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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)

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

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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.

Step 2: Getting Access to the Landing Directory

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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.

Run the following command to install the collector

pip install kada_collectors_extractors_tableau-2.0.0-py3-none-any.whl

Configure the Collector

The collector uses a configuration JSON file to manage connection details and credentials. Below is the configuration file. You will need to create this configuration file and place it in a directory accessible to the collector.

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": {}
}

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FIELD

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FIELD TYPE

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DESCRIPTION

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EXAMPLE

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server_address

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string

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Tableau server address inclusive of http/https

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“https://10.1.19.15”

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username

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string

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Username to log into tableau api

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“tabadmin”

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password

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string

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Password to log into tableau api

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sites

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list<string>

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List of specific sites that you wish to extract, if left as [] it will extract all sites.

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[]

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db_host

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string

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This is generally the same as server address less the http/https

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“10.1.19.15”

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db_username

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string

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By default the tableau database use is readonly should not need to change this unless you actively manage the database

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“readonly”

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db_password

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list<string>

...

Password for the database user

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db_port

...

integer

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Default is 8060 unless your tableau is configured differently

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8060

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db_name

...

string

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Default database to use is workgroup

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“workgroup”

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meta_only

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boolean

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If for some reason you want to extract meta only set this to true otherwise leave it as false

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false

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retries

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integer

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Number of retries that the extractor should hit the API incase of intermittent failures, default is 5

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5

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dry_run

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boolean

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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.

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true

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output_path

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string

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Absolute path to the output location where files are to be written

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“/tmp/output”

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mask

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boolean

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To enable masking or not

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true

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mapping

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json

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This should be populate with the mapping.json output where each data source name mentioned is mapped to an onboarded K host

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Where analytics.adw is the onboarded database in K

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

Run the Collector

Run the following command to run the collector
kada_tableau_extractor.py

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

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

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

parser = argparse.ArgumentParser(description='KADA Tableau 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)

This can be executed anywhere that has the wheel installed.

Outputs from the Collector

Extracts to load to K

A set of files (metadata, log, roles etc) will be generated. These files will appear in the output_path directory set in the configuration

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.

The High Water Mark file is used to mark when the last run was executed.

• 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. Go and edit the source.

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• Record the Storage Location

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Upload the files to the Storage Location

Example orchestrating the Collector using Airflow

The following example is how you can orchestrate the Tableau collector using Airflow and push the files to K hosted on Azure. The code is not expected to be used as-is but as a template for your own code.

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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:

1. You are using the KADA SaaS offering and it cannot connect to your sources due to firewall restrictions

2. You want to push metadata to KADA rather than allow it pull data for Security reasons

3. You want to inspect the metadata before pushing it to K

Using a collector requires you to manage

1. Deploying and orchestrating the extract code

2. Managing a high water mark so the extract only pull the latest metadata

3. Storing and pushing the extracts to your K instance.

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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 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 repositoryhttps://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.

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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

...

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_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.

{
"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": {}
}

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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.

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

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

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

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

parser = argparse.ArgumentParser(description='KADA Tableau 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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+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 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

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.

from kada_collectors.extractors.tableau import Extractor

kwargs = {my args} # However you choose to construct your args
hwm_kwrgs = {"start_hwm": "end_hwm": } # The hwm values

ext = Extractor(**kwargs)
ext.run(**hwm_kwrgs)

class Extractor(server_address: str = None, username: str = None, password: str = None, \
    sites: list = [], db_host: str = None, db_password: str = None, \
    db_port: int = 8060, db_name: str = '≈', db_username: str = 'readonly', \
    meta_only: bool = False, events_only: bool = False, retries: int = 5, \
    dry_run: bool = False, output_path: str = './output', \
    mask: bool = False, mapping: dict = {})

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

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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

# built-in
import os

# Installed
from airflow.operators.python_operator import PythonOperator
from airflow.models.dag import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
from airflow.utils.task_group import TaskGroup

from plugins.utils.azure_blob_storage import AzureBlobStorage

from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.tableau import Extractor

# To be configed by the customer.
# Note variables may change if using a different object store.
KADA_SAS_TOKEN = os.getenv("KADA_SAS_TOKEN")
KADA_CONTAINER = ""
KADA_STORAGE_ACCOUNT = ""
KADA_LANDING_PATH = "lz/tableau/landing"
KADA_EXTRACTOR_CONFIG = {
    "server_address": "http://tabserver",
    "username": "user",
    "password": "password",
    "sites": [],
    "db_host": "tabserver",
    "db_username": "repo_user",
    "db_password": "repo_password",
    "db_port": 8060,
    "db_name": "workgroup",
    "meta_only": False,
    "retries": 5,
    "dry_run": False,
    "output_path": "/set/to/output/path",
    "mask": True,
    "mapping": {}
}

# To be implemented by the customer. 
# Upload to your landing zone storage.
def upload():
  output = KADA_EXTRACTOR_CONFIG['output_path']
  for filename in os.listdir(output):
      if filename.endswith('.csv'):
        file_to_upload_path = os.path.join(output, filename)

        AzureBlobStorage.upload_file_sas_token(
            client=KADA_SAS_TOKEN,
            

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storage_

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account=KADA_STORAGE_ACCOUNT,
            

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container=KADA_CONTAINER, 
           

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 blob=f'{KADA_LANDING_PATH}/{filename}', 
       

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     local_path=file_to_upload_path
    

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)

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with DAG(dag_id="taskgroup_example", start_date=days_ago(1)) as dag:
  
  

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# 

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To be 

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implemented 

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by 

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the 

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customer.
    # Retrieve the timestamp 

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from 

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the prior run
    

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start_hwm = 

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'YYYY-MM-DD HH:mm:SS'
    

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Advanced Usage

If you wish to maintain your own high water mark files else where 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.

If you are handling external arguments of the runner yourself, you’ll need to consider the following for the run method https://kadaai.atlassian.net/wiki/spaces/DAT/pages/1894318152/Notes+v2.0.0#The-run-method

from kada_collectors.extractors.tableau import Extractor

kwargs = {my args} # However you choose to construct your args
hwm_kwrgs = {"start_hwm": "end_hwm": } # The hwm values

ext = Extractor(**kwargs)
ext.run(**hwm_kwrgs)end_hwm = 'YYYY-MM-DD HH:mm:SS' # timestamp now
    
    ext = Extractor(**KADA_EXTRACTOR_CONFIG)
    
    start = DummyOperator(task_id="start")

    with TaskGroup("taskgroup_1", tooltip="extract tableau and upload") as extract_upload:
        task_1 = PythonOperator(
            task_id="extract_tableau",
            python_callable=ext.run, 
            op_kwargs={"start_hwm": start_hwm, "end_hwm": end_hwm},
            provide_context=True,
        )
        
        task_2 = PythonOperator(
            task_id="upload_extracts",
            python_callable=upload, 
            op_kwargs={},
            provide_context=True,
        )

        # To be implemented by the customer. 
        # Timestamp needs to be saved for next run
        task_3 = DummyOperator(task_id='save_hwm') 

    end = DummyOperator(task_id='end')

    start >> extract_upload >> end