Power BI (via Collector method) - v3.1.0

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About Collectors

Collector Method

Pre-requisites

Python 3.6 - 3.10
Access to K landing directory

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

{
    "client": "",
    "secret": "",
    "tenant": "",
    "output_path": "/tmp/output",
    "mask": true,
    "timeout": 20,
    "filter_flag": true,
    "filter_workspaces": [],
    "mapping": {
        "myDSN": {
            "host": "myhost",
            "database": "mydatabase"
        }
    },
    "compress": true
}

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

import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.powerbi import Extractor

get_generic_logger('root') # Set to use the root logger, you can change the context accordingly or define your own logger

_type = 'powerbi'
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, 'kada_{}_extractor_config.json'.format(_type))

parser = argparse.ArgumentParser(description='KADA PowerBI Extractor.')
parser.add_argument('--config', '-c', dest='config', default=filename, help='Location of the configuration json, default is the config json in the same directory as the script.')
args = parser.parse_args()

start_hwm, end_hwm = get_hwm(_type)

ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})

publish_hwm(_type, end_hwm)

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