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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 K landing directory

• Access to Power BI (see section below)

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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 whl via Platform Settings → Sources → Download Collectors

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Run the following command to install the collector.

pip install kada_collectors_extractors_powerbi-3.1.0-py3-<version>-none-any.whl

You will also need to install the common library kada_collectors_lib -1.0.2 for this collector to function properly.

pip install kada_collectors_lib-1.0.2-py3<version>-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

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

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.

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

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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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