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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

    • 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

  • Access to the KADA Collector repository

    • 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 Tableau whl (e.g. kada_collectors_extractors_tableau-#.#.#-py3-none-any.whl)

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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. If you wish to extract the default site only you may do so specifying “default”

[]

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. You 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

Add the mapping for each data source in Tableau to a data source that is loaded into K. You will need to map the data source name in Tableau to the data source host name in K (these can be found on the Sources page)

Skip any data sources are not onboarded in K (these will be loaded in as references until the source is added to K).

See Host / Database Mapping for more details

Where somehost is the alternate name created in Tableau for the Analytics database that has been onboarded to K

Code Block
languagejson
{
"somehost.adw": "analytics.adw"
}

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

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Code Block

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language

...

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