Azure SQL Ingestion Source

Azure SQL data source reads the objects from Azure SQL and SQL Server database.

Data Source Configuration #

Fetch From Source/Upload Data File #

For designing the application, you can either fetch the sample data from the Azure SQL source by providing the data source connection details or upload a sample data file in one of the supported formats to see the schema details during the application design.

If Upload Data File is selected to fetch sample data, provide the below details.

File Format #

Select the sample file format (file type) depending on the data type.

Gathr-supported file formats for Azure SQL data source are CSV, JSON, TEXT, Parquet and ORC.

For CSV file format, select its corresponding delimiter.

Header Included #

Enable this option to read the first row as a header if your Azure SQL data is in CSV format.

Upload #

Please upload the sample file as per the file format selected above.

If Fetch From Source is selected, continue configuring the data source.

Connection Name #

Connections are the service identifiers. A connection name can be selected from the list if you have created and saved connection details for Azure SQL earlier. Or create one as explained in the topic- Azure SQL Connection →

Use the Test Connection option to ensure that the connection with the Azure SQL channel is established successfully.

A success message states that the connection is available. In case of any error in test connection, edit the connection to resolve the issue before proceeding further.

Schema Name #

Source Schema name for which the list of table will be viewed.

Table Name #

Source table name to be selected for which you want to view the metadata.

Add Configuration: Additional properties can be added using this option as key-value pairs.

More Configurations #

Query #

Hive compatible SQL query to be executed in the component.

Design Time Query #

Query used to fetch limited records during Application design. Used only during schema detection and data reload.

Enable Query Partitioning #

This enables parallel reading of data from the table. It is disabled by default.

Tables will be partitioned if this check-box is enabled.

If Enable Query Partitioning is check marked, additional fields will be displayed as given below:

No. of Partitions #

Specifies the number of parallel threads to be invoked to partition the table while reading the data.

Partition on Column #

This column will be used to partition the data. This has to be a numeric column, on which spark will perform partitioning to read data in parallel.

Lower Bound #

Value of the lower bound for partitioning column. This value will be used to decide the partition boundaries. The entire dataset will be distributed into multiple chunks depending on the values.

Upper Bound #

Value of the upper bound for partitioning column. This value will be used to decide the partition boundaries. The entire dataset will be distributed into multiple chunks depending on the values.

If Enable Query Partitioning is disabled, then proceed by updating the following field.

Fetch Size #

The fetch size determines the number of rows to be fetched per round trip. The default value is 1000.

Schema #

Check the populated schema details. For more details, see Schema Preview →

Advanced Configuration #

Optionally, you can enable incremental read. For more details, see Azure SQL Incremental Configuration →