Data lake exploration with external tables made easy

Published Apr 20 2020 08:41 AM 2,897 Views
Microsoft

Azure Synapse Analytics (formerly SQL Data Warehouse) is an analytics platform that provides a set of enhanced capabilities for data professionals to achieve more with faster insights from their data. One of the new capabilities currently in preview is the Synapse Studio which is a unified workspace experience for building and managing end-to-end analytics solutions. Synapse studio provides many simplified experiences to empower data professionals such as the Synapse SQL External Table wizard.

 

External tables for Synapse SQL are used to persist the schema of data residing in the lake for data exploration and quick adhoc analytics. Previously, defining external tables was a manual and tedious process which required you to first define database objects such as the external file format, database scoped credential, and external data source. Then you would need to inspect the Parquet file using separate tools such as Parquet-Tools and go through a matching exercise to find the corresponding SQL type from the Parquet type. This can become a laborious process, especially for wide tables with hundreds of columns.

 

This time-consuming process has now been reduced to only 3 clicks in the Synapse Studio providing a code-less experience to creating your external table.

 

ExternalTableWizard.gif

 

As you can see, all the necessary SQL objects are created for you, AAD pass-through authentication is used for secure access, and a SELECT TOP 100 is automatically generated all in a single SQL script so that you can immediately connect to your SQL endpoint and run the script to query your data in the lake. 

 

For more information, visit the Azure Synapse documentation here. You can sign up for the Studio preview at this link here.

 

To provide feedback on Synapse SQL experiences in the studio, please reach out to the following email distribution list: SynapseSQLStudioFeedback@service.microsoft.com.

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‎Apr 20 2020 01:55 PM
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