AWS Glue Studio now supports reading uncatalogued data from Amazon S3 and inferring its schema
AWS Glue now supports reading data stored in Amazon S3 without first adding it to the AWS Glue Data Catalog. This feature makes it fast to start authoring Extract, Transform, and Load (ETL) and ELT jobs in AWS Glue Studio by allowing you to use locations and objects in Amazon S3 directly as data sources. AWS Glue infers the schema of the data in real time , allowing you to easily define transforms on it in the AWS Glue Studio visual job authoring interface.
AWS Glue now supports reading data stored in Amazon S3 without first adding it to the AWS Glue Data Catalog. This feature makes it fast to start authoring Extract, Transform, and Load (ETL) and ELT jobs in AWS Glue Studio by allowing you to use locations and objects in Amazon S3 directly as data sources. AWS Glue infers the schema of the data in real time , allowing you to easily define transforms on it in the AWS Glue Studio visual job authoring interface.
Previously, you could use AWS Glue Data Catalog tables as data sources in AWS Glue Studio by first running an AWS Glue crawler on the data or manually adding tables to the Data Catalog. Now, you can specify a location or file in Amazon S3 directly in the visual job authoring interface, and AWS Glue Studio will infer the schema of the data and display it. Your job can read directly from the Amazon S3 bucket during each job run and adapt to schema changes.
AWS Glue Studio is available in the following AWS Regions: US East (N. Virginia), US East (Ohio),US West (N. California), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Stockholm), Middle East (Bahrain), and South America (São Paulo).
To learn more about this feature, visit our documentation.