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About Collectors
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Pre-requisites
Python 3.6 - 3.9
Access to the KADA Collector repository that contains the Snowflake whl
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 Snowflake whl (e.g. kada_collectors_extractors_snowflake-#.#.#-py3-none-any.whl)
Access to K landing directory
Access toSnowflake (see section below)
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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
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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
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Step 2: Getting Access to the Source Landing Directory
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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 Snowflake whl via Platform Settings → Sources → Download Collectors
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Run the following command to install the collector
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OS | Packages |
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CentOS | libffi-devel |
Ubuntu | libssl-dev |
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Step 4: Configure the Collector
The collector requires a set of parameters to connect to and extract metadata from Snowflake
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{
"account": "",
"username": "",
"password": "",
"information_database": "",
"role": "",
"warehouse": "",
"databases": [],
"login_timeout": 5,
"output_path": "/tmp/output",
"mask": true
} |
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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.
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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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#The-run-method
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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
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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.
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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)
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Example: Using Airflow to orchestrate the Extract and Push to K
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