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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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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.
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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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