account's catalogs dataset.
Yes
--no-log-upload
Do not upload the log of the collector run to a dataset in data.world. This is the same dataset where the collector output is uploaded. By default, log files are uploaded to this dataset.
Yes
dwcc: <CollectorVersion>
The version of the collector you want to use (For example,
datadotworld/dwcc:2.248)Yes
Add the following additional parameter to test run the collector.
--dry-run: If specified, the collector does not actually harvest any metadata, but just checks the connection parameters provided by the user and reports success or failure at connecting.
We recommend enabling debug level logs when running the collector for the first time. This approach aids in swiftly troubleshooting any configuration and connection issues that might arise during collector runs. Add the following parameter to your collector command:
-e log_level=DEBUG: Enables debug level logging for collectors.
Verifying environment variables and directories
Verify that you have set up all the required environment variables that were identified by the Collector Wizard before running the collector. Alternatively, you can set these credentials in a credential vault and use a script to retrieve those credentials.
Verify that you have set up all the required directories that were identified by the Collector Wizard.
Running the collector
Important
Before you begin running the collector make sure you have completed all the pre-requisite tasks.
Running collector using YAML file
Go to the machine where you have setup docker to run the collector.
Place the YAML file generated from the Collector wizard to the correct directory.
From the command line, run the command generated from the application for executing the YAML file.
Caution
Note that is just a sample command for showing the syntax. You must generate the command specific to your setup from the application UI.
docker run -it --rm --mount type=bind,source=${HOME}/dwcc,target=/dwcc-output \ --mount type=bind,source=${HOME}/dwcc,target=/app/log -e DW_AUTH_TOKEN=${DW_AUTH_TOKEN} \ -e CLIENT_ID=${CLIENT_ID} -e DW_MICROSOFT_FABRIC_SECRET=${DW_MICROSOFT_FABRIC_SECRET} \ -e TENANT_ID=${TENANT_ID} datadotworld/dwcc:2.271 --config-file=/dwcc-output/config-microsoft_fabric.ymlIf you are running the collector using Jar files, be sure to edit the command as instructed on this page.
The collector automatically uploads the file to the specified dataset and you can also find the output at the location you specified while running the collector. Similarly, the log files are uploaded to the specified dataset and can be found in the directory mounted to target=/app/log specified in the command.
If you decide in the future that you want to run the collector using an updated version, simply modify the collector version in the provided command. This will allow you to run the collector with the latest version.
Running collector without the YAML file
Go to the machine where you have setup docker to run the collector.
From the command line, run the command generated from the application. Here is a sample command.
Caution
Note that is just a sample command for showing the syntax. You must generate the command specific to your setup from the application UI.
docker run -it --rm --mount type=bind,source=${HOME}/dwcc,target=/dwcc-output \ --mount type=bind,source=${HOME}/dwcc,target=/app/log datadotworld/dwcc:2.271 \ catalog-microsoft-fabric --collector-metadata=config-id=f7bef99a-8c01-492c-9af6 \ --agent=initech --api-token=${DW_AUTH_TOKEN} --upload=true --name="Microsoft Fabric Collection" \ --output=/dwcc-output --upload-location=ddw-catalogs --client-id=${CLIENT_ID} \ --client-secret=${DW_MICROSOFT_FABRIC_SECRET} --tenant-id=${TENANT_ID}If you are running the collector using Jar files, be sure to edit the command as instructed on this page.
The collector automatically uploads the file to the specified dataset and you can also find the output at the location you specified while running the collector. Similarly, the log files are uploaded to the specified dataset and can be found in the directory mounted to target=/app/log specified in the command.
If you decide in the future that you want to run the collector using an updated version, simply modify the collector version in the provided command. This will allow you to run the collector with the latest version.
Automating updates to your metadata catalog
Maintaining an up-to-date metadata catalog is crucial and can be achieved by employing Azure Pipelines, CircleCI, or any automation tool of your preference to execute the catalog collector regularly.
There are two primary strategies for setting up the collector run times:
Scheduled: You can configure the collector according to the anticipated frequency of metadata changes in your data source and the business need to access updated metadata. It's necessary to account for the completion time of the collector run (which depends on the size of the source) and the time required to load the collector's output into your catalog. This could be for instance daily or weekly. We recommend scheduling the collector run during off-peak times for optimal performance.
Event-triggered: If you have set up automations that refresh the data in a source technology, you can set up the collector to execute whenever the upstream jobs are completed successfully. For example, if you're using Airflow, Github actions, dbt, etc., you can configure the collector to automatically run and keep your catalog updated following modifications to your data sources.
Managing collector runs and configuration details
From the Metadata collectors summary page, view the collectors runs to ensure they are running successfully,
From the same Metadata collectors summary page you can view, edit, or delete the configuration details for the collectors.