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About Collectors

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Pre-requisites

• Python 3.6 - 3.10

• Tableau Server Version [2019.3] and above.

• Enable the Tableau Metadata API for Tableau Server

  • This requires a server restart if not enabled

• Tableau API access

  • An API user (record the username and password) needs to be created to access Tableau API.

  • The user cannot be a SSO user. This is a Tableau limitation. SSO users cannot access Tableau API

  • User needs Site Administrator Creator or Server/Site Administrator role. Roles are dependent on both Licensing and Server version see https://help.tableau.com/current/server/en-us/users_site_roles.htm

    • Site Administrator Creator is only available on Role Based Licensing Model

    • Server/Site Administrator is available on both Role Based and Core Based Licensing Model

• Tableau Repository access

  • Follow the instructions to create a user that can access the Tableau repositoryhttps://help.tableau.com/current/server/en-us/perf_collect_server_repo.htm

    • Note the Tableau repository default user is calledreadonly

• Access to the KADA Collector repository

  • The repository is currently hosted in Azure Blob. 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 tableau whl (e.g. kada_collectors_extractors_tableau-#.#.#-py3-none-any.whl)

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Step 1: Create the Source in K

Create a Tableau source in K

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• Give the source a Name - e.g. Tableau Production

• Add the Host name for the Tableau server

• Click Finish Setup

• After the source is created.

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Step 2: Getting Access to the 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.

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pip install kada_collectors_extractors_tableau-2.0.0-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 Tableau.

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{
    "server_address": "",
    "username": "",
    "password": "",
    "sites": [],
    "db_host": "",
    "db_username": "readonly",
    "db_password": "",
    "db_port": 8060,
    "db_name": "workgroup",
    "meta_only": false,
    "retries": 5,
    "dry_run": false,
    "output_path": "/tmp/output",
    "mask": true,
    "mapping": {}
}

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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 the following 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 (metadata, log, roles 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 tableau_hwm.txt and produce files according to the configuration JSON. This file is only produced if you store the hight water mark in a file.

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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

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: Using Airflow to orchestrate the Extract and Push to K

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