Azure Data Factory (via Collector method) - v3.0

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About Collectors

Collector Method

Pre-requisites

Python 3.6 - 3.10
Access to K landing directory
Access to Azure Data Factory (see section below)

Azure Data Factory Permissions

Refer to Step 1 - 3 in https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1952907265

Step 1: Create the Source in K

Create a Azure Data Factory source in K

Go to Settings, Select Sources and click Add Source
Select “Load from File” option
Give the source a Name - e.g. Azure Data Factory Production
Add the Host name for the Azure Data Factory 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.

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 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 common library kada_collectors_lib for this collector to function properly.

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

Step 4: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from Azure Data Factory

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE
client	string	Onboarded client in Azure to access ADF
secret	string	Onboarded client secret in Azure to access ADF
tenant	string	Tenant ID of where ADF exists
subscription_id	string	Subscription in Azure which the ADF is associated to
resource_group_name	string	Resource group in Auzre which the ADF is associated to
factory_name	string	The name of the ADF factory
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
timeout	integer	Timeout in seconds allowed against the ADF APIs, for slower connections we recommend 30, default is 20	20
mapping	json	Mapping file of data source names against the onboarded host and database name in K	Assuming I have a “myDSN” data source name in ADF, I’ll map it to host “myhost” and database “mydatabase” onboarded in K, snowflake type references are handled automatically `{ "myDSN": { "host": "myhost", "database": "mydatabase" } }`
compress	boolean	To compress the output as .csv.gz instead of just .csv	true

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_adf_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. It will produce and read a high water mark file from the same directory as the execution called adf_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_adf_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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#The-run-method

client_id: Client ID registered in ADF
client_secret: As per registered app in Azure
tenant_id: Tenant Id of where the registered app in Azure exists
subscription_id: Subscription ID of where the registered app in Azure exists
resource_group_name: The resource group name of where the registered app in Azure exists
factory_name: The azure data factory name
mapping: Dict of DNS to database and hostnames
timeout: Timeout for the API call
output_path: full or relative path to where the outputs should go
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 adf_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