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About Collectors
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Pre-requisites
Python 3.6 - 3.10
Access to the KADA Collector repository that contains the PowerBI whl
The repository is currently hosted in KADA’s Azure Blob Storage. You will be given a SAS token to access the repository. Reach out to KADA Support (support@kada.ai) if you do not have access.
Download the Power BI whl (e.g. kada_collectors_extractors_powerbi-#.#.#-py3-none-any.whl)
Access to Power BI (see section below)Access to K landing directory
Power BI access
Follow the steps in PowerBI to setup a Service Principal with access to Power BI.
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Application (client) ID
Directory (tenant) ID
Secret Value
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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
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Step 2: Getting Access to the Source Landing Directory
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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 Power BI whl via Platform Settings → Sources → Download Collectors
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Run the following command to install the collector
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pip install kada_collectors_extractors_powerbi-#.#.#-py3-none-any.whl |
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Step 4: Configure the Collector
The collector requires a set of parameters to connect to and extract metadata from Power BI
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{
"client": "",
"secret": "",
"tenant": "",
"output_path": "/tmp/output",
"mask": true,
"timeout": 20,
"filter_flag": false,
"filter_workspaces": [],
"mapping": {
"myDSN": {
"host": "myhost",
"database": "mydatabase"
}
}
} |
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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.
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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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#The-run-method
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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
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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.
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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)
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Example: Using Airflow to orchestrate the Extract and Push to K
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