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Tableau Server (Collector method) - v2.0.0

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.


Pre-requisites


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

When using a Collector you will push metadata to a K landing directory.

To find your landing directory you will need to

  1. Go to Platform Settings - Settings. Note down the value of this setting

    1. If using Azure: storage_azure_storage_account

    2. if using AWS:

      1. storage_root_folder - the AWS s3 bucket

      2. storage_aws_region - the region where the AWS s3 bucket is hosted

  2. Go to Sources - Edit the Source you have configured. Note down the landing directory in the About this Source section

To connect to the landing directory you will need

  • If using Azure: a SAS token to push data to the landing directory. Request this from KADA Support (support@kada.ai)

  • if using AWS:

    • an Access key and Secret. Request this from KADA Support (support@kada.ai)

    • OR provide your IAM role to KADA Support to provision access.


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 → SourcesDownload Collectors

Run the following command to install the collector

CODE
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

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

CODE
{
"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

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

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)

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 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 Additional Notes | The-run-method


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.

CODE

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)
CODE
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


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)


Example: Using Airflow to orchestrate the Extract and Push to K

PY
# 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,
            storage_account=KADA_STORAGE_ACCOUNT,
            container=KADA_CONTAINER, 
            blob=f'{KADA_LANDING_PATH}/{filename}', 
            local_path=file_to_upload_path
        )

with DAG(dag_id="taskgroup_example", start_date=days_ago(1)) as dag:
  
    # To be implemented by the customer.
    # Retrieve the timestamp from the prior run
    start_hwm = 'YYYY-MM-DD HH:mm:SS'
    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

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