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	<title>AWS Big Data &#8211; Noise</title>
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		<title>Orchestrating big data processing with AWS Step Functions Distributed Map</title>
		<link>https://noise.getoto.net/2025/11/05/orchestrating-big-data-processing-with-aws-step-functions-distributed-map/</link>
		
		<dc:creator><![CDATA[Biswanath Mukherjee]]></dc:creator>
		<pubDate>Tue, 04 Nov 2025 23:42:01 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, you'll learn how to use AWS Step Functions Distributed Map to process Amazon Athena data manifest and Parquet files through a step-by-step demonstration.]]></description>
		
		
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			</item>
		<item>
		<title>The Amazon SageMaker Lakehouse Architecture now supports Tag-Based Access Control for federated catalogs</title>
		<link>https://noise.getoto.net/2025/08/29/the-amazon-sagemaker-lakehouse-architecture-now-supports-tag-based-access-control-for-federated-catalogs/</link>
		
		<dc:creator><![CDATA[Sandeep Adwankar]]></dc:creator>
		<pubDate>Fri, 29 Aug 2025 18:31:04 +0000</pubDate>
				<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[We are now announcing support for Lake Formation tag-based access control (LF-TBAC) to federated catalogs of S3 Tables, Redshift data warehouses, and federated data sources such as Amazon DynamoDB, MySQL, PostgreSQL, SQL Server, Oracle, Amazon DocumentDB, Google BigQuery, and Snowflake. In this post, we illustrate how to manage S3 Tables and Redshift tables in the lakehouse using a single fine-grained access control mechanism of LF-TBAC. We also show how to access these lakehouse tables using your choice of analytics services, such as Athena, Redshift, and Apache Spark in Amazon EMR Serverless.]]></description>
		
		
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			</item>
		<item>
		<title>Improve Amazon EMR HBase availability and tail latency using generational ZGC</title>
		<link>https://noise.getoto.net/2025/08/19/improve-amazon-emr-hbase-availability-and-tail-latency-using-generational-zgc/</link>
		
		<dc:creator><![CDATA[Vishal Chaudhary]]></dc:creator>
		<pubDate>Tue, 19 Aug 2025 17:21:56 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[HBase]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f66e54202e55f89cec57aebbef7dcc5b</guid>

					<description><![CDATA[Large-scale HBase deployments on Amazon EMR suffer from unpredictable garbage collection behavior that creates performance bottlenecks for business-critical applications. To solve this problem, Amazon EMR leverages Oracle's generational ZGC technology from JDK 21 to deliver predictable, sub-millisecond pause times. This post shows you how to configure generational ZGC in Amazon EMR 7.10.0, apply performance tuning methods, and optimize HBase RegionServer garbage collection settings.]]></description>
		
		
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			</item>
		<item>
		<title>Enhance Amazon EMR observability with automated incident mitigation using Amazon Bedrock and Amazon Managed Grafana</title>
		<link>https://noise.getoto.net/2025/08/14/enhance-amazon-emr-observability-with-automated-incident-mitigation-using-amazon-bedrock-and-amazon-managed-grafana/</link>
		
		<dc:creator><![CDATA[Yu-Ting Su]]></dc:creator>
		<pubDate>Thu, 14 Aug 2025 15:25:16 +0000</pubDate>
				<category><![CDATA[Amazon Bedrock]]></category>
		<category><![CDATA[Amazon Bedrock Agents]]></category>
		<category><![CDATA[Amazon Bedrock Knowledge Bases]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon EventBridge]]></category>
		<category><![CDATA[Amazon Managed Grafana]]></category>
		<category><![CDATA[Amazon Managed Service for Prometheus]]></category>
		<category><![CDATA[Amazon Nova]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we demonstrate how to integrate real-time monitoring with AI-powered remediation suggestions, combining Amazon Managed Grafana for visualization, Amazon Bedrock for intelligent response recommendations, and AWS Systems Manager for automated remediation actions on Amazon Web Services (AWS).]]></description>
		
		
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			</item>
		<item>
		<title>Stream data from Amazon MSK to Apache Iceberg tables in Amazon S3 and Amazon S3 Tables using Amazon Data Firehose</title>
		<link>https://noise.getoto.net/2025/06/21/stream-data-from-amazon-msk-to-apache-iceberg-tables-in-amazon-s3-and-amazon-s3-tables-using-amazon-data-firehose/</link>
		
		<dc:creator><![CDATA[Pratik Patel]]></dc:creator>
		<pubDate>Fri, 20 Jun 2025 21:20:56 +0000</pubDate>
				<category><![CDATA[Amazon Data Firehose]]></category>
		<category><![CDATA[Amazon S3 Tables]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=efb861bdea8dbf5269a93d2dd324ea8a</guid>

					<description><![CDATA[In this post, we walk through two solutions that demonstrate how to stream data from your Amazon MSK provisioned cluster to Iceberg-based data lakes in Amazon S3 using Amazon Data Firehose.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Build a secure serverless streaming pipeline with Amazon MSK Serverless, Amazon EMR Serverless and IAM</title>
		<link>https://noise.getoto.net/2025/06/02/build-a-secure-serverless-streaming-pipeline-with-amazon-msk-serverless-amazon-emr-serverless-and-iam/</link>
		
		<dc:creator><![CDATA[Shubham Purwar]]></dc:creator>
		<pubDate>Mon, 02 Jun 2025 14:45:04 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=b1a37828eb0f71d100b2054b91dfd2a3</guid>

					<description><![CDATA[The post demonstrates a comprehensive, end-to-end solution for processing data from MSK Serverless using an EMR Serverless Spark Streaming job, secured with IAM authentication. Additionally, it demonstrates how to query the processed data using Amazon Athena, providing a seamless and integrated workflow for data processing and analysis. This solution enables near real-time querying of the latest data processed from MSK Serverless and EMR Serverless using Athena, providing instant insights and analytics.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Accelerate lightweight analytics using PyIceberg with AWS Lambda and an AWS Glue Iceberg REST endpoint</title>
		<link>https://noise.getoto.net/2025/05/09/accelerate-lightweight-analytics-using-pyiceberg-with-aws-lambda-and-an-aws-glue-iceberg-rest-endpoint/</link>
		
		<dc:creator><![CDATA[Sotaro Hikita]]></dc:creator>
		<pubDate>Fri, 09 May 2025 15:50:07 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c32dd913e60aa7e4db151d1bdfacdc70</guid>

					<description><![CDATA[In this post, we demonstrate how PyIceberg, integrated with the AWS Glue Data Catalog and AWS Lambda, provides a lightweight approach to harness Iceberg’s powerful features through intuitive Python interfaces. We show how this integration enables teams to start working with Iceberg tables with minimal setup and infrastructure dependencies.]]></description>
		
		
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			</item>
		<item>
		<title>Build end-to-end Apache Spark pipelines with Amazon MWAA, Batch Processing Gateway, and Amazon EMR on EKS clusters</title>
		<link>https://noise.getoto.net/2025/05/01/build-end-to-end-apache-spark-pipelines-with-amazon-mwaa-batch-processing-gateway-and-amazon-emr-on-eks-clusters/</link>
		
		<dc:creator><![CDATA[Avinash Desireddy]]></dc:creator>
		<pubDate>Thu, 01 May 2025 15:51:29 +0000</pubDate>
				<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[open source]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=caf1e550ef43020d339c155278b57566</guid>

					<description><![CDATA[This post shows how to enhance the multi-cluster solution by integrating Amazon Managed Workflows for Apache Airflow (Amazon MWAA) with BPG. By using Amazon MWAA, we add job scheduling and orchestration capabilities, enabling you to build a comprehensive end-to-end Spark-based data processing pipeline.]]></description>
		
		
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			</item>
		<item>
		<title>Best practices for least privilege configuration in Amazon MWAA</title>
		<link>https://noise.getoto.net/2025/04/29/best-practices-for-least-privilege-configuration-in-amazon-mwaa/</link>
		
		<dc:creator><![CDATA[Elizabeth Davis]]></dc:creator>
		<pubDate>Tue, 29 Apr 2025 16:32:12 +0000</pubDate>
				<category><![CDATA[Amazon Managed Workflows for Apache Airflow (Amazon MWAA)]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[security]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=cbf3bb7eb31771aa92d5bd30785be788</guid>

					<description><![CDATA[In this post, we explore how to apply the principle of least privilege to your Amazon MWAA environment by tightening network security using security groups, network access control lists (ACLs), and virtual private cloud (VPC) endpoints. We also discuss the Amazon MWAA execution and deployment roles and their respective permissions.]]></description>
		
		
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			</item>
		<item>
		<title>Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog</title>
		<link>https://noise.getoto.net/2025/04/08/manage-concurrent-write-conflicts-in-apache-iceberg-on-the-aws-glue-data-catalog/</link>
		
		<dc:creator><![CDATA[Sotaro Hikita]]></dc:creator>
		<pubDate>Tue, 08 Apr 2025 16:51:54 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9108ddd7d874df3b91bb40fc7e67b4b7</guid>

					<description><![CDATA[This post demonstrates how to implement reliable concurrent write handling mechanisms in Iceberg tables. We will explore Iceberg’s concurrency model, examine common conflict scenarios, and provide practical implementation patterns of both automatic retry mechanisms and situations requiring custom conflict resolution logic for building resilient data pipelines. We will also cover the pattern with automatic compaction through AWS Glue Data Catalog table optimization.]]></description>
		
		
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		<item>
		<title>Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow</title>
		<link>https://noise.getoto.net/2025/01/06/ingest-data-from-google-analytics-4-and-google-sheets-to-amazon-redshift-using-amazon-appflow/</link>
		
		<dc:creator><![CDATA[Ritesh Sinha]]></dc:creator>
		<pubDate>Mon, 06 Jan 2025 18:52:09 +0000</pubDate>
				<category><![CDATA[Amazon AppFlow]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[Migration]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=9fb472deb1cca8aa603da9daf4de4e8a</guid>

					<description><![CDATA[Amazon AppFlow bridges the gap between Google applications and Amazon Redshift, empowering organizations to unlock deeper insights and drive data-informed decisions. In this post, we show you how to establish the data ingestion pipeline between Google Analytics 4, Google Sheets, and an Amazon Redshift Serverless workgroup.]]></description>
		
		
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			</item>
		<item>
		<title>Amazon EMR 7.5 runtime for Apache Spark and Iceberg can run Spark workloads 3.6 times faster than Spark 3.5.3 and Iceberg 1.6.1</title>
		<link>https://noise.getoto.net/2024/12/27/amazon-emr-7-5-runtime-for-apache-spark-and-iceberg-can-run-spark-workloads-3-6-times-faster-than-spark-3-5-3-and-iceberg-1-6-1/</link>
		
		<dc:creator><![CDATA[Atul Payapilly]]></dc:creator>
		<pubDate>Fri, 27 Dec 2024 17:23:48 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=d31bfb2ababda8051e7404c0974ed20e</guid>

					<description><![CDATA[The Amazon EMR runtime for Apache Spark offers a high-performance runtime environment while maintaining 100% API compatibility with open source Apache Spark and Apache Iceberg table format. In this post, we demonstrate the performance benefits of using the Amazon EMR 7.5 runtime for Spark and Iceberg compared to open source Spark 3.5.3 with Iceberg 1.6.1 tables on the TPC-DS 3TB benchmark v2.13.]]></description>
		
		
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		<item>
		<title>Accelerate queries on Apache Iceberg tables through AWS Glue auto compaction</title>
		<link>https://noise.getoto.net/2024/12/19/accelerate-queries-on-apache-iceberg-tables-through-aws-glue-auto-compaction/</link>
		
		<dc:creator><![CDATA[Navnit Shukla]]></dc:creator>
		<pubDate>Thu, 19 Dec 2024 15:05:38 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=575e2cfb7e5a6d39501662408016d3b9</guid>

					<description><![CDATA[In this post, we explore new features of the AWS Glue Data Catalog, which now supports improved automatic compaction of Iceberg tables for streaming data, making it straightforward for you to keep your transactional data lakes consistently performant. Enabling automatic compaction on Iceberg tables reduces metadata overhead on your Iceberg tables and improves query performance]]></description>
		
		
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		<item>
		<title>How DeNA Co., Ltd. accelerated anonymized data quality tests up to 100 times faster using Amazon Redshift Serverless and dbt</title>
		<link>https://noise.getoto.net/2024/12/17/how-dena-co-ltd-accelerated-anonymized-data-quality-tests-up-to-100-times-faster-using-amazon-redshift-serverless-and-dbt/</link>
		
		<dc:creator><![CDATA[Momota Sasaki]]></dc:creator>
		<pubDate>Tue, 17 Dec 2024 16:40:39 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Healthcare]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=72d4f9911ef79398d697151ccae18c1b</guid>

					<description><![CDATA[DeNA Co., Ltd. (DeNA) engages in a variety of businesses, from games and live communities to sports &#38; the community and healthcare &#38; medical, under our mission to delight people beyond their wildest dreams. This post introduces a case study where DeNA combined Amazon Redshift Serverless and dbt (dbt Core) to accelerate data quality tests in their business.]]></description>
		
		
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		<item>
		<title>Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality</title>
		<link>https://noise.getoto.net/2024/12/10/build-write-audit-publish-pattern-with-apache-iceberg-branching-and-aws-glue-data-quality/</link>
		
		<dc:creator><![CDATA[Tomohiro Tanaka]]></dc:creator>
		<pubDate>Mon, 09 Dec 2024 22:24:12 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue Data Quality]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=48a4c450652dcfa868289e83ef8bd8c9</guid>

					<description><![CDATA[This post explores robust strategies for maintaining data quality when ingesting data into Apache Iceberg tables using AWS Glue Data Quality and Iceberg branches. We discuss two common strategies to verify the quality of published data. We dive deep into the Write-Audit-Publish (WAP) pattern, demonstrating how it works with Apache Iceberg.]]></description>
		
		
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		<item>
		<title>Implement historical record lookup and Slowly Changing Dimensions Type-2 using Apache Iceberg</title>
		<link>https://noise.getoto.net/2024/12/10/implement-historical-record-lookup-and-slowly-changing-dimensions-type-2-using-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Tomohiro Tanaka]]></dc:creator>
		<pubDate>Mon, 09 Dec 2024 22:21:41 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
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					<description><![CDATA[This post will explore how to look up the history of records and tables using Apache Iceberg, focusing on Slowly Changing Dimensions (SCD) Type-2. This method creates new records for each data change while preserving old ones, thus maintaining a full history. By the end, you'll understand how to use Apache Iceberg to manage historical records effectively on a typical CDC architecture.]]></description>
		
		
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		<item>
		<title>Use open table format libraries on AWS Glue 5.0 for Apache Spark</title>
		<link>https://noise.getoto.net/2024/12/04/use-open-table-format-libraries-on-aws-glue-5-0-for-apache-spark/</link>
		
		<dc:creator><![CDATA[Sotaro Hikita]]></dc:creator>
		<pubDate>Wed, 04 Dec 2024 19:04:31 +0000</pubDate>
				<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=49be600f3d32f1628be585b47349cf4d</guid>

					<description><![CDATA[Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. In earlier posts, we discussed AWS Glue 5.0 for Apache Spark. In this post, we highlight notable updates on Iceberg, Hudi, and Delta Lake in AWS Glue 5.0.]]></description>
		
		
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		<item>
		<title>Amazon EMR streamlines big data processing with simplified Amazon S3 Glacier access</title>
		<link>https://noise.getoto.net/2024/11/28/amazon-emr-streamlines-big-data-processing-with-simplified-amazon-s3-glacier-access/</link>
		
		<dc:creator><![CDATA[Giovanni Matteo Fumarola]]></dc:creator>
		<pubDate>Thu, 28 Nov 2024 00:44:06 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon S3 Glacier]]></category>
		<category><![CDATA[AWS Big Data]]></category>
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					<description><![CDATA[In this post, we demonstrate how to set up and use Amazon EMR on EC2 with S3 Glacier for cost-effective data processing.]]></description>
		
		
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		<title>Integrate custom applications with AWS Lake Formation – Part 1</title>
		<link>https://noise.getoto.net/2024/11/19/integrate-custom-applications-with-aws-lake-formation-part-1/</link>
		
		<dc:creator><![CDATA[Stefano Sandona]]></dc:creator>
		<pubDate>Tue, 19 Nov 2024 17:49:50 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Security & Governance]]></category>
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					<description><![CDATA[In this two-part series, we show how to integrate custom applications or data processing engines with Lake Formation using the third-party services integration feature. In this post, we dive deep into the required Lake Formation and AWS Glue APIs. We walk through the steps to enforce Lake Formation policies within custom data applications. As an example, we present a sample Lake Formation integrated application implemented using AWS Lambda.]]></description>
		
		
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		<item>
		<title>Integrate custom applications with AWS Lake Formation – Part 2</title>
		<link>https://noise.getoto.net/2024/11/19/integrate-custom-applications-with-aws-lake-formation-part-2/</link>
		
		<dc:creator><![CDATA[Stefano Sandona]]></dc:creator>
		<pubDate>Tue, 19 Nov 2024 17:49:12 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Amplify]]></category>
		<category><![CDATA[AWS AppSync]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Security & Governance]]></category>
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					<description><![CDATA[In this two-part series, we show how to integrate custom applications or data processing engines with Lake Formation using the third-party services integration feature. In this post, we explore how to deploy a fully functional web client application, built with JavaScript/React through AWS Amplify (Gen 1), that uses the same Lambda function as the backend. The provisioned web application provides a user-friendly and intuitive way to view the Lake Formation policies that have been enforced.]]></description>
		
		
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