Next steps - AWS Prescriptive Guidance

Next steps

Understanding AWS Glue transformations

For more efficient data processing, AWS Glue includes built-in transformation functions. The functions pass from transform to transform in a data structure called a DynamicFrame, which is an extension to an Apache Spark SQL DataFrame. A DynamicFrame is similar to a DataFrame, except that each record is self-describing, so no schema is required initially.

To get acquainted with several AWS Glue PySpark built-in functions, read the blog post Building an AWS Glue ETL pipeline locally without an AWS account.

Authoring your first ETL job

If you haven't written an ETL job before, you can get started by using the Three AWS Glue ETL job types for converting data to Apache Parquet pattern.

If you have experience writing ETL jobs, you can use the AWS Glue GitHub samples to explore more deeply.


For pricing information, see AWS Glue pricing. You can also use the pricing calculator to estimate your monthly cost for using different AWS Glue components.

                The Edit AWS Glue screen in the pricing calculator.