Azure Data Factory (via Collector method) - v3.1

About Collectors

Pre-requisites

Collector Server Minimum Requirements

Azure Data Factory Requirements

Access to 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

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

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 Azure which the ADF is associated to
factory_name	string	The name of the ADF factory
output_path	string	Absolute path to the output location	"/tmp/output"
mask	boolean	To enable masking or not	true
timeout	integer	Timeout in seconds allowed against the ADF APIs	20
mapping	json	Mapping file of data source names against the onboarded host and database name in K	{"myDSN": {"host": "myhost", "database": "mydatabase"}}
compress	boolean	To compress the output	true
active_days	integer	The pipeline must have been run within active days from today to be considered active	60

kada_adf_extractor_config.json

JSON

{
    "client": "",
    "secret": "",
    "tenant": "",
    "subscription_id": "",
    "resource_group_name": "",
    "factory_name": "",
    "output_path": "/tmp/output",
    "mask": true,
    "timeout": 20,
    "mapping": {
        "myDSN": {
            "host": "myhost",
            "database": "mydatabase"
        }
    },
    "compress": true,
    "active_days": 60
}

Step 5: Run the Collector

This is the wrapper script: kada_adf_extractor.py

Python

import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.adf import Extractor

get_generic_logger('root')

_type = 'adf'
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, 'kada_{}_extractor_config.json'.format(_type))

parser = argparse.ArgumentParser(description='KADA ADF Extractor.')
parser.add_argument('--config', '-c', dest='config', default=filename)
parser.add_argument('--name', '-n', dest='name', default=_type)
args = parser.parse_args()

start_hwm, end_hwm = get_hwm(args.name)

ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})

publish_hwm(args.name, end_hwm)

Step 6: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated in the output_path directory.

High Water Mark File

A high water mark file is created called adf_hwm.txt.

Step 7: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.