Snowflake (via Collector method) - v3.0.0

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About Collectors

Collector Method

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

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

User name / Password
Role
Warehouse
Snowflake account (found in the URL of your Snowflake instance - between https:// and .snowflakecomputing.com/…)

Snowflake integration uses username/password. Using keys will be supported in an upcoming release

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

Collector Method

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 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. These are some known possible packages you may require depending on your OS, this is not exhaustive and only serves as a guide.

OS	Packages

OS	Packages
CentOS	libffi-devel openssl-devel
Ubuntu	libssl-dev libffi-dev

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

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE
account	string	Snowflake account	“abc123.australia-east.azure”
username	string	Username to log into the snowflake account
password	string	Password to log into the snowflake account
information_database	string	Database where all the required tables are located, generally this is snowflake	“snowflake”
role	string	The role to access the required account_usage tables, generally this is accountadmin	“accountadmin”
warehouse	string	The warehouse to execute the queries against	“xs_analytics”
databases	list<string>	A list of databases to extract from Snowflake	[“dwh”, “adw”]
login_timeout	integer	The max amount of time in seconds allowed for the extractor to establish and authenticate a connection, generally 5 is sufficient but if you have a slow network you can increase this up to 20	5
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
compress	boolean	To gzip the output or not	true
host	string	The host value for snowflake that was onboarded in K	“abc123.australia-east.azure.snowflakecomputing.com”

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_snowflake_extractor_config.json

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 is the wrapper script: kada_snowflake_extractor.py

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 Collector Integration General 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 Collector Integration General Notes | The run method

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

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

Collector Method