This section describes how you interact through the with your AWS Glue data warehouse via AWS Glue Catalog.
The can use Glue for the following tasks:
Read Access: Your Glue administrator must configure read permissions to Glue databases.
Write Access: Not supported.
For more information, see Configure for AWS.
The can read in partitioned tables. However, it cannot read individual partitions of partitioned tables.
Tip: If you are reading data from a partitioned table, one of your early recipe steps in the Transformer page should filter out the unneeded table data so that you are reading only the records of the individual partition.
Users should know where shared data is located and where personal data can be saved without interfering with or confusing other users.
NOTE: The does not modify source data in Glue. Datasets sourced from Glue are read without modification from their source locations.
You can create a from a table or view stored in Glue. For more information, see AWS Glue Browser.
If you have enabled custom SQL and are reading data from a view, nested functions are written to a temporary filename, unless they are explicitly aliased.
Tip: If your custom SQL uses nested functions, you should create an explicit alias from the results. Otherwise, the job is likely to fail.
SELECT UPPER(`t1`.`colum1`), TRIM(`t1`.`column2`),...
When these are read from a Glue view, the temporary column names are:
_c1, etc. During job execution, Spark ignores the
SELECT UPPER(`t1`.`column1`) as col1, TRIM(`t1`.`column2`) as col2,...
In this improved example, the two Glue view columns are aliased to the explicit column names, which are correctly interpreted and used by the Spark running environment during job execution.