Tableau Server (Collector method) - v2.0.0

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About Collectors

Collectors are extractors that are developed and managed by you (A customer of K).

KADA provides python libraries that customers can use to quickly deploy a Collector.

Why you should use a Collector

There are several reasons why you may use a collector vs the direct connect extractor:

You are using the KADA SaaS offering and it cannot connect to your sources due to firewall restrictions
You want to push metadata to KADA rather than allow it pull data for Security reasons
You want to inspect the metadata before pushing it to K

Using a collector requires you to manage

Deploying and orchestrating the extract code
Managing a high water mark so the extract only pull the latest metadata
Storing and pushing the extracts to your K instance.

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 https://help.tableau.com/current/api/rest_api/en-us/REST/rest_api_concepts_auth.htm
- 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 repository https://help.tableau.com/current/server/en-us/perf_collect_server_repo.htm
  - This requires a server restart if not enabled
  - Note the Tableau repository default user is calledreadonly
Access to K landing directory.

Step 1: Create the Source in K

Create a Tableau source in K

Go to Settings, Select Sources and click Add Source
Select “Load from File” option

Give the source a Name - e.g. Tableau Production
Add the Host name for the Tableau 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_tableau-2.0.0-py3-none-any.whl

Step 4: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from Tableau.

PARAMATER	TYPE	DESCRIPTION	EXAMPLE

PARAMATER	TYPE	DESCRIPTION	EXAMPLE
server_address	string	Tableau server address inclusive of http/https	“https://10.1.19.15”
username	string	Username to log into tableau api	“tabadmin”
password	string	Password to log into tableau api
sites	list<string>	List of specific sites that you wish to extract, if left as [] it will extract all sites.	[]
db_host	string	This is generally the same as server address less the http/https	“10.1.19.15”
db_username	string	By default the tableau database use is readonly should not need to change this unless you actively manage the database	“readonly”
db_password	list<string>	Password for the database user
db_port	integer	Default is 8060 unless your tableau is configured differently	8060
db_name	string	Default database to use is workgroup	“workgroup”
meta_only	boolean	If for some reason you want to extract meta only set this to true otherwise leave it as false	false
retries	integer	Number of retries that the extractor should hit the API incase of intermittent failures, default is 5	5
dry_run	boolean	By doing a dry run you produce the mapping.json file which is used to populate the mapping field below. It is recommended you do a dry run first to see what databases are available to map.	true
output_path	string	Absolute path to the output location where files are to be written	“/tmp/output”
mask	boolean	To enable masking or not	true
mapping	json	This should be populate with the mapping.json output where each data source name mentioned is mapped to an onboarded K host	Where analytics.adw is the onboarded database in K `{ "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.

kada_tableau_extractor_config.json

{
    "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": {}
}

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_tableau_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

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

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 tableau_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

server_address: server address
username: username to sign into server
password: password to sign into server
sites: list of sites to extract.
db_host: Tableau database address
db_password: Tableau database password
db_port: Tableau database port
db_name: Tableau database name
db_username: Tableau database username
meta_only: extract metadata only
events_only: extract events only
retries: Number of attemps if an API fails on NonXMLResponse Error, default is 5
dry_run: If specified the extractor will do a dry run to produce a template mapping.
output_path: full or relative path to where the outputs should go
login_timeout: The timeout for snowflake Auth
mask: To mask the META/DATABASE_LOG files or not

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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