Snowflake Extension (via Collector method) - v1.0.0

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 to 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 pulls the latest metadata

3. Storing and pushing the extracts to your K instance

Pre-requisites

Collector Server Minimum Requirements

For the collector to operate effectively, it will need to be deployed on a server with the below minimum specifications:

• CPU: 2 vCPU

• Memory: 8GB

• Storage: 30GB (depends on historical data extracted)

• OS: unix distro e.g. RHEL preferred but can also work with Windows Server

• Python 3.10.x or later

• Access to K landing directory

Snowflake Requirements

• Access to Snowflake (see section below). The instructions for access assume you have already added the Snowflake source in K.

Step 1: Snowflake Access

Option 1: Extending the K user with access to Snowflake Extension

Assuming you followed the Snowflake integration instructions and created a role called CATALOG_READ_ONLY you can follow the steps to add grants to extend the user to be able to read Snowflake Extension objects.

--Log in with a user that has the permissions to assign/update roles

--Add the following Semantic View related roles to the KADA role created previously
GRANT SELECT ON ALL SEMANTIC VIEWS IN DATABASE <each_target_db> TO ROLE CATALOG_READ_ONLY;
GRANT SELECT ON FUTURE SEMANTIC VIEWS IN DATABASE <each_target_db> TO ROLE CATALOG_READ_ONLY;

Option 2: Creating a user with access to Snowflake DMF

To create a user with general access to metadata available in Snowflake Account Usage schema

-- Log in with a user that has the permissions to create a role/user

-- Create a new role for the Catalog user
CREATE ROLE CATALOG_READ_ONLY;

-- Grant the role access to the Account usage schema
GRANT IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE TO CATALOG_READ_ONLY;
GRANT SELECT ON ALL TABLES IN SCHEMA SNOWFLAKE.ACCOUNT_USAGE TO CATALOG_READ_ONLY;
GRANT MONITOR ON ACCOUNT TO ROLE CATALOG_READ_ONLY;

-- Add the following semantic view related roles
GRANT SELECT ON ALL SEMANTIC VIEWS IN DATABASE <each_target_db> TO ROLE CATALOG_READ_ONLY;
GRANT SELECT ON FUTURE SEMANTIC VIEWS IN DATABASE <each_target_db> TO ROLE CATALOG_READ_ONLY;

-- Create a new user for K and grant it the role (remove the [])
create user [kada_user] password=['abc123!@#'] default_role = CATALOG_READ_ONLY default_warehouse = [warehouse];

Final Steps

From the above record down the following to be used for the setup

1. User name / Password

2. Role

3. Warehouse

4. (If creating a new database for metadata) Database name

5. Snowflake account (found in the URL of your Snowflake instance - between https:// and .snowflakecomputing.com/…)

Authentication Methods

Key-Pair Authentication

If you want to connect to Snowflake via Key Pair Authentication, follow these steps https://docs.snowflake.com/en/user-guide/key-pair-auth#step-1-generate-the-private-key and attach the key to the user you created.

Entra OAuth Connection

Follow the instructions here to setup your Snowflake environment for Entra OAuth Setting Up External OAuth Token Using Azure AD For OAuth Client

Step 2: Create the Source in K

• Go to Settings, Select Sources and click Add Source

• Select Snowflake Ext and click next

• Select “Load from File” option

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

• Add the Host name for the Snowflake Server

• Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

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:

   • If using Azure: storage_azure_storage_account

   • If using AWS:

     • storage_root_folder - the AWS s3 bucket

     • 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 4: 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.

You can download the latest Core Library and Snowflake whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector.

pip install kada_collectors_extractors_<version>-none-any.whl

You will also need to install the latest common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_lib-<version>-none-any.whl

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.

Please also see https://docs.snowflake.com/en/user-guide/python-connector-install.html

Step 5: Configure the Collector

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

-----BEGIN ENCRYPTED PRIVATE KEY-----\nblah\n-----END ENCRYPTED PRIVATE KEY-----

kada_snowflake_extractor_config.json

{
    "account": "",
    "username": "",
    "password": "",
    "information_database": "",
    "role": "",
    "warehouse": "",
    "databases": [],
    "login_timeout": 5,
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "host": "",
    "use_private_key": false,
    "private_key": "",
    "use_oauth": false,
    "oauth_token_url": "",
    "client_id": "",
    "client_secret": "",
    "oauth_scope": "",
    "enterprise": true,
    "meta_only": false,
    "stage_file_lookback_days": 30
}

Step 6: Run the Collector

The following code is an example of how to run the extractor. This is the wrapper script: kada_snowflake_ext_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.snowflake_ext import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA Snowflake Extension 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.')
parser.add_argument('--name', '-n', dest='name', default=_type, help='Name of the collector instance.')
args = parser.parse_args()

start_hwm, end_hwm = get_hwm(args.name)

ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})

publish_hwm(args.name, end_hwm)

Advance options:

Refer to Collector Integration General Notes | Storing HWM in another location for storing HWM in another location.

from kada_collectors.extractors.snowflake import Extractor

kwargs = {my args}
hwm_kwrgs = {"start_hwm": "end_hwm": }

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

Step 7: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated in the output_path directory.

High Water Mark File

A high water mark file snowflake_dmf_hwm.txt is created if you call the publish_hwm method.

Step 8: Push the Extracts to K

Once the files have been validated, 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

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 DAG.

# 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 configured 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.
# Change '.csv' to '.csv.gz' if you set compress = true in the config
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

Last updated: July 28, 2026