What should you do?

You have an Azure Synapse Analytics Apache Spark pool named Pool1.

You plan to load JSON files from an Azure Data Lake Storage Gen2 container into the tables in Pool1. The structure and data types vary by file.

You need to load the files into the tables. The solution must maintain the source data types.

What should you do?
A . Load the data by using PySpark.
B . Load the data by using the OPENROWSET Transact-SQL command in an Azure Synapse Analytics serverless SQL pool.
C . Use a Get Metadata activity in Azure Data Factory.
D . Use a Conditional Split transformation in an Azure Synapse data flow.

View Answer

Answer: B

Explanation:

Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools.

Serverless SQL pool enables you to query data in your data lake. It offers a T-SQL query surface area that accommodates semi-structured and unstructured data queries.

To support a smooth experience for in place querying of data that’s located in Azure Storage files, serverless SQL pool uses the OPENROWSET function with additional capabilities.

The easiest way to see to the content of your JSON file is to provide the file URL to the OPENROWSET

function, specify csv FORMAT.

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-json-files

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-data-storage