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About Collectors
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Pre-requisites
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Python 3.6 - 3.10
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Collector Server Minimum Requirements
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PowerBI Requirements
Access to Power BI (see section below)
Power BI access
Follow the steps in PowerBI to setup a Service Principal with access to Power BI.
You will need for the setup
Application (client) ID
Directory (tenant) ID
Secret Value
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Lineage limitations in regards to Dataset Fields to Pages, lineage for this is dependant on the ability to export the PowerBI Report to analyse the pbix file. If we are unable to download the pbix file, this lineage will be missing for that report. |
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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 latest Core Library and Athena whl via Platform Settings → Sources → Download Collectors
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Run the following command to install the collector.
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pip install kada_collectors_extractors_<version>-none-any.whl |
You will also need to install the common library kada_collectors_lib -1.0.0 for this collector to function properly.
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The collector requires a set of parameters to connect to and extract metadata from Power BI
FIELD | FIELD TYPE | DESCRIPTION | EXAMPLE | ||
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client | string | Onboarded client in Azure to access powerbi | |||
secret | string | Onboarded client secret in Azure to access powerbi | |||
tenant | string | Tenant ID of where powerbi exists | |||
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 powerbi APIs, for slower connections we recommend 30, default is 20 | 20 | ||
export_timeout | integer | Timeout in seconds allowed against the powerbi export pbix APIs, we recommend not setting this lower than 120 | 120 | ||
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 powerbi, I’ll map it to host “myhost” and database “mydatabase” onboarded in K, snowflake type references are handled automatically
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filter_flag | boolean | Should we be filtering out workspaces based on filter_workspaces as a whitelist? | false | ||
filter_workspaces | list<string> | List of workspace names that should be processed, this is case insensitive. Note that personal workspaces are excluded globally and will never be included even if you include it here. | [“data lab”, “analysis models”] | ||
compress | boolean | To gzip the output or not | true |
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.
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import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.powerbi import Extractor
get_generic_logger('root') # Set to use the root logger, you can change the context accordingly or define your own logger
_type = 'powerbi'
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, 'kada_{}_extractor_config.json'.format(_type))
parser = argparse.ArgumentParser(description='KADA PowerBI Extractor.')
parser.add_argument('--config', '-c', dest='config', default=filename, help='Location of the configuration json, default is the config json in the same directory as the script.')
parser.add_argument('--name', '-n', dest='name', default=_type, help='Name of the collector instance.')
args = parser.parse_args()
start_hwm, end_hwm = get_hwm(_typeargs.name)
ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})
publish_hwm(_type, end_hwm) |
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