Aug 19, 2021 · AWS Glue 3.0 introduces a performance-optimized Apache Spark 3.1 runtime for batch and stream processing. The new engine speeds up data ingestion, processing and integration allowing you to hydrate your data lake and extract insights from data quicker.

With AWS Glue, you can discover and connect to more than 70 diverse data sources and manage your data in a centralized data catalog. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

Feb 12, 2018 · You can configure the AWS Glue version parameter when you add or update a job. The AWS Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version that's supported for jobs of type Spark.

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Apr 14, 2022 · AWS Glue version 3.0 provides a performance-optimized Apache Spark 3.1 runtime experience for batch and stream processing. AWS Glue version 4.0, the latest version of AWS Glue Spark jobs, provides a performance-optimized Apache Spark 3.3 runtime experience. You can author AWS Glue jobs in different ways.

AWS Glue 3.0 uses Amazon EMR-optimized Spark 3.1.1, and AWS Glue 4.0 uses Amazon EMR-optimized Spark 3.3.0. Several Spark changes alone might require revision of your scripts to ensure that removed features are not being referenced.

Aug 19, 2021 · The AWS Glue 3.0 runtime optimizes both read and write access to Amazon Simple Storage Service (Amazon S3), using faster vectorized readers and Amazon S3 optimized output committers. It also optimizes access to the AWS Glue Data Catalog with the use of partition predicates.

Glue 4.0 vs. Glue 3.0. Let’s compare the architecture, features and job options of Glue 3.0 and Glue 4.0, which will help you decide if you can and want to upgrade your Glue 3.0 jobs to Glue 4.0. At the minimum, your jobs will benefit from the better performance of Glue 4.0.

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