AWS Glue is a fully managed, pay-as-you-go, extract, transform, and load (ETL) service that automates the time-consuming steps of data preparation for analytics.
AWS Glue automatically discovers and profiles data via the Glue Data Catalog, recommends and generates ETL code to transform your source data into target schemas.
AWS Glue runs the ETL jobs on a fully managed, scale-out Apache Spark environment to load your data into its destination.
AWS Glue also allows you to setup, orchestrate, and monitor complex data flows.
You can create and run an ETL job with a few clicks in the AWS Management Console.
Use AWS Glue to discover properties of data, transform it, and prepare it for analytics.
Glue can automatically discover both structured and semi-structured data stored in data lakes on Amazon S3, data warehouses in Amazon Redshift, and various databases running on AWS.
It provides a unified view of data via the Glue Data Catalog that is available for ETL, querying and reporting using services like Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
Glue automatically generates Scala or Python code for ETL jobs that you can further customize using tools you are already familiar with.
AWS Glue is serverless, so there are no compute resources to configure and manage.
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
You can create and run an ETL job with a few clicks in the AWS Management Console.
You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog.
Once cataloged, your data is immediately searchable, queryable, and available for ETL.
AWS Glue generates the code to execute your data transformations and data loading processes.