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	<title>Data Lake &#8211; Noise</title>
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	<link>https://noise.getoto.net</link>
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		<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>Unleash deeper insights with Amazon Redshift data sharing for data lake tables</title>
		<link>https://noise.getoto.net/2024/10/10/unleash-deeper-insights-with-amazon-redshift-data-sharing-for-data-lake-tables/</link>
		
		<dc:creator><![CDATA[Mohammed Alkateb]]></dc:creator>
		<pubDate>Thu, 10 Oct 2024 16:07:47 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Data Sharing]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=e838649ca2064400f69ee40dd6c14b56</guid>

					<description><![CDATA[Amazon Redshift now enables the secure sharing of data lake tables—also known as external tables or Amazon Redshift Spectrum tables—that are managed in the AWS Glue Data Catalog, as well as Redshift views referencing those data lake tables. By using granular access controls, data sharing in Amazon Redshift helps data owners maintain tight governance over who can access the shared information. In this post, we explore powerful use cases that demonstrate how you can enhance cross-team and cross-organizational collaboration, reduce overhead, and unlock new insights by using this innovative data sharing functionality.]]></description>
		
		
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		<item>
		<title>Accelerate Amazon Redshift Data Lake queries with AWS Glue Data Catalog Column Statistics</title>
		<link>https://noise.getoto.net/2024/10/01/accelerate-amazon-redshift-data-lake-queries-with-aws-glue-data-catalog-column-statistics/</link>
		
		<dc:creator><![CDATA[Kalaiselvi Kamaraj]]></dc:creator>
		<pubDate>Tue, 01 Oct 2024 20:45:00 +0000</pubDate>
				<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Redshift Spectrum]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Optimization]]></category>
		<category><![CDATA[Performance]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a7f4e30a943fc72dc5cef822c701ba74</guid>

					<description><![CDATA[Over the last year, Amazon Redshift added several performance optimizations for data lake queries across multiple areas of query engine such as rewrite, planning, scan execution and consuming AWS Glue Data Catalog column statistics. In this post, we highlight the performance improvements we observed using industry standard TPC-DS benchmarks. Overall execution time of TPC-DS 3 TB benchmark improved by 3x. Some of the queries in our benchmark experienced up to 12x speed up.]]></description>
		
		
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		<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>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=8122db3da954fed8ed0764b262d6bba7</guid>

					<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>
		<title>Build a semantic search engine for tabular columns with Transformers and Amazon OpenSearch Service</title>
		<link>https://noise.getoto.net/2023/03/01/build-a-semantic-search-engine-for-tabular-columns-with-transformers-and-amazon-opensearch-service/</link>
		
		<dc:creator><![CDATA[Kachi Odoemene]]></dc:creator>
		<pubDate>Wed, 01 Mar 2023 19:52:28 +0000</pubDate>
				<category><![CDATA[Amazon ML Solutions Lab]]></category>
		<category><![CDATA[Amazon OpenSearch Service]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[embedding]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<category><![CDATA[Tutorial]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=550a1aa6018966846bf756882899c31e</guid>

					<description><![CDATA[Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. The inability to accurately find and analyze data from disparate sources represents a potential efficiency killer for everyone from data scientists, medical researchers, academics, to financial and government analysts. Conventional […]]]></description>
		
		
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		<item>
		<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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		<item>
		<title>How BookMyShow saved 80% in costs by migrating to an AWS modern data architecture</title>
		<link>https://noise.getoto.net/2023/01/11/how-bookmyshow-saved-80-in-costs-by-migrating-to-an-aws-modern-data-architecture/</link>
		
		<dc:creator><![CDATA[Mahesh Vandi Chalil]]></dc:creator>
		<pubDate>Wed, 11 Jan 2023 19:07:19 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Intermediate (200)]]></category>
		<category><![CDATA[Media & Entertainment]]></category>
		<category><![CDATA[Migration]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a05edd4105f510fd439d6629a673c4ee</guid>

					<description><![CDATA[This is a guest post co-authored by Mahesh Vandi Chalil, Chief Technology Officer of BookMyShow. BookMyShow (BMS), a leading entertainment company in India, provides an online ticketing platform for movies, plays, concerts, and sporting events. Selling up to 200 million tickets on an annual run rate basis (pre-COVID) to customers in India, Sri Lanka, Singapore, […]]]></description>
		
		
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		<item>
		<title>Scale read and write workloads with Amazon Redshift</title>
		<link>https://noise.getoto.net/2022/12/13/scale-read-and-write-workloads-with-amazon-redshift/</link>
		
		<dc:creator><![CDATA[Harsha Tadiparthi]]></dc:creator>
		<pubDate>Tue, 13 Dec 2022 16:59:40 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Data Lake]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=081b18c68248f9b36bc8b77893dff2bf</guid>

					<description><![CDATA[Amazon Redshift is a fast, fully managed, petabyte-scale cloud data warehouse that enables you to analyze large datasets using standard SQL. The concurrency scaling feature in Amazon Redshift automatically adds and removes capacity by adding concurrency scaling to handle demands from thousands of concurrent users, thereby providing consistent SLAs for unpredictable and spiky workloads such […]]]></description>
		
		
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		<item>
		<title>How Epos Now modernized their data platform by building an end-to-end data lake with the AWS Data Lab</title>
		<link>https://noise.getoto.net/2022/08/01/how-epos-now-modernized-their-data-platform-by-building-an-end-to-end-data-lake-with-the-aws-data-lab/</link>
		
		<dc:creator><![CDATA[Debadatta Mohapatra]]></dc:creator>
		<pubDate>Mon, 01 Aug 2022 17:45:59 +0000</pubDate>
				<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[data platform]]></category>
		<category><![CDATA[Financial Services]]></category>
		<category><![CDATA[FinTech]]></category>
		<category><![CDATA[PostgreSQL compatible]]></category>
		<category><![CDATA[startup]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f977b079b561997539fd7c1da7126b8d</guid>

					<description><![CDATA[Epos Now provides point of sale and payment solutions to over 40,000 hospitality and retailers across 71 countries. Their mission is to help businesses of all sizes reach their full potential through the power of cloud technology, with solutions that are affordable, efficient, and accessible. Their solutions allow businesses to leverage actionable insights, manage their […]]]></description>
		
		
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		<item>
		<title>Scaling cross-account AWS KMS–encrypted Amazon S3 bucket access using ABAC</title>
		<link>https://noise.getoto.net/2022/02/23/scaling-cross-account-aws-kms-encrypted-amazon-s3-bucket-access-using-abac/</link>
		
		<dc:creator><![CDATA[Jorg Huser]]></dc:creator>
		<pubDate>Wed, 23 Feb 2022 20:19:34 +0000</pubDate>
				<category><![CDATA[ABAC]]></category>
		<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon S3]]></category>
		<category><![CDATA[Attribute-based access control]]></category>
		<category><![CDATA[authorization]]></category>
		<category><![CDATA[AWS Key Management Service (KMS)]]></category>
		<category><![CDATA[AWS Lake Formation]]></category>
		<category><![CDATA[Big Data Platform]]></category>
		<category><![CDATA[Big Data Security Management]]></category>
		<category><![CDATA[cross-account privilege design escalation]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Data Protection in Data Lakes]]></category>
		<category><![CDATA[Key management]]></category>
		<category><![CDATA[Key Management for Big Data]]></category>
		<category><![CDATA[PrincipalOrgId]]></category>
		<category><![CDATA[Resource-based policies]]></category>
		<category><![CDATA[Security Blog]]></category>
		<category><![CDATA[Security, Identity & Compliance]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=4be77477157ab5936b2faa4570cb47cb</guid>

					<description><![CDATA[This blog post shows you how to share encrypted Amazon Simple Storage Service (Amazon S3) buckets across accounts on a multi-tenant data lake. Our objective is to show scalability over a larger volume of accounts that can access the data lake, in a scenario where there is one central account to share from. Most use […]]]></description>
		
		
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		<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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		<title>How to Accelerate Building a Lake House Architecture with AWS Glue</title>
		<link>https://noise.getoto.net/2021/08/24/how-to-accelerate-building-a-lake-house-architecture-with-aws-glue/</link>
		
		<dc:creator><![CDATA[Raghavarao Sodabathina]]></dc:creator>
		<pubDate>Tue, 24 Aug 2021 17:06:44 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Glue DataBrew]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Data Warehouse]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=19d19a8c9ef718a65ea3673d52d7654d</guid>

					<description><![CDATA[Customers are building databases, data warehouses, and data lake solutions in isolation from each other, each having its own separate data ingestion, storage, management, and governance layers. Often these disjointed efforts to build separate data stores end up creating data silos, data integration complexities, excessive data movement, and data consistency issues. These issues are preventing […]]]></description>
		
		
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		<title>How MEDHOST’s cardiac risk prediction successfully leveraged AWS analytic services</title>
		<link>https://noise.getoto.net/2021/08/23/how-medhosts-cardiac-risk-prediction-successfully-leveraged-aws-analytic-services/</link>
		
		<dc:creator><![CDATA[Pandian Velayutham]]></dc:creator>
		<pubDate>Mon, 23 Aug 2021 17:14:59 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Kinesis]]></category>
		<category><![CDATA[Amazon Kinesis Data Firehose]]></category>
		<category><![CDATA[Amazon Kinesis Data Streams]]></category>
		<category><![CDATA[Amazon QuickSight]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[database]]></category>
		<category><![CDATA[Industries]]></category>
		<category><![CDATA[Kinesis Data Firehose]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<category><![CDATA[Uncategorized]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=cd81e1e048f755d4123ec81d2d358272</guid>

					<description><![CDATA[MEDHOST has been providing products and services to healthcare facilities of all types and sizes for over 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their patient care and operational excellence with its integrated clinical and financial EHR solutions. MEDHOST also offers a comprehensive Emergency Department Information System with […]]]></description>
		
		
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		<title>Deploy data lake ETL jobs using CDK Pipelines</title>
		<link>https://noise.getoto.net/2021/07/30/deploy-data-lake-etl-jobs-using-cdk-pipelines/</link>
		
		<dc:creator><![CDATA[Ravi Itha]]></dc:creator>
		<pubDate>Fri, 30 Jul 2021 05:35:48 +0000</pubDate>
				<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS CDK]]></category>
		<category><![CDATA[AWS Cloud Development Kit]]></category>
		<category><![CDATA[AWS CodeBuild]]></category>
		<category><![CDATA[AWS CodeDeploy]]></category>
		<category><![CDATA[AWS CodePipeline]]></category>
		<category><![CDATA[Best practices]]></category>
		<category><![CDATA[CI/CD]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[devops]]></category>
		<category><![CDATA[Technical How-to]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=a804acd036d895b2ec22e1a914325e29</guid>

					<description><![CDATA[Many organizations are building data lakes on AWS, which provides the most secure, scalable, comprehensive, and cost-effective portfolio of services. Like any application development project, a data lake must answer a fundamental question: “What is the DevOps strategy?” Defining a DevOps strategy for a data lake requires extensive planning and multiple teams. This typically requires […]]]></description>
		
		
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		<title>Amazon MSK backup for Archival, Replay, or Analytics</title>
		<link>https://noise.getoto.net/2021/02/19/amazon-msk-backup-for-archival-replay-or-analytics/</link>
		
		<dc:creator><![CDATA[Rohit Yadav]]></dc:creator>
		<pubDate>Fri, 19 Feb 2021 18:03:14 +0000</pubDate>
				<category><![CDATA[Amazon EMR]]></category>
		<category><![CDATA[Amazon Managed Streaming for Apache Kafka (Amazon MSK)]]></category>
		<category><![CDATA[Apache Kafka]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[data streaming]]></category>
		<category><![CDATA[Kinesis Data Firehose]]></category>
		<category><![CDATA[sql]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=915c5d5977398fc4cae4334f684c4f7d</guid>

					<description><![CDATA[Amazon MSK is a fully managed service that helps you build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes. You can also stream changes to […]]]></description>
		
		
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		<title>Use Macie to discover sensitive data as part of automated data pipelines</title>
		<link>https://noise.getoto.net/2020/12/09/use-macie-to-discover-sensitive-data-as-part-of-automated-data-pipelines/</link>
		
		<dc:creator><![CDATA[Brandon Wu]]></dc:creator>
		<pubDate>Wed, 09 Dec 2020 21:26:31 +0000</pubDate>
				<category><![CDATA[Advanced (300)]]></category>
		<category><![CDATA[Amazon Macie]]></category>
		<category><![CDATA[Amazon S3]]></category>
		<category><![CDATA[Amazon SES]]></category>
		<category><![CDATA[api gateway]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[cloud security]]></category>
		<category><![CDATA[cybersecurity]]></category>
		<category><![CDATA[data discovery]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[Data Pipeline]]></category>
		<category><![CDATA[Data protection]]></category>
		<category><![CDATA[data security]]></category>
		<category><![CDATA[DevSecOps]]></category>
		<category><![CDATA[PII]]></category>
		<category><![CDATA[Security Blog]]></category>
		<category><![CDATA[Security, Identity & Compliance]]></category>
		<category><![CDATA[serverless]]></category>
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		<guid isPermaLink="false">http://noise.getoto.net/?guid=e270a2b4fc0eba8dc45cf2753674c009</guid>

					<description><![CDATA[Data is a crucial part of every business and is used for strategic decision making at all levels of an organization. To extract value from their data more quickly, Amazon Web Services (AWS) customers are building automated data pipelines&#8212;from data ingestion to transformation and analytics. As part of this process, my customers often ask how [&#8230;]]]></description>
		
		
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			</item>
		<item>
		<title>How to delete user data in an AWS data lake</title>
		<link>https://noise.getoto.net/2020/09/18/how-to-delete-user-data-in-an-aws-data-lake/</link>
		
		<dc:creator><![CDATA[George Komninos]]></dc:creator>
		<pubDate>Fri, 18 Sep 2020 16:05:00 +0000</pubDate>
				<category><![CDATA[Amazon API Gateway]]></category>
		<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon EC2]]></category>
		<category><![CDATA[Amazon RDS]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon S3]]></category>
		<category><![CDATA[Amazon Simple Storage Services (S3)]]></category>
		<category><![CDATA[Analytics]]></category>
		<category><![CDATA[AWS Big Data]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS Step Functions]]></category>
		<category><![CDATA[Data Lake]]></category>
		<category><![CDATA[database]]></category>
		<category><![CDATA[DynamoDB]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=bdb4551ee14ee9309859211a0150f161</guid>

					<description><![CDATA[General Data Protection Regulation (GDPR) is an important aspect of today&#8217;s technology world, and processing data in compliance with GDPR is a necessity for those who implement solutions within the AWS public cloud. One article of GDPR is the &#8220;right to erasure&#8221; or &#8220;right to be forgotten&#8221; which may require you to implement a solution [&#8230;]]]></description>
		
		
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