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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Pre-requisites
Python 3.6 - 3.10
Tableau Server Version [2019.3] and above.
Enable the Tableau Metadata API for Tableau Server
This requires a server restart if not enabled
Tableau API access
An API user (record the username and password) needs to be created to access Tableau API.
The user cannot be a SSO user. This is a Tableau limitation. SSO users cannot access Tableau API
User needs
Site Administrator Creator
orServer/Site Administrator
role. Roles are dependent on both Licensing and Server version see https://help.tableau.com/current/server/en-us/users_site_roles.htmSite Administrator Creator
is only available on Role Based Licensing ModelServer/Site Administrator
is available on both Role Based and Core Based Licensing Model
Tableau Repository access
Follow the instructions to create a user that can access the Tableau repositoryhttps://help.tableau.com/current/server/en-us/perf_collect_server_repo.htm
This requires a server restart if not enabled
Note the Tableau repository default user is called
readonly
Access to the KADA Collector repository
The repository is currently hosted in KADA’s Azure Blob Storage. You will be given a SAS token to access the repository. Reach out to KADA Support (support@kada.ai) if you do not have access.
Download the Tableau whl (e.g. kada_collectors_extractors_tableau-#.#.#-py3-none-any.whl)
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PARAMATER | TYPE | DESCRIPTION | EXAMPLE | |||||
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server_address | string | Tableau server address inclusive of http/https | ||||||
username | string | Username to log into tableau API | “tabadmin” | |||||
password | string | Password to log into tableau API | ||||||
sites | list<string> | List of specific sites that you wish to extract, if left as [] it will extract all sites. If you wish to extract the default site only you may do so specifying “default” | [] | |||||
db_host | string | This is generally the same as server address less the http/https | “10.1.19.15” | |||||
db_username | string | By default the tableau database use is readonly. You should not need to change this unless you actively manage the database | “readonly” | |||||
db_password | list<string> | Password for the database user | ||||||
db_port | integer | Default is 8060 unless your tableau is configured differently | 8060 | |||||
db_name | string | Default database to use is workgroup | “workgroup” | |||||
meta_only | boolean | If for some reason you want to extract meta only set this to true otherwise leave it as false | false | |||||
retries | integer | Number of retries that the extractor should hit the API incase of intermittent failures, default is 5 | 5 | |||||
dry_run | boolean | By doing a dry run you produce the mapping.json file which is used to populate the mapping field below. It is recommended you do a dry run first to see what databases are available to map. | true | |||||
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 | |||||
mapping | json | Add the mapping for each data source in Tableau to a data source that is loaded into K. You will need to map the data source name in Tableau to the data source host name in K (these can be found on the Sources page) Skip any data sources are not onboarded in K (these will be loaded in as references until the source is added to K). See Host / Database Mapping for more details | Where somehost is the alternate name created in Tableau for the Analytics database that has been onboarded to K
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Example: Using Airflow to orchestrate the Extract and Push to K
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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.
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 |