Power BI (via Collector method) - v3.2.0

About Collectors

Collector Method

Pre-requisites

Collector Server Minimum Requirements

PowerBI Requirements

Access to Power BI
Follow the steps in PowerBI to setup a Service Principal with access to Power BI.
- You will need for the setup
  - Application (client) ID
  - Directory (tenant) ID
  - Secret Value

Lineage limitations in regards to Dataset Fields to Pages, lineage for this is dependant on the ability to export the PowerBI Report to analyse the pbix file. If we are unable to download the pbix file, this lineage will be missing for that report.
Download a report from the Power BI service to Power BI Desktop - Power BI

Step 1: Create the Source in K

Create a Power BI source in K

Go to Settings, Select Sources and click Add Source
Select “Load from File” option
Give the source a Name - e.g. PowerBI Production
Add the Host name for the PowerBI Server
Click Finish Setup

Step 2: Getting Access to the Source Landing Directory

Collector Method

Step 3: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.

You can download the latest Core Library and whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector.

pip install kada_collectors_extractors_<version>-none-any.whl

You will also need to install the common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_lib-<version>-none-any.whl

Step 4: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from Power BI

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE
client	string	Onboarded client in Azure to access powerbi
secret	string	Onboarded client secret in Azure to access powerbi
tenant	string	Tenant ID of where powerbi exists
output_path	string	Absolute path to the output location where files are to be written	“/tmp/output”
mask	boolean	To enable masking or not	true
timeout	integer	Timeout in seconds allowed against the powerbi APIs, for slower connections we recommend 30, default is 20	20
export_timeout	integer	Timeout in seconds allowed against the powerbi export pbix APIs, we recommend not setting this lower than 120	120
mapping	JSON	Mapping file of data source names against the onboarded host and database name in K	Assuming I have a “myDSN” data source name in powerbi, I’ll map it to host “myhost” and database “mydatabase” onboarded in K, snowflake type references are handled automatically `{ "myDSN": { "host": "myhost", "database": "mydatabase" } }`
filter_flag	boolean	Should we be filtering out workspaces based on filter_workspaces as a whitelist?	false
filter_workspaces	list<string>	List of workspace names that should be processed, this is case insensitive. Note that personal workspaces are excluded globally and will never be included even if you include it here.	[“data lab”, “analysis models”]
compress	boolean	To gzip the output or not	true

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below.

kada_powerbi_extractor_config.json

Step 5: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

This can be executed in any python environment where the whl has been installed.

This code sample uses the kada_powerbi_extractor_config.json for handling the configuration details

Advance options:

If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information Collector Integration General Notes | Storing HWM in another location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information Collector Integration General Notes | The run method

client: The Client ID for the registered application for access to PowerBI APIs
secret: The Secret for the registered application for access to PowerBI APIs
tenant: The Tenant ID for the registered application for access to PowerBI APIs
mapping: Dict of DNS to database and hostnames
filter_flag: Should we be filtering out workspaces based on filter_workspaces as a whitelist
filter_workspaces: whitelist of workspaces
timeout: Timeout for the API call
export_timeout: Timeout in seconds for exporting pbix files, defaults to 120 seconds
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not

Step 6: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

High Water Mark File

A high water mark file is created in the same directory as the execution called powerbi_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

Step 7: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

Example: Using Airflow to orchestrate the Extract and Push to K

Collector Method