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	<title>AWS Glue &#8211; Noise</title>
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		<title>AWS Weekly Roundup: Prime Day 2026 by the numbers, Claude Haiku 5.5, Amazon Nova 2.5 Sonic, and more (October 13, 2026)</title>
		<link>https://noise.getoto.net/2026/10/13/aws-weekly-roundup-prime-day-2026-by-the-numbers-claude-haiku-5-5-amazon-nova-2-5-sonic-and-more-october-13-2026/</link>
		
		<dc:creator><![CDATA[Daniel Abib]]></dc:creator>
		<pubDate>Tue, 13 Oct 2026 15:59:06 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Aurora]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon GuardDuty]]></category>
		<category><![CDATA[Amazon Nova]]></category>
		<category><![CDATA[Amazon RDS]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Fault Injection Service (FIS)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[news]]></category>
		<category><![CDATA[Strands Agents]]></category>
		<category><![CDATA[Week in Review]]></category>
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					<description><![CDATA[Some numbers are hard to picture until you sit with them for a moment. Last week we published the Prime Day 2026 metrics, and the one that stuck with me is 2.3 quadrillion: the daily requests Amazon ElastiCache peaked at during the event. Prime Day ran back in June, so this post always lands as […]]]></description>
		
		
		
			</item>
		<item>
		<title>Bring data quality to your Amazon EMR workloads with Amazon EMR Data Quality built on AWS Glue Data Quality</title>
		<link>https://noise.getoto.net/2026/10/13/bring-data-quality-to-your-amazon-emr-workloads-with-amazon-emr-data-quality-built-on-aws-glue-data-quality/</link>
		
		<dc:creator><![CDATA[Mert Hocanin]]></dc:creator>
		<pubDate>Mon, 12 Oct 2026 23:40:26 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=09d92fe50d011c1aed630a52cc31ece4</guid>

					<description><![CDATA[AWS Glue Data Quality is now available natively on Amazon EMR, bundled in AWS runtime for Apache Spark (emr-spark-8.1.0). Write the same DQDL rulesets you use in AWS Glue directly in your Amazon EMR Spark jobs. This post walks through two patterns: lightweight inline validation and an Apache Iceberg Write-Audit-Publish workflow with managed rulesets.]]></description>
		
		
		
			</item>
		<item>
		<title>Cost-effective ETL with DuckDB and Amazon S3 Tables on AWS Glue</title>
		<link>https://noise.getoto.net/2026/10/06/cost-effective-etl-with-duckdb-and-amazon-s3-tables-on-aws-glue/</link>
		
		<dc:creator><![CDATA[Bezuayehu Wate]]></dc:creator>
		<pubDate>Tue, 06 Oct 2026 16:13:02 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f2935babab0bc167ff7f05026c640b08</guid>

					<description><![CDATA[Learn how to pair DuckDB with AWS Glue 6.0 to run SQL-centric ETL on a single worker, reading Parquet from Amazon S3 and writing Apache Iceberg tables to Amazon S3 Tables. This post walks through a complete, runnable example and compares measured cost and runtime against an equivalent Apache Spark job on the same Glue runtime.]]></description>
		
		
		
			</item>
		<item>
		<title>How United Airlines uses Amazon Redshift and AWS Glue Data Catalog federation to query Databricks-managed data</title>
		<link>https://noise.getoto.net/2026/09/15/how-united-airlines-uses-amazon-redshift-and-aws-glue-data-catalog-federation-to-query-databricks-managed-data/</link>
		
		<dc:creator><![CDATA[Vaibhav Agrawal]]></dc:creator>
		<pubDate>Tue, 15 Sep 2026 15:59:48 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a93c6134987cf96013b5829ee73bcc94</guid>

					<description><![CDATA[Learn how United Airlines uses AWS Glue Data Catalog federation to query Databricks Unity Catalog data directly from Amazon Redshift Serverless without duplicating data, using resource links and AWS Lake Formation for governance.]]></description>
		
		
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			</item>
		<item>
		<title>Accelerating Spark queries with Iceberg materialized views</title>
		<link>https://noise.getoto.net/2026/09/10/accelerating-spark-queries-with-iceberg-materialized-views/</link>
		
		<dc:creator><![CDATA[Yuzhou Sun]]></dc:creator>
		<pubDate>Thu, 10 Sep 2026 16:05:45 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=56a9627942a20dfc449b2a2cd05e5317</guid>

					<description><![CDATA[Accelerate slow, repetitive Apache Spark analytical queries on Apache Iceberg tables without rewriting any SQL. This post shows how automatic query rewrite in Amazon EMR and AWS Glue uses Iceberg materialized views in the AWS Glue Data Catalog to transparently substitute matching query plans, and how to design materialized views for the best speedup.]]></description>
		
		
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			</item>
		<item>
		<title>Build declarative ETL pipelines with AWS Glue 6.0</title>
		<link>https://noise.getoto.net/2026/09/09/build-declarative-etl-pipelines-with-aws-glue-6-0/</link>
		
		<dc:creator><![CDATA[Syed Humair]]></dc:creator>
		<pubDate>Wed, 09 Sep 2026 17:38:22 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=745ad5c9024a4ea3b80a2f6580c25da1</guid>

					<description><![CDATA[AWS Glue 6.0 introduces Spark Declarative Pipelines. In this post, you build a single declarative AWS Glue 6.0 job that turns raw order records into validated, aggregated tables through a bronze, silver, and gold sequence, without writing any orchestration logic.]]></description>
		
		
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			</item>
		<item>
		<title>Build a real-time event pipeline with Spark Real-Time Mode on AWS Glue 6.0</title>
		<link>https://noise.getoto.net/2026/08/31/build-a-real-time-event-pipeline-with-spark-real-time-mode-on-aws-glue-6-0/</link>
		
		<dc:creator><![CDATA[Shoukat Ghouse]]></dc:creator>
		<pubDate>Mon, 31 Aug 2026 16:22:11 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[With AWS Glue 6.0, you can build real-time, near-real-time, and batch data pipelines on a single platform. Using a financial market-risk example, learn how to flag high-risk trades with sub-second latency using Spark Real-Time Mode, store heterogeneous pricing vectors with Apache Iceberg v3 Variant columns, and run batch analytics with Arrow-native UDFs.]]></description>
		
		
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			</item>
		<item>
		<title>Build with geospatial and variant types in Iceberg v3 on AWS Glue 6.0</title>
		<link>https://noise.getoto.net/2026/08/27/build-with-geospatial-and-variant-types-in-iceberg-v3-on-aws-glue-6-0/</link>
		
		<dc:creator><![CDATA[Shoukat Ghouse]]></dc:creator>
		<pubDate>Thu, 27 Aug 2026 16:00:12 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a02017c872576cc26856397b8d4fb2a6</guid>

					<description><![CDATA[AWS Glue 6.0 with Apache Spark 4.1 adds support for Apache Iceberg v3: native geospatial types, nanosecond-precision timestamps, the VARIANT type, and DEFAULT column values. This post builds a connected vehicle fleet telemetry pipeline that uses all four in a single Iceberg v3 table, from ingestion through spatial, nanosecond, and variant queries.]]></description>
		
		
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			</item>
		<item>
		<title>Introducing AWS Glue 6.0 for faster and more cost-effective data integration</title>
		<link>https://noise.getoto.net/2026/08/24/introducing-aws-glue-6-0-for-faster-and-more-cost-effective-data-integration/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Mon, 24 Aug 2026 19:06:39 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=1a8dfd6c34079ad86a096582b766be82</guid>

					<description><![CDATA[AWS Glue 6.0 is now available, lowering AWS Glue pricing by 30%, adding an AWS optimized build of Apache Spark 4.1, and introducing Apache Iceberg V3 capabilities suitable for enterprise adoption. This post covers the key capabilities and performance benefits, with code examples to help you get started.]]></description>
		
		
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			</item>
		<item>
		<title>Upgrade AWS Glue jobs to Glue 6.0 with AI-powered Spark upgrades</title>
		<link>https://noise.getoto.net/2026/08/24/upgrade-aws-glue-jobs-to-glue-6-0-with-ai-powered-spark-upgrades/</link>
		
		<dc:creator><![CDATA[Prasad Nadig]]></dc:creator>
		<pubDate>Mon, 24 Aug 2026 19:06:16 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=aed5c037bac094718dd9fdbfbeb11e63</guid>

					<description><![CDATA[Walk through upgrading a PySpark ETL job from AWS Glue 5.1 to AWS Glue 6.0 using the generative AI upgrades for Apache Spark. The upgrade analysis automatically detects incompatibilities, applies fixes, and validates results with data quality checks.]]></description>
		
		
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			</item>
		<item>
		<title>AWS Weekly Roundup: Student Rewards on AWS Builder Center, Local Zone in Las Vegas, and more (August 24, 2026)</title>
		<link>https://noise.getoto.net/2026/08/24/aws-weekly-roundup-student-rewards-on-aws-builder-center-local-zone-in-las-vegas-and-more-august-24-2026/</link>
		
		<dc:creator><![CDATA[Esra Kayabali]]></dc:creator>
		<pubDate>Mon, 24 Aug 2026 16:20:15 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon WorkSpaces]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS CloudShell]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Local Zones]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
		<category><![CDATA[Week in Review]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=8f888e644234c0df882abe3715efac51</guid>

					<description><![CDATA[During my time at AWS, I have always looked for opportunities to work with students. I have delivered over 50 talks at universities across the region, and watching the potential in the room is always a strong motivator. It reminds me of why I do this work, and that the students I meet today may […]]]></description>
		
		
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		<item>
		<title>AWS Glue 6.0 now available with 30% lower price and full Apache Iceberg v3 support</title>
		<link>https://noise.getoto.net/2026/08/21/aws-glue-6-0-now-available-with-30-lower-price-and-full-apache-iceberg-v3-support/</link>
		
		<dc:creator><![CDATA[Channy Yun (윤석찬)]]></dc:creator>
		<pubDate>Fri, 21 Aug 2026 18:53:26 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[launch]]></category>
		<category><![CDATA[news]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=065ea45a39a6cbc1bcc08cdcf0a18ba4</guid>

					<description><![CDATA[AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.12, and Scala 2.13, delivering 30% lower pricing than previous AWS Glue versions.]]></description>
		
		
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			</item>
		<item>
		<title>Automate creating AWS Glue Data Catalog views with AWS SDK for data mesh use case</title>
		<link>https://noise.getoto.net/2026/07/24/automate-creating-aws-glue-data-catalog-views-with-aws-sdk-for-data-mesh-use-case/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Fri, 24 Jul 2026 16:05:07 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f7c113f5ba6baed8c648bd6999f37886</guid>

					<description><![CDATA[This post shows you how to use the Catalog objects API CreateTable() to programmatically create ATHENA and SPARK dialects using cross-account IAM definer roles, and how to add the ATHENA dialect programmatically for the views that were created earlier with only SPARK dialect.]]></description>
		
		
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		<item>
		<title>How Mapfre USA modernized fraud claims with Amazon EMR Serverless</title>
		<link>https://noise.getoto.net/2026/07/15/how-mapfre-usa-modernized-fraud-claims-with-amazon-emr-serverless-2/</link>
		
		<dc:creator><![CDATA[Lijan Kuniyil]]></dc:creator>
		<pubDate>Tue, 14 Jul 2026 21:47:35 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[AWS Fargate]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Secrets Manager]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e1b3a39ce6eb74cb6dc1da07482b9c92</guid>

					<description><![CDATA[Insurance fraud remains a significant challenge for the insurance industry because fraudulent claims can increase loss costs, reduce trust, and consume investigation capacity that could otherwise be focused on serving customers. Traditional fraud detection approaches typically rely on rules-based controls, manual investigation triggers, historical claim patterns, and structured-data-only analysis. These approaches are useful for known […]]]></description>
		
		
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		<item>
		<title>Multi-cloud lakehouse architecture on AWS for Agentic AI, Part 1: Architecture and best practices</title>
		<link>https://noise.getoto.net/2026/07/13/multi-cloud-lakehouse-architecture-on-aws-for-agentic-ai-part-1-architecture-and-best-practices/</link>
		
		<dc:creator><![CDATA[Sakti Mishra]]></dc:creator>
		<pubDate>Mon, 13 Jul 2026 17:02:40 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Bedrock AgentCore]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a03b3dc738f79d55acf66a71db609d95</guid>

					<description><![CDATA[This post focuses on explaining the architecture approach to build the open lakehouse architecture on AWS, unifying the metadata catalog across providers for the AI agents to access. In addition, it highlights the architecture trade-offs and best practices.]]></description>
		
		
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		<title>How MAPFRE USA modernized fraud claims with Amazon EMR Serverless</title>
		<link>https://noise.getoto.net/2026/07/13/how-mapfre-usa-modernized-fraud-claims-with-amazon-emr-serverless/</link>
		
		<dc:creator><![CDATA[Lijan Kuniyil]]></dc:creator>
		<pubDate>Mon, 13 Jul 2026 09:26:59 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Financial Services]]></category>
		<category><![CDATA[generative AI]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=189d601872bea283915cea88c972a2f0</guid>

					<description><![CDATA[Insurance fraud remains a significant challenge for the insurance industry. Fraudulent claims can increase loss costs, reduce trust, and consume investigation capacity that could otherwise be focused on serving customers. Traditional fraud detection approaches typically rely on rules-based controls, manual investigation triggers, historical claim patterns, and structured-data-only analysis. These approaches are useful for known fraud […]]]></description>
		
		
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		<title>Introducing Apache Spark Connect support in AWS Glue interactive sessions</title>
		<link>https://noise.getoto.net/2026/07/07/introducing-apache-spark-connect-support-in-aws-glue-interactive-sessions/</link>
		
		<dc:creator><![CDATA[Zach Mitchell]]></dc:creator>
		<pubDate>Tue, 07 Jul 2026 16:38:42 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=ecadde28d61f9582826cc49377627333</guid>

					<description><![CDATA[Apache Spark Connect bridges the gap between these two worlds: you develop in local Python, but execute on AWS Glue against actual data. Today, AWS Glue interactive sessions support Spark Connect natively. You can connect from any environment that supports the PySpark remote() API, including VS Code, PyCharm, Amazon SageMaker Unified Studio notebooks, and standalone Python applications. You don’t need to install specialized kernels or manage cluster infrastructure.]]></description>
		
		
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		<title>How BigBasket uses the Iceberg based lakehouse architecture on AWS to power lightning-fast grocery delivery across India</title>
		<link>https://noise.getoto.net/2026/07/06/how-bigbasket-uses-the-iceberg-based-lakehouse-architecture-on-aws-to-power-lightning-fast-grocery-delivery-across-india/</link>
		
		<dc:creator><![CDATA[Annie Mattoo]]></dc:creator>
		<pubDate>Mon, 06 Jul 2026 16:50:17 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Quick Sight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a58db9aae671ef573b0e4a36a50351a0</guid>

					<description><![CDATA[In this post, we demonstrate how BigBasket implemented the lakehouse architecture on AWS, including their architecture decisions, implementation approach, and the measurable business results you can expect from a similar modernization. Whether you’re facing scalability challenges or planning your own lakehouse implementation, this blueprint provides actionable insights you can adapt for your organization.]]></description>
		
		
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		<title>Accelerating log analytics at scale with AWS Glue and Apache Iceberg materialized views</title>
		<link>https://noise.getoto.net/2026/07/02/accelerating-log-analytics-at-scale-with-aws-glue-and-apache-iceberg-materialized-views/</link>
		
		<dc:creator><![CDATA[Shinu Tharol]]></dc:creator>
		<pubDate>Thu, 02 Jul 2026 17:46:18 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Data Firehose]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=5f599f22a4a4cf02c1e297100fbf925c</guid>

					<description><![CDATA[In this post, you learn how to build an application log pipeline for production use with Amazon CloudWatch Logs, AWS Lambda, Amazon Data Firehose, AWS Glue, and Apache Iceberg materialized tables. You then use materialized views to accelerate query performance. This solution helps you achieve faster query response times on large-scale log data without requiring you to manage continuous data lake refresh.]]></description>
		
		
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		<title>Deploy modern data platforms in minutes with MDAA</title>
		<link>https://noise.getoto.net/2026/07/02/deploy-modern-data-platforms-in-minutes-with-mdaa/</link>
		
		<dc:creator><![CDATA[Sudeshna Dash]]></dc:creator>
		<pubDate>Thu, 02 Jul 2026 16:26:28 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
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		<category><![CDATA[AWS CloudTrail]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Key Management Service*]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.]]></description>
		
		
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