Category: AWS Glue

This post demonstrates how AWS Glue R type, G.12X, and G.16X workers help you scale up your AWS Glue for Apache Spark jobs.

This post demonstrates how the new jobs experience works in SageMaker Unified Studio.

Revenue NSW, Australia’s principal revenue management agency, successfully modernized its analytics infrastructure using AWS services. In this blog post, we show how the organization transformed its on-premises data environment into a unified, scalable cloud-based solution using Amazon Redshift, AWS Database Migration Service, Amazon AppFlow, and AWS Glue.

In this post, we review how to set up Redshift Serverless to use geospatial data contained within a data lake to enhance maps in ArcGIS Pro. This technique helps builders and GIS analysts use available datasets in data lakes and transform it in Amazon Redshift to further enrich the data before presenting it on a map.

In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products.

In this post, we show you how to enforce FTA control on AWS Glue 5.0 through Lake Formation permissions.

We’re excited to announce AWS Glue Data Catalog usage metrics. The usage metrics is a new feature that provides native integration with Amazon CloudWatch. In this post, we demonstrate how to access these metrics, provide a step-by-step walkthrough, and set up meaningful alarms.

This post explores RocksDB’s key features and demonstrates its implementation using Spark on Amazon EMR and AWS Glue, providing you with the knowledge you need to scale your real-time data processing capabilities.

At AWS re:Invent 2024, we introduced a no code zero-ETL integration between Amazon DynamoDB and Amazon SageMaker Lakehouse, simplifying how organizations handle data analytics and AI workflows. In this post, we share how to set up this zero-ETL integration from DynamoDB to your SageMaker Lakehouse environment.

In this post, we guide you through the process of creating a Data Catalog view using EMR Serverless, adding the SQL dialect to the view for Athena, sharing it with another account using LF-Tags, and then querying the view in the recipient account using a separate EMR Serverless workspace and AWS Glue 5.0 Spark job and Athena. This demonstration showcases the versatility and cross-account capabilities of Data Catalog views and access through various AWS analytics services.