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, see 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 examples to explore more deeply.

Pricing

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