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	<title>AWS Analytics &#8211; Noise</title>
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	<link>https://noise.getoto.net</link>
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		<title>Export JMX metrics from Kafka connectors in Amazon Managed Streaming for Apache Kafka Connect with a custom plugin</title>
		<link>https://noise.getoto.net/2025/08/15/export-jmx-metrics-from-kafka-connectors-in-amazon-managed-streaming-for-apache-kafka-connect-with-a-custom-plugin/</link>
		
		<dc:creator><![CDATA[Jaydev Nath]]></dc:creator>
		<pubDate>Fri, 15 Aug 2025 15:51:01 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Amazon MSK]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Big Data]]></category>
		<category><![CDATA[MSK Connect]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we demonstrate how you can export the JMX metrics for Debezium connector when used with Amazon MSK Connect.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Use Databricks Unity Catalog Open APIs for Spark workloads on Amazon EMR</title>
		<link>https://noise.getoto.net/2025/07/25/use-databricks-unity-catalog-open-apis-for-spark-workloads-on-amazon-emr/</link>
		
		<dc:creator><![CDATA[Venkat Viswanathan]]></dc:creator>
		<pubDate>Fri, 25 Jul 2025 16:18:02 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Data Catalog]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In this post, we demonstrate the powerful interoperability between Amazon EMR and Databricks Unity Catalog by walking through how to enable external access to Unity Catalog, configure EMR Spark to connect seamlessly with Unity Catalog, and perform DML and DDL operations on Unity Catalog tables using EMR Serverless.]]></description>
		
		
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			</item>
		<item>
		<title>Read and write Apache Iceberg tables using AWS Lake Formation hybrid access mode</title>
		<link>https://noise.getoto.net/2025/04/21/read-and-write-apache-iceberg-tables-using-aws-lake-formation-hybrid-access-mode/</link>
		
		<dc:creator><![CDATA[Aarthi Srinivasan]]></dc:creator>
		<pubDate>Mon, 21 Apr 2025 17:10:48 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
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					<description><![CDATA[In this post, we demonstrate how to use Lake Formation for read access while continuing to use AWS Identity and Access Management (IAM) policy-based permissions for write workloads that update the schema and upsert (insert and update combined) data records into the Iceberg tables.]]></description>
		
		
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			</item>
		<item>
		<title>Unlock the power of optimization in Amazon Redshift Serverless</title>
		<link>https://noise.getoto.net/2025/03/10/unlock-the-power-of-optimization-in-amazon-redshift-serverless/</link>
		
		<dc:creator><![CDATA[Ricardo Serafim]]></dc:creator>
		<pubDate>Mon, 10 Mar 2025 20:38:10 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Redshift Serverless]]></category>
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					<description><![CDATA[In this post, we demonstrate how Amazon Redshift Serverless AI-driven scaling and optimization impacts performance and cost across different optimization profiles.]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>How Getir unleashed data democratization using a data mesh architecture with Amazon Redshift</title>
		<link>https://noise.getoto.net/2024/10/23/how-getir-unleashed-data-democratization-using-a-data-mesh-architecture-with-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Asser Moustafa]]></dc:creator>
		<pubDate>Wed, 23 Oct 2024 15:52:23 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift data sharing]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Big Data]]></category>
		<category><![CDATA[Customer Solutions]]></category>
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					<description><![CDATA[In this post, we explain how ultrafast delivery pioneer, Getir, unleashed the power of data democratization on a large scale through their data mesh architecture using Amazon Redshift. We start by introducing Getir and their vision—to seamlessly, securely, and efficiently share business data across different teams within the organization for BI, extract, transform, and load (ETL), and other use cases. We’ll then explore how Amazon Redshift data sharing powered the data mesh architecture that allowed Getir to achieve this transformative vision.]]></description>
		
		
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			</item>
		<item>
		<title>Apply fine-grained access and transformation on the SUPER data type in Amazon Redshift</title>
		<link>https://noise.getoto.net/2024/06/19/apply-fine-grained-access-and-transformation-on-the-super-data-type-in-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Ritesh Sinha]]></dc:creator>
		<pubDate>Wed, 19 Jun 2024 14:17:36 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7f217be8a138edc53aaa638f47e2cab1</guid>

					<description><![CDATA[Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per […]]]></description>
		
		
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			</item>
		<item>
		<title>Detect and handle data skew on AWS Glue</title>
		<link>https://noise.getoto.net/2024/05/01/detect-and-handle-data-skew-on-aws-glue/</link>
		
		<dc:creator><![CDATA[Salim Tutuncu]]></dc:creator>
		<pubDate>Wed, 01 May 2024 16:27:24 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Apache Spark]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Big Data]]></category>
		<category><![CDATA[Expert (400)]]></category>
		<category><![CDATA[Optimization]]></category>
		<category><![CDATA[Spark]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[AWS Glue is a fully managed, serverless data integration service provided by Amazon Web Services (AWS) that uses Apache Spark as one of its backend processing engines (as of this writing, you can use Python Shell, Spark, or Ray). Data skew occurs when the data being processed is not evenly distributed across the Spark cluster, […]]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>GoDaddy benchmarking results in up to 24% better price-performance for their Spark workloads with AWS Graviton2 on Amazon EMR Serverless</title>
		<link>https://noise.getoto.net/2023/11/02/godaddy-benchmarking-results-in-up-to-24-better-price-performance-for-their-spark-workloads-with-aws-graviton2-on-amazon-emr-serverless/</link>
		
		<dc:creator><![CDATA[Mukul Sharma]]></dc:creator>
		<pubDate>Thu, 02 Nov 2023 15:58:33 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Graviton]]></category>
		<category><![CDATA[serverless]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e22a73bd4d98f200f144d18e158af3cc</guid>

					<description><![CDATA[This is a guest post co-written with Mukul Sharma, Software Development Engineer, and Ozcan IIikhan, Director of Engineering from GoDaddy. GoDaddy empowers everyday entrepreneurs by providing all the help and tools to succeed online. With more than 22 million customers worldwide, GoDaddy is the place people come to name their ideas, build a professional website, […]]]></description>
		
		
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			</item>
		<item>
		<title>Empower your Jira data in a data lake with Amazon AppFlow and AWS Glue</title>
		<link>https://noise.getoto.net/2023/08/01/empower-your-jira-data-in-a-data-lake-with-amazon-appflow-and-aws-glue/</link>
		
		<dc:creator><![CDATA[Tom Romano]]></dc:creator>
		<pubDate>Tue, 01 Aug 2023 16:14:01 +0000</pubDate>
				<category><![CDATA[Amazon AppFlow]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue DataBrew]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[SaaS]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]]]></description>
		
		
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			</item>
		<item>
		<title>Top Amazon QuickSight features launched in Q2 2022</title>
		<link>https://noise.getoto.net/2022/08/26/top-amazon-quicksight-features-launched-in-q2-2022/</link>
		
		<dc:creator><![CDATA[Sindhu Chandra]]></dc:creator>
		<pubDate>Fri, 26 Aug 2022 19:42:01 +0000</pubDate>
				<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=7da92d43de1da385bbc1b62c769c1d94</guid>

					<description><![CDATA[Amazon QuickSight is a serverless, cloud-based business intelligence (BI) service that brings data insights to your teams and end-users through machine learning (ML)-powered dashboards and data visualizations, which can be accessed via QuickSight or embedded in apps and portals that your users access. This post shares the top QuickSight features and updates launched in Q2 […]]]></description>
		
		
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			</item>
		<item>
		<title>From centralized architecture to decentralized architecture: How data sharing fine-tunes Amazon Redshift workloads</title>
		<link>https://noise.getoto.net/2022/08/16/from-centralized-architecture-to-decentralized-architecture-how-data-sharing-fine-tunes-amazon-redshift-workloads/</link>
		
		<dc:creator><![CDATA[Jingbin Ma]]></dc:creator>
		<pubDate>Tue, 16 Aug 2022 17:53:16 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift data sharing]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Big Data]]></category>
		<category><![CDATA[Optimization]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=2ba6cdbea875751a5afa9a8b02c8f263</guid>

					<description><![CDATA[Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that offers simple operations and high performance. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Today, Amazon Redshift has become the most widely used cloud data warehouse. With the significant […]]]></description>
		
		
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			</item>
		<item>
		<title>Optimize Federated Query Performance using EXPLAIN and EXPLAIN ANALYZE in Amazon Athena</title>
		<link>https://noise.getoto.net/2022/06/14/optimize-federated-query-performance-using-explain-and-explain-analyze-in-amazon-athena/</link>
		
		<dc:creator><![CDATA[Nishchai JM]]></dc:creator>
		<pubDate>Tue, 14 Jun 2022 19:39:15 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon RDS]]></category>
		<category><![CDATA[Amazon S3]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[DynamoDB]]></category>
		<category><![CDATA[RDS for MySQL]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=00af45fe6a9b469d8b9f9036820b592a</guid>

					<description><![CDATA[Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In 2019, Athena added support for federated queries to run SQL […]]]></description>
		
		
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		<item>
		<title>Simplify and optimize Python package management for AWS Glue PySpark jobs with AWS CodeArtifact</title>
		<link>https://noise.getoto.net/2022/06/10/simplify-and-optimize-python-package-management-for-aws-glue-pyspark-jobs-with-aws-codeartifact/</link>
		
		<dc:creator><![CDATA[Ashok Padmanabhan]]></dc:creator>
		<pubDate>Thu, 09 Jun 2022 21:03:52 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS CodeArtifact]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Developer Tools]]></category>
		<category><![CDATA[devops]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[Spark]]></category>
		<category><![CDATA[Technical How-to]]></category>
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					<description><![CDATA[Data engineers use various Python packages to meet their data processing requirements while building data pipelines with AWS Glue PySpark Jobs. Languages like Python and Scala are commonly used in data pipeline development. Developers can take advantage of their open-source packages or even customize their own to make it easier and faster to perform use […]]]></description>
		
		
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		<item>
		<title>Choose the right storage tier for your needs in Amazon OpenSearch Service</title>
		<link>https://noise.getoto.net/2021/11/23/choose-the-right-storage-tier-for-your-needs-in-amazon-opensearch-service/</link>
		
		<dc:creator><![CDATA[Changbin Gong]]></dc:creator>
		<pubDate>Tue, 23 Nov 2021 16:53:51 +0000</pubDate>
				<category><![CDATA[Amazon OpenSearch]]></category>
		<category><![CDATA[Amazon OpenSearch Service (Successor to Amazon Elasticsearch Service)]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Best practices]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=5899bf1656b4c2a19c0fb055cbdc4e1b</guid>

					<description><![CDATA[Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) enables organizations to perform interactive log analytics, real-time application monitoring, website search, and more. OpenSearch is an open-source, distributed search and analytics suite derived from Elasticsearch. Amazon OpenSearch Service offers the latest versions of OpenSearch, support for 19 versions of Elasticsearch (1.5 to 7.10 versions), and visualization […]]]></description>
		
		
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		<item>
		<title>Accelerate self-service analytics with Amazon Redshift Query Editor V2</title>
		<link>https://noise.getoto.net/2021/11/08/accelerate-self-service-analytics-with-amazon-redshift-query-editor-v2/</link>
		
		<dc:creator><![CDATA[Bhanu Pittampally]]></dc:creator>
		<pubDate>Mon, 08 Nov 2021 17:28:52 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift Spectrum]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[database]]></category>
		<category><![CDATA[query editor]]></category>
		<category><![CDATA[redshift]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=94cf5a8edcd61ffc3821a9fb44cc3c73</guid>

					<description><![CDATA[Amazon Redshift is a fast, fully managed cloud data warehouse. Tens of thousands of customers use Amazon Redshift as their analytics platform. Users such as data analysts, database developers, and data scientists use SQL to analyze their data in Amazon Redshift data warehouses. Amazon Redshift provides a web-based query editor in addition to supporting connectivity […]]]></description>
		
		
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		<item>
		<title>Architecting Persona-centric Data Platform with On-premises Data Sources</title>
		<link>https://noise.getoto.net/2021/06/21/architecting-persona-centric-data-platform-with-on-premises-data-sources/</link>
		
		<dc:creator><![CDATA[Raghavarao Sodabathina]]></dc:creator>
		<pubDate>Mon, 21 Jun 2021 16:47:33 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Apache Nifi]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Lake house]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e15f09c7507dc368a9c37477b1aa9bf4</guid>

					<description><![CDATA[Many organizations are moving their data from silos and aggregating it in one location. Collecting this data in a data lake enables you to perform analytics and machine learning on that data. You can store your data in purpose-built data stores, like a data warehouse, to get quick results for complex queries on structured data. […]]]></description>
		
		
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