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	<title>Apache Iceberg &#8211; Noise</title>
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	<link>https://noise.getoto.net</link>
	<description>The collective thoughts of the interwebz</description>
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		<title>How Ancestry optimizes a 100-billion-row Iceberg table</title>
		<link>https://noise.getoto.net/2025/08/28/how-ancestry-optimizes-a-100-billion-row-iceberg-table/</link>
		
		<dc:creator><![CDATA[Thomas Cardenas]]></dc:creator>
		<pubDate>Wed, 27 Aug 2025 23:20:30 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=2b5ca7c84b93066175eb814c5b605874</guid>

					<description><![CDATA[This is a guest post by Thomas Cardenas, Staff Software Engineer at Ancestry, in partnership with AWS. Ancestry, the global leader in family history and consumer genomics, uses family trees, historical records, and DNA to help people on their journeys of personal discovery. Ancestry has the largest collection of family history records, consisting of 40 […]]]></description>
		
		
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		<item>
		<title>The Amazon SageMaker lakehouse architecture now automates optimization configuration of Apache Iceberg tables on Amazon S3</title>
		<link>https://noise.getoto.net/2025/08/09/the-amazon-sagemaker-lakehouse-architecture-now-automates-optimization-configuration-of-apache-iceberg-tables-on-amazon-s3/</link>
		
		<dc:creator><![CDATA[Tomohiro Tanaka]]></dc:creator>
		<pubDate>Fri, 08 Aug 2025 21:40:34 +0000</pubDate>
				<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Apache Iceberg]]></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=e551e1dfdb2e8fbbab8bf577301f8165</guid>

					<description><![CDATA[The Amazon SageMaker lakehouse architecture now automates optimization of Iceberg tables stored in Amazon S3 with catalog-level configuration, optimizing storage in your Iceberg tables and improving query performance. This post demonstrates an end-to-end flow to enable catalog level table optimization setting.]]></description>
		
		
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			</item>
		<item>
		<title>Configure cross-account access of Amazon SageMaker Lakehouse multi-catalog tables using AWS Glue 5.0 Spark</title>
		<link>https://noise.getoto.net/2025/05/09/configure-cross-account-access-of-amazon-sagemaker-lakehouse-multi-catalog-tables-using-aws-glue-5-0-spark/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Fri, 09 May 2025 17:18:44 +0000</pubDate>
				<category><![CDATA[*Learning Levels]]></category>
		<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon SageMaker Lakehouse]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></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=cc26012180b2a67d5ac39577ed6929eb</guid>

					<description><![CDATA[In this post, we show you how to share an Amazon Redshift table and Amazon S3 based Iceberg table from the account that owns the data to another account that consumes the data. In the recipient account, we run a join query on the shared data lake and data warehouse tables using Spark in AWS Glue 5.0. We walk you through the complete cross-account setup and provide the Spark configuration in a Python notebook.]]></description>
		
		
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		<item>
		<title>Build a high-performance quant research platform with Apache Iceberg</title>
		<link>https://noise.getoto.net/2025/01/09/build-a-high-performance-quant-research-platform-with-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Guy Bachar]]></dc:creator>
		<pubDate>Thu, 09 Jan 2025 20:55:39 +0000</pubDate>
				<category><![CDATA[Amazon EMR on EKS]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[EKS]]></category>
		<category><![CDATA[Financial Services]]></category>
		<category><![CDATA[open source]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=233ad968db2737c62263cd3357ac05de</guid>

					<description><![CDATA[In our previous post Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg, we showed how to use Apache Iceberg in the context of strategy backtesting. In this post, we focus on data management implementation options such as accessing data directly in Amazon Simple Storage Service (Amazon S3), using popular data formats like Parquet, or using open table formats like Iceberg. Our experiments are based on real-world historical full order book data, provided by our partner CryptoStruct, and compare the trade-offs between these choices, focusing on performance, cost, and quant developer productivity.]]></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>
		<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>
		<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>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=3d36ff8b5d175cfd0c5e52265479bb78</guid>

					<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>
		<item>
		<title>Modernize your legacy databases with AWS data lakes, Part 2: Build a data lake using AWS DMS data on Apache Iceberg</title>
		<link>https://noise.getoto.net/2024/10/30/modernize-your-legacy-databases-with-aws-data-lakes-part-2-build-a-data-lake-using-aws-dms-data-on-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Shaheer Mansoor]]></dc:creator>
		<pubDate>Wed, 30 Oct 2024 20:15:02 +0000</pubDate>
				<category><![CDATA[Amazon Simple Queue Service (SQS)]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Database Migration Service]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Big Data]]></category>
		<category><![CDATA[Data Catalog]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Events]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=3bd27ad09e40947c19d2f0eab552f51a</guid>

					<description><![CDATA[This is part two of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to load data from a legacy database (SQL Server) into a transactional data lake (Apache Iceberg) using AWS Glue. We show how to build data pipelines using AWS Glue jobs, optimize them for both cost and performance, and implement schema evolution to automate manual tasks. To review the first part of the series, where we load SQL Server data into Amazon Simple Storage Service (Amazon S3) using AWS Database Migration Service (AWS DMS), see Modernize your legacy databases with AWS data lakes, Part 1: Migrate SQL Server using AWS DMS.]]></description>
		
		
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		<item>
		<title>The AWS Glue Data Catalog now supports storage optimization of Apache Iceberg tables</title>
		<link>https://noise.getoto.net/2024/09/12/the-aws-glue-data-catalog-now-supports-storage-optimization-of-apache-iceberg-tables/</link>
		
		<dc:creator><![CDATA[Sandeep Adwankar]]></dc:creator>
		<pubDate>Thu, 12 Sep 2024 20:00:35 +0000</pubDate>
				<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=1ebd3f3106940f41e5fb3207a7ea4188</guid>

					<description><![CDATA[The AWS Glue Data Catalog now enhances managed table optimization of Apache Iceberg tables by automatically removing data files that are no longer needed. Along with the Glue Data Catalog’s automated compaction feature, these storage optimizations can help you reduce metadata overhead, control storage costs, and improve query performance. Iceberg creates a new version called […]]]></description>
		
		
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		<item>
		<title>Understanding Apache Iceberg on AWS with the new technical guide</title>
		<link>https://noise.getoto.net/2024/05/20/understanding-apache-iceberg-on-aws-with-the-new-technical-guide/</link>
		
		<dc:creator><![CDATA[Carlos Rodrigues]]></dc:creator>
		<pubDate>Mon, 20 May 2024 17:07:36 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=245456cfaaeba26ae562bf25c9e62878</guid>

					<description><![CDATA[We’re excited to announce the launch of the Apache Iceberg on AWS technical guide. Whether you are new to Apache Iceberg on AWS or already running production workloads on AWS, this comprehensive technical guide offers detailed guidance on foundational concepts to advanced optimizations to build your transactional data lake with Apache Iceberg on AWS.]]></description>
		
		
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		<item>
		<title>Incremental Processing using Netflix Maestro and Apache Iceberg</title>
		<link>https://noise.getoto.net/2023/11/21/incremental-processing-using-netflix-maestro-and-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 21 Nov 2023 05:49:38 +0000</pubDate>
				<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[Data Pipeline]]></category>
		<category><![CDATA[data-accuracy]]></category>
		<category><![CDATA[workflow]]></category>
		<guid isPermaLink="false">https://medium.com/p/b8ba072ddeeb</guid>

					<description><![CDATA[by Jun He, Yingyi Zhang, and Pawan DixitIncremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-proces...]]></description>
		
		
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		<item>
		<title>Migrate an existing data lake to a transactional data lake using Apache Iceberg</title>
		<link>https://noise.getoto.net/2023/10/03/migrate-an-existing-data-lake-to-a-transactional-data-lake-using-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Rajdip Chaudhuri]]></dc:creator>
		<pubDate>Tue, 03 Oct 2023 16:33:22 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Analytics]]></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=32b4d848bb0f2c90689e72b433187d32</guid>

					<description><![CDATA[A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Over the years, data lakes on Amazon Simple Storage […]]]></description>
		
		
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		<item>
		<title>Apache Iceberg optimization: Solving the small files problem in Amazon EMR</title>
		<link>https://noise.getoto.net/2023/10/03/apache-iceberg-optimization-solving-the-small-files-problem-in-amazon-emr/</link>
		
		<dc:creator><![CDATA[Avijit Goswami]]></dc:creator>
		<pubDate>Tue, 03 Oct 2023 16:27:13 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=0517074755c91fd6c568894acec27002</guid>

					<description><![CDATA[Currently, Iceberg provides a compaction utility that compacts small files at a table or partition level. But this approach requires you to implement the compaction job using your preferred job scheduler or manually triggering the compaction job. In this post, we discuss the new Iceberg feature that you can use to automatically compact small files while writing data into Iceberg tables using Spark on Amazon EMR or Amazon Athena.]]></description>
		
		
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		<item>
		<title>Perform upserts in a data lake using Amazon Athena and Apache Iceberg</title>
		<link>https://noise.getoto.net/2023/04/27/perform-upserts-in-a-data-lake-using-amazon-athena-and-apache-iceberg/</link>
		
		<dc:creator><![CDATA[Ranjit Rajan]]></dc:creator>
		<pubDate>Thu, 27 Apr 2023 15:50:37 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=c76fae285e6c455eaa63d1f0b7f8b1a9</guid>

					<description><![CDATA[Amazon Athena supports the MERGE command on Apache Iceberg tables, which allows you to perform inserts, updates, and deletes in your data lake at scale using familiar SQL statements that are compliant with ACID (Atomic, Consistent, Isolated, Durable). Apache Iceberg is an open table format for data lakes that manages large collections of files as […]]]></description>
		
		
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		<item>
		<title>Use Apache Iceberg in a data lake to support incremental data processing</title>
		<link>https://noise.getoto.net/2023/03/02/use-apache-iceberg-in-a-data-lake-to-support-incremental-data-processing/</link>
		
		<dc:creator><![CDATA[Flora Wu]]></dc:creator>
		<pubDate>Thu, 02 Mar 2023 20:42:59 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a65cfa50c237171177d680a8a4bcd720</guid>

					<description><![CDATA[Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. It adds tables to compute engines including Spark, Trino, PrestoDB, Flink, and Hive using a high-performance table format that works just like a SQL table. Iceberg has […]]]></description>
		
		
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		<item>
		<title>Build a real-time GDPR-aligned Apache Iceberg data lake</title>
		<link>https://noise.getoto.net/2023/02/24/build-a-real-time-gdpr-aligned-apache-iceberg-data-lake/</link>
		
		<dc:creator><![CDATA[Dhiraj Thakur]]></dc:creator>
		<pubDate>Fri, 24 Feb 2023 18:29:28 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=50cba7faab75aef386d8d4575480dfb4</guid>

					<description><![CDATA[Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. But regulations such as the General Data Protection Regulation (GDPR) have created obligations for data operators who must be able to erase or […]]]></description>
		
		
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		<title>Automate replication of relational sources into a transactional data lake with Apache Iceberg and AWS Glue</title>
		<link>https://noise.getoto.net/2023/02/14/automate-replication-of-relational-sources-into-a-transactional-data-lake-with-apache-iceberg-and-aws-glue/</link>
		
		<dc:creator><![CDATA[Luis Gerardo Baeza]]></dc:creator>
		<pubDate>Tue, 14 Feb 2023 21:32:22 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=704161540e4df3ace92f7553c197d6b0</guid>

					<description><![CDATA[Organizations have chosen to build data lakes on top of Amazon Simple Storage Service (Amazon S3) for many years. A data lake is the most popular choice for organizations to store all their organizational data generated by different teams, across business domains, from all different formats, and even over history. According to a study, the […]]]></description>
		
		
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		<title>Build a data lake with Apache Flink on Amazon EMR</title>
		<link>https://noise.getoto.net/2023/01/27/build-a-data-lake-with-apache-flink-on-amazon-emr/</link>
		
		<dc:creator><![CDATA[Jianwei Li]]></dc:creator>
		<pubDate>Fri, 27 Jan 2023 21:05:02 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[apache flink]]></category>
		<category><![CDATA[Apache Hudi]]></category>
		<category><![CDATA[Apache Iceberg]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=0e4f811fd115ff232a08ce87dbc76206</guid>

					<description><![CDATA[To build a data-driven business, it is important to democratize enterprise data assets in a data catalog. With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep […]]]></description>
		
		
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