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	<title>Amazon Managed Workflows for Apache Airflow (Amazon MWAA) &#8211; Noise</title>
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		<title>Monitoring MWAA-orchestrated ETL pipelines with Amazon OpenSearch Service</title>
		<link>https://noise.getoto.net/2026/10/06/monitoring-mwaa-orchestrated-etl-pipelines-with-amazon-opensearch-service/</link>
		
		<dc:creator><![CDATA[Anupa Bhattacharyya]]></dc:creator>
		<pubDate>Tue, 06 Oct 2026 16:17:46 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Troubleshooting a multi-service, MWAA-orchestrated ETL pipeline often means hunting across scattered Amazon CloudWatch log groups. This post shows how to centralize your ETL logs in Amazon OpenSearch Service and query them in plain language through an MCP server on Amazon Bedrock AgentCore, so you find root causes faster.]]></description>
		
		
		
			</item>
		<item>
		<title>Building an LLM-powered DAG failure analysis plugin for Amazon MWAA</title>
		<link>https://noise.getoto.net/2026/09/29/building-an-llm-powered-dag-failure-analysis-plugin-for-amazon-mwaa/</link>
		
		<dc:creator><![CDATA[Sushant Samantaray]]></dc:creator>
		<pubDate>Tue, 29 Sep 2026 16:17:09 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Debugging Apache Airflow DAG failures across services like AWS Glue, Amazon EMR, and Amazon Athena is slow and manual. In this post, we show you how to build a custom Airflow plugin that integrates with Amazon Bedrock to automatically analyze DAG task failures and deliver on-demand root cause analysis on Amazon MWAA.]]></description>
		
		
		
			</item>
		<item>
		<title>PythonOperator and BashOperator Now Available on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless</title>
		<link>https://noise.getoto.net/2026/08/25/pythonoperator-and-bashoperator-now-available-on-amazon-managed-workflows-for-apache-airflow-amazon-mwaa-serverless/</link>
		
		<dc:creator><![CDATA[Pradeep Kumar Nalluri]]></dc:creator>
		<pubDate>Tue, 25 Aug 2026 19:28:00 +0000</pubDate>
				<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[You can now use PythonOperator and BashOperator to run custom Python functions and shell scripts directly in the Amazon MWAA Serverless runtime, without provisioning additional infrastructure. This post walks through building a serverless pipeline that converts CSV files to JSON using a PythonOperator and verifies the output with a BashOperator.]]></description>
		
		
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			</item>
		<item>
		<title>Event-driven pipeline orchestration with Amazon MWAA and Airflow 3.0</title>
		<link>https://noise.getoto.net/2026/08/06/event-driven-pipeline-orchestration-with-amazon-mwaa-and-airflow-3-0/</link>
		
		<dc:creator><![CDATA[Satya Chikkala]]></dc:creator>
		<pubDate>Thu, 06 Aug 2026 16:16:39 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Best practices]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=b5516b40ee14e6e19f82606dca7c5deb</guid>

					<description><![CDATA[Data engineering teams running Apache Airflow across multiple AWS accounts have no built-in way to coordinate workflows between separate Amazon MWAA environments. With Airflow 3.0 on Amazon MWAA, you can use asset-based scheduling and Asset Watchers with Amazon SQS to build event-driven, cross-account orchestration that replaces polling with near real-time triggers.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Building a scalable personalized recommendation system on AWS: From batch to real-time</title>
		<link>https://noise.getoto.net/2026/07/27/building-a-scalable-personalized-recommendation-system-on-aws-from-batch-to-real-time/</link>
		
		<dc:creator><![CDATA[Shraddha Anil Naik]]></dc:creator>
		<pubDate>Mon, 27 Jul 2026 16:09:17 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Best practices]]></category>
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					<description><![CDATA[Learn how the Everyday Essentials team built a scalable personalized recommendation platform on AWS using a batch-first architecture with Amazon MWAA for orchestration, Amazon SageMaker for training and vector search, and AWS Lake Formation for governed data access, then extended it to real-time with Amazon MemoryDB.]]></description>
		
		
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			</item>
		<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>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<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>
		
		
		<enclosure url="" length="0" type="" />

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		<item>
		<title>Why tombola chose Graviton-powered RG instances for Amazon Redshift</title>
		<link>https://noise.getoto.net/2026/06/22/why-tombola-chose-graviton-powered-rg-instances-for-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Prabhu Pandian]]></dc:creator>
		<pubDate>Mon, 22 Jun 2026 16:45:34 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Elastic Container Service]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Graviton]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=d112faae6dcc31f9ba5b86ef80f4d093</guid>

					<description><![CDATA[In this post, you learn how tombola followed a strict engineering principle: no changes to production without evidence. That meant a head-to-head comparison of RA3 versus RG on their actual workload. You also see benchmark results on Amazon S3 Tables and the migration from RA3 to RG instances.]]></description>
		
		
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			</item>
		<item>
		<title>Choosing the right workflow orchestration service for your use case: Amazon MWAA and AWS Step Functions</title>
		<link>https://noise.getoto.net/2026/06/10/choosing-the-right-workflow-orchestration-service-for-your-use-case-amazon-mwaa-and-aws-step-functions/</link>
		
		<dc:creator><![CDATA[Rajkumar Raghuwanshi]]></dc:creator>
		<pubDate>Wed, 10 Jun 2026 15:32:13 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[This post explores how to select the right workflow orchestration service based on your specific use case requirements. We'll examine key workflow characteristics, present real-world scenarios, and provide practical guidance to help you make an informed decision for your particular needs.]]></description>
		
		
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			</item>
		<item>
		<title>A guide to capacity planning for Airflow worker pool in Amazon MWAA</title>
		<link>https://noise.getoto.net/2026/05/01/a-guide-to-capacity-planning-for-airflow-worker-pool-in-amazon-mwaa/</link>
		
		<dc:creator><![CDATA[Boyko Radulov]]></dc:creator>
		<pubDate>Fri, 01 May 2026 15:42:45 +0000</pubDate>
				<category><![CDATA[Amazon CloudWatch]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Best practices]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=6480ee4a746d5a9a3253f1eb139f70d5</guid>

					<description><![CDATA[In our previous post, A guide to Airflow worker pool optimization in Amazon MWAA, we explored when adding workers to your Amazon Managed Workflows for Apache Airflow (Amazon MWAA) environment actually solves performance issues, and when it doesn’t. We walked through patterns like high CPU utilization and long queue times where scaling may be appropriate, […]]]></description>
		
		
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			</item>
		<item>
		<title>Automated tag-based DAG permission management in Amazon MWAA</title>
		<link>https://noise.getoto.net/2026/03/31/automated-tag-based-dag-permission-management-in-amazon-mwaa/</link>
		
		<dc:creator><![CDATA[Amey Ramakant Mhadgut]]></dc:creator>
		<pubDate>Tue, 31 Mar 2026 15:57:06 +0000</pubDate>
				<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=19ccec0a71d2c0782c35690c1412e3d9</guid>

					<description><![CDATA[In this post, we show you how to use Apache Airflow tags to systematically manage DAG permissions, reducing operational burden while maintaining robust security controls that complement infrastructure-level security measures.]]></description>
		
		
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			</item>
		<item>
		<title>Orchestrate end-to-end scalable ETL pipeline with Amazon SageMaker workflows</title>
		<link>https://noise.getoto.net/2026/02/10/orchestrate-end-to-end-scalable-etl-pipeline-with-amazon-sagemaker-workflows/</link>
		
		<dc:creator><![CDATA[Shubham Kumar]]></dc:creator>
		<pubDate>Mon, 09 Feb 2026 22:14:13 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=860679c55de06b06e7d385528c8926ce</guid>

					<description><![CDATA[This post explores how to build and manage a comprehensive extract, transform, and load (ETL) pipeline using SageMaker Unified Studio workflows through a code-based approach. We demonstrate how to use a single, integrated interface to handle all aspects of data processing, from preparation to orchestration, by using AWS services including Amazon EMR, AWS Glue, Amazon Redshift, and Amazon MWAA. This solution streamlines the data pipeline through a single UI.]]></description>
		
		
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			</item>
		<item>
		<title>How Tipico democratized data transformations using Amazon Managed Workflows for Apache Airflow and AWS Batch</title>
		<link>https://noise.getoto.net/2026/01/28/how-tipico-democratized-data-transformations-using-amazon-managed-workflows-for-apache-airflow-and-aws-batch/</link>
		
		<dc:creator><![CDATA[Jake J. Dalli]]></dc:creator>
		<pubDate>Wed, 28 Jan 2026 17:56:00 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[AWS Batch]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=15ad83b016b939333a431f0be10d9781</guid>

					<description><![CDATA[Tipico is the number one name in sports betting in Germany. Every day, we connect millions of fans to the thrill of sport, combining technology, passion, and trust to deliver fast, secure, and exciting betting, both online and in more than a thousand retail shops across Germany. We also bring this experience to Austria, where we proudly operate a strong sports betting business. In this post, we show how Tipico built a unified data transformation platform using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and AWS Batch.]]></description>
		
		
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		<item>
		<title>AWS analytics at re:Invent 2025: Unifying Data, AI, and governance at scale</title>
		<link>https://noise.getoto.net/2026/01/08/aws-analytics-at-reinvent-2025-unifying-data-ai-and-governance-at-scale/</link>
		
		<dc:creator><![CDATA[Larry Weber]]></dc:creator>
		<pubDate>Wed, 07 Jan 2026 22:44:25 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Data & AI Governance]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=ff20917a3d0a5ec52a0043e684e9594d</guid>

					<description><![CDATA[re:Invent 2025 showcased the bold Amazon Web Services (AWS) vision for the future of analytics, one where data warehouses, data lakes, and AI development converge into a seamless, open, intelligent platform, with Apache Iceberg compatibility at its core. Across over 18 major announcements spanning three weeks, AWS demonstrated how organizations can break down data silos, […]]]></description>
		
		
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		<item>
		<title>Building scalable AWS Lake Formation governed data lakes with dbt and Amazon Managed Workflows for Apache Airflow</title>
		<link>https://noise.getoto.net/2026/01/07/building-scalable-aws-lake-formation-governed-data-lakes-with-dbt-and-amazon-managed-workflows-for-apache-airflow/</link>
		
		<dc:creator><![CDATA[Abhilasha Agarwal]]></dc:creator>
		<pubDate>Tue, 06 Jan 2026 22:37:30 +0000</pubDate>
				<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Apache Airflow]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[dbt]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=8bb642af6120960db457446db26d022b</guid>

					<description><![CDATA[Organizations often struggle with building scalable and maintainable data lakes—especially when handling complex data transformations, enforcing data quality, and monitoring compliance with established governance. Traditional approaches typically involve custom scripts and disparate tools, which can increase operational overhead and complicate access control. A scalable, integrated approach is needed to simplify these processes, improve data reliability, […]]]></description>
		
		
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		<item>
		<title>Introducing Amazon MWAA Serverless</title>
		<link>https://noise.getoto.net/2025/11/18/introducing-amazon-mwaa-serverless/</link>
		
		<dc:creator><![CDATA[John Jackson]]></dc:creator>
		<pubDate>Mon, 17 Nov 2025 22:22:46 +0000</pubDate>
				<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[serverless]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=aa8da86ad002392fa1d9755c3ecc6c87</guid>

					<description><![CDATA[Today, AWS announced Amazon Managed Workflows for Apache Airflow (MWAA) Serverless. This is a new deployment option for MWAA that eliminates the operational overhead of managing Apache Airflow environments while optimizing costs through serverless scaling. In this post, we demonstrate how to use MWAA Serverless to build and deploy scalable workflow automation solutions.]]></description>
		
		
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		<item>
		<title>Best practices for migrating from Apache Airflow 2.x to Apache Airflow 3.x on Amazon MWAA</title>
		<link>https://noise.getoto.net/2025/10/07/best-practices-for-migrating-from-apache-airflow-2-x-to-apache-airflow-3-x-on-amazon-mwaa/</link>
		
		<dc:creator><![CDATA[Anurag Srivastava]]></dc:creator>
		<pubDate>Tue, 07 Oct 2025 17:31:02 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Best practices]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=8cdc889cdd85c1780767c71f9e4d8e15</guid>

					<description><![CDATA[Apache Airflow 3.x on Amazon MWAA introduces architectural improvements such as API-based task execution that provides enhanced security and isolation. This migration presents an opportunity to embrace next-generation workflow orchestration capabilities while providing business continuity. This post provides best practices and a streamlined approach to successfully navigate this critical migration, providing minimal disruption to your mission-critical data pipelines while maximizing the enhanced capabilities of Airflow 3.]]></description>
		
		
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		<item>
		<title>Introducing Apache Airflow 3 on Amazon MWAA: New features and capabilities</title>
		<link>https://noise.getoto.net/2025/10/01/introducing-apache-airflow-3-on-amazon-mwaa-new-features-and-capabilities/</link>
		
		<dc:creator><![CDATA[Anurag Srivastava]]></dc:creator>
		<pubDate>Wed, 01 Oct 2025 16:40:57 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[announcements]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c123a8ebfaf3581b7ba3b9de4b83c417</guid>

					<description><![CDATA[AWS announced the general availability of Apache Airflow 3 on Amazon Managed Workflows for Apache Airflow (Amazon MWAA). This release transforms how organizations use Apache Airflow to orchestrate data pipelines and business processes in the cloud, bringing enhanced security, improved performance, and modern workflow orchestration capabilities to Amazon MWAA customers. This post explores the features of Airflow 3 on Amazon MWAA and outlines enhancements that improve your workflow orchestration capabilities]]></description>
		
		
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		<title>Use Apache Airflow workflows to orchestrate data processing on Amazon SageMaker Unified Studio</title>
		<link>https://noise.getoto.net/2025/09/22/use-apache-airflow-workflows-to-orchestrate-data-processing-on-amazon-sagemaker-unified-studio/</link>
		
		<dc:creator><![CDATA[Vinod Jayendra]]></dc:creator>
		<pubDate>Mon, 22 Sep 2025 16:56:56 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon SageMaker Unified Studio]]></category>
		<category><![CDATA[Application Integration]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Orchestrating machine learning pipelines is complex, especially when data processing, training, and deployment span multiple services and tools. In this post, we walk through a hands-on, end-to-end example of developing, testing, and running a machine learning (ML) pipeline using workflow capabilities in Amazon SageMaker, accessed through the Amazon SageMaker Unified Studio experience. These workflows are powered by Amazon Managed Workflows for Apache Airflow.]]></description>
		
		
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		<item>
		<title>Build data pipelines with dbt in Amazon Redshift using Amazon MWAA and Cosmos</title>
		<link>https://noise.getoto.net/2025/08/13/build-data-pipelines-with-dbt-in-amazon-redshift-using-amazon-mwaa-and-cosmos/</link>
		
		<dc:creator><![CDATA[Cindy Li]]></dc:creator>
		<pubDate>Wed, 13 Aug 2025 20:12:37 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=98400f8be169603fd59f431df4e04840</guid>

					<description><![CDATA[In this post, we explore a streamlined, configuration-driven approach to orchestrate dbt Core jobs using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Cosmos, an open source package. These jobs run transformations on Amazon Redshift. With this setup, teams can collaborate effectively while maintaining data quality, operational efficiency, and observability.]]></description>
		
		
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