This section describes how you interact through the

• Databricks Tables enables interaction with flat-format files as schematized datasets.
• For more information, see https://docs.microsoft.com/en-us/azure/databricks/data/tables.

Uses of Databricks Tables

The

1. Create datasets by reading from Databricks Tables tables.
2. Write data to Databricks Tables.

The underlying format for Databricks Tables is Parquet.

Limitations

• Access to external Hive metastores is not supported.
• Ad-hoc publishing to Azure Databricks is not supported.
• Creation of datasets with custom SQL is not supported.
• Use of partitioned tables in Databricks Tables is not supported.

Before You Begin Using Databricks Tables

• Databricks Tables deployment: Your 

  D s item
  item administrator

   must enable use of Databricks Tables. See Create Databricks Tables Connections. 

• Databricks Personal Access Token: You must acquire and save a Databricks Personal Access Token into your 

  D s item
  item account

  . For more information, see Databricks Personal Access Token Page.

Storing Data in Databricks Tables

Reading from Databricks Tables

You can create a

• Read support is also available for Databricks Delta Lake.
• For more information, see Databricks Tables Browser.

For more information on how data types are imported from Databricks Tables, see Databricks Tables Data Type Conversions.

Writing to Databricks Tables

You can write data back to Databricks Tables using one of the following methods:

• Job results can be written directly to Databricks Tables as part of the normal job execution. 
  • Data is written as a managed table to DBFS in Parquet format.
  • Create a new publishing action to write to Databricks Tables.
  • See Run Job Page.
• For more information on how data is converted to Databricks Tables, see Databricks Tables Data Type Conversions.

Ad-hoc Publishing to Databricks Tables

Not supported.