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I am in dilemma whether to keep on using Power BI rest API calls in updating Fabric workspace.
What's future of Power BI Rest API after launching of Fabric Rest API?
Solved! Go to Solution.
The Power BI REST API and the Microsoft Fabric REST API serve different purposes and are likely to coexist in various capacities. Here’s a brief overview to help with your decision:
Functionality: If your operations are heavily reliant on Power BI-specific features and integrations, the Power BI REST API might still be your best choice. If you’re looking for broader integration with Microsoft’s data ecosystem, exploring the Fabric REST API could be beneficial.
Compatibility: Check if the Fabric REST API supports all the functionalities you currently use with the Power BI REST API. As Fabric API matures, it might offer new features or improved integrations that could make it more suitable for your needs.
Future-Proofing: If you’re planning for long-term solutions and Fabric’s capabilities align with your future requirements, transitioning to the Fabric REST API might be a strategic move.
Support and Updates: Stay informed about updates from Microsoft regarding both APIs to ensure you’re leveraging the best tools for your needs.
Ultimately, the choice depends on your specific requirements and how each API aligns with your long-term strategy.
Scratch that.
It works;
We can develop in PBIP model locally, and publish a PBIP semantic model direct from source code using the SemanticModel endpoints;
https://learn.microsoft.com/en-us/rest/api/fabric/semanticmodel/items/create-semantic-model?tabs=HTT...
The model needs to be compiled into a post body with inline base64 encoded elements.
This opens up a fairly simple github flow development lifecycle.
I'm using python;
def get_pbip_deploy_body_payload(project_root, model_name, *args, **kwargs):
semantic_model = os.path.join(project_root, f"{model_name}.SemanticModel")
files = [
"definition/database.tmdl",
"definition/model.tmdl",
"definition.pbism",
"diagramLayout.json",
".platform",
]
tables_path = f"{os.path.join(semantic_model,'definition', 'tables')}/*.tmdl"
for file in glob.glob(tables_path):
file_path = f"definition/tables/{os.path.basename(file)}"
files.append(file_path)
pp(files)
body = {
"displayName": model_name,
"definition": {
"parts":[]
}
}
for file in files:
with open(os.path.join(semantic_model, file), mode='rt') as file_data:
text = file_data.read()
inlineb64 = b64encode(text.encode('utf-8')).decode('utf-8')
body["definition"]["parts"].append(
{
"path": file,
"payload": inlineb64,
"payloadType": "InlineBase64"
}
)
return body
Hi,
A follow question to this...
Is it correct that the Fabric API requires Fabric capacity?
We're currently able to use the PowerBi API with PPU license workspaces.
However I was experimenting with using the CreateItem endpoint to publish a semantic model and I'm getting a 401 unauthorised response. Not sure whether I've done something wrong or it's the Fabric capacity limitation.
The Power BI REST API and the Microsoft Fabric REST API serve different purposes and are likely to coexist in various capacities. Here’s a brief overview to help with your decision:
Functionality: If your operations are heavily reliant on Power BI-specific features and integrations, the Power BI REST API might still be your best choice. If you’re looking for broader integration with Microsoft’s data ecosystem, exploring the Fabric REST API could be beneficial.
Compatibility: Check if the Fabric REST API supports all the functionalities you currently use with the Power BI REST API. As Fabric API matures, it might offer new features or improved integrations that could make it more suitable for your needs.
Future-Proofing: If you’re planning for long-term solutions and Fabric’s capabilities align with your future requirements, transitioning to the Fabric REST API might be a strategic move.
Support and Updates: Stay informed about updates from Microsoft regarding both APIs to ensure you’re leveraging the best tools for your needs.
Ultimately, the choice depends on your specific requirements and how each API aligns with your long-term strategy.
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