![]() ![]() This connection supports either AWS keys or instance profiles (DBFS mount points are not supported, so if you do not want to rely on AWS keys you should use cluster instance profiles instead). Spark connects to S3 using both the Hadoop FileSystem interfaces and directly using the Amazon Java SDK’s S3 client. S3 acts as an intermediary to store bulk data when reading from or writing to Redshift. save () // Write back to a table using IAM Role based authentication df. ![]() load () // After you have applied transformations to the data, you can use // the data source API to write the data back to another table // Write back to a table df. option ( "forward_spark_s3_credentials", True ). option ( "query", "select x, count(*) group by x" ). load () // Read data from a query val df = spark. Read data from a table val df = spark. save () ) # Write back to a table using IAM Role based authentication ( df. load () ) # After you have applied transformations to the data, you can use # the data source API to write the data back to another table # Write back to a table ( df. load () ) # Read data from a query df = ( spark. Azure Synapse with Structured Streaming.Interact with external data on Databricks. ![]()
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