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Power BI (via Collector method) - v3.1.0

Owned by Chichi
Last updated: Mar 26, 2024 by Ben
4 min read

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About Collectors

Collector Method

Pre-requisites

  • Access to Power BI (see section below)

 

Power BI access

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

 

 

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

SUPPORTED VERSION

DESCRIPTION

EXAMPLE

FIELD

FIELD TYPE

SUPPORTED VERSION

DESCRIPTION

EXAMPLE

client

string

2.0.0+

Onboarded client in Azure to access powerbi

 

secret

string

2.0.0+

Onboarded client secret in Azure to access powerbi

 

tenant

string

2.0.0+

Tenant ID of where powerbi exists

 

output_path

string

2.0.0+

Absolute path to the output location where files are to be written

“/tmp/output”

mask

boolean

2.0.0+

To enable masking or not

true

timeout

integer

2.0.0+

Timeout in seconds allowed against the powerbi APIs, for slower connections we recommend 30, default is 20

20

filter_flag

boolean

2.1.0+

Enable or disable filtering workspaces based on filter_workspaces

false

filter_workspaces

list<string>

2.1.0+

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”]

mapping

JSON

2.0.0+

Add the mapping for each data source in Power BI to a data source that is loaded into K. You will need to map the data source name in Power BI to the data source host name in K (these can be found on the Sources page)

Skip any data sources are not onboarded in K (these will be loaded in as references until the source is added to K).

See Host / Database Mapping for more details

Where somehost is the alternate name created in Power BI for the Analytics database that has been onboarded to K

{ "somehost.adw": "analytics.adw" }

compress

boolean

3.0.0+

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

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