Snowflake (via Collector method) - v3.1.0

About Collectors

Pre-requisites

• Python 3.6 - 3.9

• Access to K landing directory

• Access to Snowflake (see section below)

Snowflake Access

Create a Snowflake user with read access to following tables in the Snowflake database.

• account_usage.history

• account_usage.views

• account_usage.tables

• account_usage.columns

• account_usage.copy_history

• account_usage.grants_to_roles

• account_usage.grants_to_users

• account_usage.schemata

• account_usage.databases

You can use the following code:

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 Accoutn usage schema
grant select on all tables in schema SNOWFLAKE.ACCOUNT_USAGE to 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];

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

1. User name / Password

2. Role

3. Warehouse

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

If you want the 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.

Step 1: Create the Source in K

Create a Snowflake source in K

• Go to Settings, Select Sources and click Add Source

• 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 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.

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 4: Configure the Collector

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

blah
blah
-----END ENCRYPTED PRIVATE KEY----- |
| host | string | The host value for snowflake that was onboarded in K | "abc123.australia-east.azure.snowflakecomputing.com" |

kada_snowflake_extractor_config.json

{
    "account": "",
    "username": "",
    "password": "",
    "information_database": "",
    "role": "",
    "warehouse": "",
    "databases": [],
    "login_timeout": 5,
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "use_private_key": false,
    "private_key": "",
    "host": ""
}

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 is the wrapper script: kada_snowflake_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 import Extractor

get_generic_logger('root')

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

parser = argparse.ArgumentParser(description='KADA Snowflake 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(args.name, 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. Refer to the Additional Notes page for more information.

from kada_collectors.extractors.snowflake import Extractor

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

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

class Extractor(account: str = None,
    username: str = None,
    password: str = None,
    databases: list = [],
    information_database: str = 'snowflake',
    role: str = 'accountadmin',
    output_path: str = './output',
    warehouse: str = None,
    login_timeout: int = 5,
    mask: bool = False,
    compress: bool = False,
    host: str = None,
    use_private_key: bool = False,
    private_key: str = None) -> None)

account: snowflake account
username: username to sign into snowflake
password: password to sign into snowflake
information_database: database with snowflake level information
databases: list of databases to extract
role: role with access to the database with snowflake level information
output_path: full or relative path to where the outputs should go
warehouse: specify a different warehouse if required, otherwise the default will be used
login_timeout: The timeout for snowflake Auth
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not
use_private_key: Using private/public RSA keys
private_key: the private key value in plain text not byte encoded
host: The host value onboarded in K

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

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