Author: Noritaka Sekiyama

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.

With SageMaker Lakehouse, you can access tables stored in Amazon Redshift managed storage (RMS) through Iceberg APIs, using the Iceberg REST catalog backed by AWS Glue Data Catalog. This post describes how to integrate data on RMS tables through Apache Spark using SageMaker Unified Studio, Amazon EMR 7.5.0 and higher, and AWS Glue 5.0.

Today, we’re excited to introduce a new unified scheduling feature that simplifies this process. SageMaker Unified Studio allows you to create ETL flows using a visual interface and write SQL analytics queries using query books. In this post, we walk through how to schedule your visual ETL flows and query books with just a few clicks, explore the underlying architecture, and demonstrate how this feature can streamline your data workflow automation.

In this post we discuss integrating additional vital data sources such as Amazon Simple Storage Service (Amazon S3) buckets, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon EMR clusters. We demonstrate how to configure the necessary permissions, establish connections, and effectively use these resources within SageMaker Unified Studio. Whether you’re working with object storage, relational databases, NoSQL databases, or big data processing, this post can help you seamlessly incorporate your existing data infrastructure into your SageMaker Unified Studio workflows.

Amazon SageMaker Unified Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment. This post demonstrates how SageMaker Unified Studio unifies your analytic workloads.

Today, we are launching AWS Glue 5.0, a new version of AWS Glue that accelerates data integration workloads in AWS. AWS Glue 5.0 upgrades the Spark engines to Apache Spark 3.5.2 and Python 3.11, giving you newer Spark and Python releases so you can develop, run, and scale your data integration workloads and get insights faster. This post describes what’s new in AWS Glue 5.0, performance improvements, key highlights on Spark and related libraries, and how to get started on AWS Glue 5.0.

This post demonstrates how generative AI troubleshooting for Spark in AWS Glue helps your day-to-day Spark application debugging. It simplifies the debugging process for your Spark applications by using generative AI to automatically identify the root cause of failures and provides actionable recommendations to resolve the issues.

Today, we are excited to announce the preview of generative AI upgrades for Spark, a new capability that enables data practitioners to quickly upgrade and modernize their Spark applications running on AWS. Starting with Spark jobs in AWS Glue, this feature allows you to upgrade from an older AWS Glue version to AWS Glue version 4.0. This new capability reduces the time data engineers spend on modernizing their Spark applications, allowing them to focus on building new data pipelines and getting valuable analytics faster.

The AWS Glue Data Catalog supports automatic table optimization of Apache Iceberg tables, including compaction, snapshots, and orphan data management. The data compaction optimizer constantly monitors table partitions and kicks off the compaction process when the threshold is exceeded for the number of files and file sizes. This post demonstrates how it works with step-by-step instructions.

Today, we are pleased to announce the general availability of AWS Glue job queuing. Job queuing increases scalability and improves the customer experience of managing AWS Glue jobs. With this new capability, you no longer need to manage concurrency of your AWS Glue job runs and attempt retries just to avoid job failures due to high concurrency. This post demonstrates how job queuing helps you scale your Glue workloads and how job queuing works.