topic Re: Serverless views in Fabric in Data Engineering

Serverless views in Fabric

girishtharwani2 — Mon, 27 Jan 2025 17:05:50 GMT

Hi,

I have loaded data from a source into the Data Lake (ADLS) using a Fabric pipeline. In Synapse, we used to query ADLS data using serverless views. Is there a similar way to utilize serverless views in Fabric?

Thanks,
Girish

Re: Serverless views in Fabric

nilendraFabric — Mon, 27 Jan 2025 18:07:14 GMT

Hello girishtharwani2

You can use sql endpoint on OPENROWSET.

this functionality will be available using OPENROWSET

Estimated release timeline: Q1 2025

Release Type: Public preview

Fabric DW enables the users to use the OPENROWSET function to read data from the files in the lake. A simple example of OPENROWSET function is:

SELECT * 
FROM OPENROWSET ( BULK ‘<file path>’ ) 
WITH ( <column definition> )

The OPENROWSET function will read the content of the file(s) at the given <file path>and return the content of the files. Thi function enables easy browsing and previewing the files before ingestion.

https://learn.microsoft.com/en-us/fabric/release-plan/data-warehouse

please see if this is helpful and try running OPENROWSET

Re: Serverless views in Fabric

Anonymous — Wed, 29 Jan 2025 09:18:56 GMT

Hi girishtharwani2
Thank you for reaching out microsoft fabric community forum.
I wanted to check if you had the opportunity to review the information provided by nilendraFabric . Please feel free to contact us if you have any further questions. If his response has addressed your query, please accept it as a solution and give a 'Kudos' so other members can easily find it.
Thank you.

Re: Serverless views in Fabric

girishtharwani2 — Mon, 03 Feb 2025 16:38:06 GMT

nilendraFabric The feature is yet to be released it seems, any other alternative that I can leverage ?

Thanks,

Girish

Re: Serverless views in Fabric

nilendraFabric — Mon, 03 Feb 2025 17:01:33 GMT

There are 2 options

Easiest way is to create shortcut to Adls

Select New Shortcut → Azure Data Lake Storage Gen2 → Provide ADLS URL.

And then query it like this

SELECT * FROM Lakehouse.DemoShortcut.sales_data;

other way is

Use the `COPY INTO` Command
The most direct method for bulk-loading data into a Fabric Warehouse or Lakehouse table:

COPY INTO dbo.RetailSales
FROM 'https://<storage-account>.dfs.core.windows.net/<container>/sales/*.parquet'
WITH (
FILE_TYPE = 'PARQUET',
CREDENTIAL = (IDENTITY = 'Managed Identity')
);

Hope this helps

please accept this solution if this is helpful and give kudos

Re: Serverless views in Fabric

girishtharwani2 — Tue, 04 Feb 2025 14:33:09 GMT

Thanks for the inputs.

In serverless in Synapse, I have partitioned by the files based on date and I used to pick latest folder as date was handled dynamically.

I see I can manually create a table on ADLS but it will be one time, Is there a way I can achieve similar functionality in lakehouse as I am getting in serverless ?

Thanks,

Girish

Re: Serverless views in Fabric

Anonymous — Sun, 09 Feb 2025 03:24:14 GMT

Hello girishtharwani2.,

Thank you for your detailed explaination regarding your query.

I would also take a moment to personally thank nilendraFabric , for actively participating in the community forum and for the solutions you’ve been sharing in the community forum. Your contributions make a real difference.

In Fabric Lakehouse, while there isn't a direct equivalent to Synapse Serverless SQL for dynamically querying partitioned data, you can achieve similar functionality using Lakehouse Shortcuts with Delta tables or dynamic queries with metadata tables.

Here are some approaches to consider:

If your data in ADLS follows the Delta format, Fabric Lakehouse natively supports querying Delta tables, allowing you to use SQL to filter the latest partition dynamically.
If you are working with Parquet files, you can use Fabric Notebook with PySpark to create a metadata table that dynamically tracks partition locations.

I hope this should resolve your issue, if you need any further assistance, feel free to reach out.

If this post helps, then please give us Kudos and consider Accept it as a solution to help the other members find it more quickly.

Thank you.