List of Current Available Collectors
K Platform currently offers the following Collector Methods:
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About Collectors
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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 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 pull the latest metadata Storing and pushing the extracts to your K instance.
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Downloading the latest Collector and Core Library
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| Download-Collector |
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| Download-Collector |
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The latest core library needs to be installed prior to deploying a Collector. You can download the latest Core Library and Collector whl via Platform Settings → Sources → Download Collectors
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Collector Server Minimum Specifications
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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
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About Landing Directories
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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 |
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Airflow Example
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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. | Code Block |
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| # 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 configed 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 |
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Downloading the latest Collector and Core Library
The latest core library needs to be installed prior to deploying a Collector. You can download the latest Core Library and Collector whl via Platform Settings → Sources → Download Collectors
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