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	<title>Noritaka Sekiyama &#8211; Noise</title>
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		<title>Scale your AWS Glue for Apache Spark jobs with R type, G.12X, and G.16X workers</title>
		<link>https://noise.getoto.net/2025/07/18/scale-your-aws-glue-for-apache-spark-jobs-with-r-type-g-12x-and-g-16x-workers/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Thu, 17 Jul 2025 21:07:52 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Access Amazon Redshift Managed Storage tables through Apache Spark on AWS Glue and Amazon EMR using Amazon SageMaker Lakehouse</title>
		<link>https://noise.getoto.net/2025/05/15/access-amazon-redshift-managed-storage-tables-through-apache-spark-on-aws-glue-and-amazon-emr-using-amazon-sagemaker-lakehouse/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Thu, 15 May 2025 17:25:07 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Unified scheduling for visual ETL flows and query books in Amazon SageMaker Unified Studio</title>
		<link>https://noise.getoto.net/2025/05/01/unified-scheduling-for-visual-etl-flows-and-query-books-in-amazon-sagemaker-unified-studio/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Wed, 30 Apr 2025 21:50:33 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[launch]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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		<item>
		<title>An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)</title>
		<link>https://noise.getoto.net/2024/12/11/an-integrated-experience-for-all-your-data-and-ai-with-amazon-sagemaker-unified-studio-preview/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Wed, 11 Dec 2024 16:58:08 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[launch]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=994e6af3dda086d5c175c899d231ac08</guid>

					<description><![CDATA[Amazon SageMaker Uniﬁed Studio, in preview, 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.]]></description>
		
		
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			</item>
		<item>
		<title>Introducing AWS Glue 5.0 for Apache Spark</title>
		<link>https://noise.getoto.net/2024/12/04/introducing-aws-glue-5-0-for-apache-spark/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 19:03:10 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9ce75c0a4fb222085f1644cde974e22d</guid>

					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Introducing generative AI troubleshooting for Apache Spark in AWS Glue (preview)</title>
		<link>https://noise.getoto.net/2024/11/22/introducing-generative-ai-troubleshooting-for-apache-spark-in-aws-glue-preview/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Fri, 22 Nov 2024 19:49:04 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[generative AI]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Introducing generative AI upgrades for Apache Spark in AWS Glue (preview)</title>
		<link>https://noise.getoto.net/2024/11/22/introducing-generative-ai-upgrades-for-apache-spark-in-aws-glue-preview/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Fri, 22 Nov 2024 19:46:51 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[generative AI]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>AWS Glue Data Catalog supports automatic optimization of Apache Iceberg tables through your Amazon VPC</title>
		<link>https://noise.getoto.net/2024/11/21/aws-glue-data-catalog-supports-automatic-optimization-of-apache-iceberg-tables-through-your-amazon-vpc/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Thu, 21 Nov 2024 21:19:49 +0000</pubDate>
				<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Introducing job queuing to scale your AWS Glue workloads</title>
		<link>https://noise.getoto.net/2024/09/03/introducing-job-queuing-to-scale-your-aws-glue-workloads/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Tue, 03 Sep 2024 17:42:12 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[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.]]></description>
		
		
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			</item>
		<item>
		<title>Migrate workloads from AWS Data Pipeline</title>
		<link>https://noise.getoto.net/2024/07/25/migrate-workloads-from-aws-data-pipeline/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Thu, 25 Jul 2024 16:24:59 +0000</pubDate>
				<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Data Pipeline]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a0045d08ee5fad3c39a7223f5a0edf2f</guid>

					<description><![CDATA[AWS Data Pipeline helps customers automate the movement and transformation of data. With Data Pipeline, customers can define data-driven workflows, so that tasks can be dependent on the successful completion of previous tasks. Launched in 2012, Data Pipeline predates several popular Amazon Web Services (AWS) offerings for orchestrating data pipelines such as AWS Glue, AWS […]]]></description>
		
		
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			</item>
		<item>
		<title>Introducing AWS Glue usage profiles for flexible cost control</title>
		<link>https://noise.getoto.net/2024/06/18/introducing-aws-glue-usage-profiles-for-flexible-cost-control/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Tue, 18 Jun 2024 16:59:38 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a4e4cfbe3e82155824fb9d9c163c0c1c</guid>

					<description><![CDATA[AWS Glue is a serverless data integration service that enables you to run extract, transform, and load (ETL) workloads on your data in a scalable and serverless manner. One of the main advantages of using a cloud platform is its flexibility; you can provision compute resources when you actually need them. However, with this ease […]]]></description>
		
		
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		<item>
		<title>Introducing Amazon Q data integration in AWS Glue</title>
		<link>https://noise.getoto.net/2024/04/30/introducing-amazon-q-data-integration-in-aws-glue/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Tue, 30 Apr 2024 12:20:25 +0000</pubDate>
				<category><![CDATA[Amazon Q]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=3cf3ca512c160fbe01aee1bcf16f0252</guid>

					<description><![CDATA[Today, we’re excited to announce general availability of Amazon Q data integration in AWS Glue. Amazon Q data integration, a new generative AI-powered capability of Amazon Q Developer, enables you to build data integration pipelines using natural language. This reduces the time and effort you need to learn, build, and run data integration jobs using […]]]></description>
		
		
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		<item>
		<title>Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight</title>
		<link>https://noise.getoto.net/2024/03/29/enhance-monitoring-and-debugging-for-aws-glue-jobs-using-new-job-observability-metrics-part-3-visualization-and-trend-analysis-using-amazon-quicksight/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Fri, 29 Mar 2024 14:59:04 +0000</pubDate>
				<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=d67501c879f6cb09e91b9dd4d7577afb</guid>

					<description><![CDATA[In Part 2 of this series, we discussed how to enable AWS Glue job observability metrics and integrate them with Grafana for real-time monitoring. Grafana provides powerful customizable dashboards to view pipeline health. However, to analyze trends over time, aggregate from different dimensions, and share insights across the organization, a purpose-built business intelligence (BI) tool […]]]></description>
		
		
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		<item>
		<title>Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics: Part 2</title>
		<link>https://noise.getoto.net/2024/02/13/enhance-monitoring-and-debugging-for-aws-glue-jobs-using-new-job-observability-metrics-part-2/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Tue, 13 Feb 2024 18:14:33 +0000</pubDate>
				<category><![CDATA[Amazon Managed Grafana]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=86893b33d3605f917b11c4b5974deefc</guid>

					<description><![CDATA[Monitoring data pipelines in real time is critical for catching issues early and minimizing disruptions. AWS Glue has made this more straightforward with the launch of AWS Glue job observability metrics, which provide valuable insights into your data integration pipelines built on AWS Glue. However, you might need to track key performance indicators across multiple […]]]></description>
		
		
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		<item>
		<title>New Amazon CloudWatch log class to cost-effectively scale your AWS Glue workloads</title>
		<link>https://noise.getoto.net/2023/12/20/new-amazon-cloudwatch-log-class-to-cost-effectively-scale-your-aws-glue-workloads/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Wed, 20 Dec 2023 18:14:46 +0000</pubDate>
				<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7680c24982cba19aebc9fc3cd1345090</guid>

					<description><![CDATA[AWS Glue is a serverless data integration service that makes it easier to discover, prepare, and combine data for analytics, machine learning (ML), and application development. You can use AWS Glue to create, run, and monitor data integration and ETL (extract, transform, and load) pipelines and catalog your assets across multiple data stores. One of […]]]></description>
		
		
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		<item>
		<title>Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter</title>
		<link>https://noise.getoto.net/2023/11/29/build-and-manage-your-modern-data-stack-using-dbt-and-aws-glue-through-dbt-glue-the-new-trusted-dbt-adapter/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Wed, 29 Nov 2023 20:14:21 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c96bb1c4b2c785f104e2ceaca0ac1924</guid>

					<description><![CDATA[dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt focuses on the transform layer of extract, load, transform (ELT) or extract, transform, load (ETL) processes across data warehouses and databases through specific engine adapters to achieve extract and load functionality. It […]]]></description>
		
		
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		<item>
		<title>Introducing Apache Hudi support with AWS Glue crawlers</title>
		<link>https://noise.getoto.net/2023/11/22/introducing-apache-hudi-support-with-aws-glue-crawlers/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Wed, 22 Nov 2023 14:09:06 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. Data engineers use Apache Hudi for streaming workloads as well as to create efficient incremental data pipelines. Hudi provides tables, transactions, efficient […]]]></description>
		
		
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		<title>Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics</title>
		<link>https://noise.getoto.net/2023/11/21/enhance-monitoring-and-debugging-for-aws-glue-jobs-using-new-job-observability-metrics/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Tue, 21 Nov 2023 01:06:56 +0000</pubDate>
				<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=b6d2927ea1f5d18c41a080b756b3dea3</guid>

					<description><![CDATA[For any modern data-driven company, having smooth data integration pipelines is crucial. These pipelines pull data from various sources, transform it, and load it into destination systems for analytics and reporting. When running properly, it provides timely and trustworthy information. However, without vigilance, the varying data volumes, characteristics, and application behavior can cause data pipelines […]]]></description>
		
		
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		<title>Introducing AWS Glue serverless Spark UI for better monitoring and troubleshooting</title>
		<link>https://noise.getoto.net/2023/11/21/introducing-aws-glue-serverless-spark-ui-for-better-monitoring-and-troubleshooting/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Tue, 21 Nov 2023 01:03:01 +0000</pubDate>
				<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[serverless]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f03d0262ae9d372fc097d9ac0116da54</guid>

					<description><![CDATA[Today, we are pleased to announce serverless Spark UI built into the AWS Glue console. You can now use Spark UI easily as it’s a built-in component of the AWS Glue console, enabling you to access it with a single click when examining the details of any given job run. There’s no infrastructure setup or teardown required. AWS Glue serverless Spark UI is a fully-managed serverless offering and generally starts up in a matter of seconds. Serverless Spark UI makes it significantly faster and easier to get jobs working in production because you have ready access to low level details for your job runs.]]></description>
		
		
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		<title>Load data incrementally from transactional data lakes to data warehouses</title>
		<link>https://noise.getoto.net/2023/10/19/load-data-incrementally-from-transactional-data-lakes-to-data-warehouses/</link>
		
		<dc:creator><![CDATA[Noritaka Sekiyama]]></dc:creator>
		<pubDate>Thu, 19 Oct 2023 16:54:57 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=47edc2ff91655e36ce9d01400d867fee</guid>

					<description><![CDATA[Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. An open table format such as Apache Hudi, Delta Lake, or Apache Iceberg is widely used to build data lakes […]]]></description>
		
		
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