Azure Data Factory (via Collector method) - v3.1
- Sidney Chen
- Chichi
About Collectors
Pre-requisites
Collector Server Minimum Requirements
Azure Data Factory Requirements
Access to Azure Data Factory
Refer to Step 1 - 3 in Azure Data Factory
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
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.whlYou 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.whlStep 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 |
|---|---|---|---|
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 |
active_days | integer | The pipeline must have been run within active days from today to be considered active, default is 60 | 60 |
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 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
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
active_days: Pipeline must have been run at least once within the number of days from today to be considered active
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