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

About Collectors

Collectors are extractors that are developed and managed by you (a customer of K).

KADA provides python libraries that customers can use to quickly deploy a Collector.

Why you should use a Collector

There are several reasons why you may use a collector vs the direct connect extractor:

You are using the KADA SaaS offering and it cannot connect to your sources due to firewall restrictions
You want to push metadata to KADA rather than allow it to pull data for security reasons
You want to inspect the metadata before pushing it to K

Using a collector requires you to manage:

Deploying and orchestrating the extract code
Managing a high water mark so the extract only pulls the latest metadata
Storing and pushing the extracts to your K instance

Pre-requisites

Collector Server Minimum Requirements

For the collector to operate effectively, it will need to be deployed on a server with the below minimum specifications:

CPU: 2 vCPU
Memory: 8GB
Storage: 30GB (depends on historical data extracted)
OS: unix distro e.g. RHEL preferred but can also work with Windows Server
Python 3.10.x or later
Access to K landing directory

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

When using a Collector you will push metadata to a K landing directory.

To find your landing directory you will need to:

Go to Platform Settings - Settings. Note down the value of this setting:
- If using Azure: storage_azure_storage_account
- If using AWS:
  - storage_root_folder - the AWS s3 bucket
  - storage_aws_region - the region where the AWS s3 bucket is hosted
Go to Sources - Edit the Source you have configured. Note down the landing directory in the About this Source section.

To connect to the landing directory you will need:

If using Azure: a SAS token to push data to the landing directory. Request this from KADA Support (support@kada.ai)
If using AWS:
- An Access key and Secret. Request this from KADA Support (support@kada.ai)
- OR provide your IAM role to KADA Support to provision access.

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.

Refer to Collector Integration General Notes for more information.

Step 7: Push the Extracts to K

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

Example: Using Airflow to orchestrate the Extract and Push to K

The following example is how you can orchestrate the Tableau collector using Airflow and push the files to K hosted on Azure. The code is not expected to be used as-is but as a template for your own DAG.

Python

# built-in
import os

# Installed
from airflow.operators.python_operator import PythonOperator
from airflow.models.dag import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
from airflow.utils.task_group import TaskGroup

from plugins.utils.azure_blob_storage import AzureBlobStorage

from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.tableau import Extractor

# To be configured by the customer.
# Note variables may change if using a different object store.
KADA_SAS_TOKEN = os.getenv("KADA_SAS_TOKEN")
KADA_CONTAINER = ""
KADA_STORAGE_ACCOUNT = ""
KADA_LANDING_PATH = "lz/tableau/landing"
KADA_EXTRACTOR_CONFIG = {
    "server_address": "http://tabserver",
    "username": "user",
    "password": "password",
    "sites": [],
    "db_host": "tabserver",
    "db_username": "repo_user",
    "db_password": "repo_password",
    "db_port": 8060,
    "db_name": "workgroup",
    "meta_only": False,
    "retries": 5,
    "dry_run": False,
    "output_path": "/set/to/output/path",
    "mask": True,
    "mapping": {}
}

# To be implemented by the customer.
# Upload to your landing zone storage.
# Change '.csv' to '.csv.gz' if you set compress = true in the config
def upload():
  output = KADA_EXTRACTOR_CONFIG['output_path']
  for filename in os.listdir(output):
      if filename.endswith('.csv'):
        file_to_upload_path = os.path.join(output, filename)

        AzureBlobStorage.upload_file_sas_token(
            client=KADA_SAS_TOKEN,
            storage_account=KADA_STORAGE_ACCOUNT,
            container=KADA_CONTAINER,
            blob=f'{KADA_LANDING_PATH}/{filename}',
            local_path=file_to_upload_path
        )

with DAG(dag_id="taskgroup_example", start_date=days_ago(1)) as dag:

    # To be implemented by the customer.
    # Retrieve the timestamp from the prior run
    start_hwm = 'YYYY-MM-DD HH:mm:SS'
    end_hwm = 'YYYY-MM-DD HH:mm:SS' # timestamp now

    ext = Extractor(**KADA_EXTRACTOR_CONFIG)

    start = DummyOperator(task_id="start")

    with TaskGroup("taskgroup_1", tooltip="extract tableau and upload") as extract_upload:
        task_1 = PythonOperator(
            task_id="extract_tableau",
            python_callable=ext.run,
            op_kwargs={"start_hwm": start_hwm, "end_hwm": end_hwm},
            provide_context=True,
        )

        task_2 = PythonOperator(
            task_id="upload_extracts",
            python_callable=upload,
            op_kwargs={},
            provide_context=True,
        )

        # To be implemented by the customer.
        # Timestamp needs to be saved for next run
        task_3 = DummyOperator(task_id='save_hwm')

    end = DummyOperator(task_id='end')

    start >> extract_upload >> end