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	<title>data streaming &#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 we seamlessly migrated high volume real-time streaming traffic from one service to another with zero data loss and duplication</title>
		<link>https://noise.getoto.net/2024/12/05/how-we-seamlessly-migrated-high-volume-real-time-streaming-traffic-from-one-service-to-another-with-zero-data-loss-and-duplication/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Thu, 05 Dec 2024 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[data streaming]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Optimisation]]></category>
		<category><![CDATA[Real-time streaming]]></category>
		<category><![CDATA[Service]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/seamless-migration</guid>

					<description><![CDATA[At Grab, we continuously enhance our systems to improve scalability, reliability and cost-efficiency. Recently, we undertook a project to split the read and write functionalities of one of our backend services into separate services. This was motivated...]]></description>
		
		
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		<title>Rethinking Stream Processing: Data Exploration</title>
		<link>https://noise.getoto.net/2024/01/31/rethinking-stream-processing-data-exploration/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Wed, 31 Jan 2024 00:10:10 +0000</pubDate>
				<category><![CDATA[data streaming]]></category>
		<category><![CDATA[Deployments]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[kafka]]></category>
		<category><![CDATA[Kubernetes]]></category>
		<category><![CDATA[Streaming applications]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/rethinking-streaming-processing-data-exploration</guid>

					<description><![CDATA[Introduction

In this digital age, companies collect multitudes of data that enable the tracking of business metrics and performance. Over the years, data analytics tools for data storage and processing have evolved from the days of Excel sheets and ma...]]></description>
		
		
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		<title>Kafka on Kubernetes: Reloaded for fault tolerance</title>
		<link>https://noise.getoto.net/2023/12/26/kafka-on-kubernetes-reloaded-for-fault-tolerance/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Tue, 26 Dec 2023 00:10:10 +0000</pubDate>
				<category><![CDATA[AWS]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[data streaming]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[kafka]]></category>
		<category><![CDATA[Kubernetes]]></category>
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					<description><![CDATA[Introduction

Coban - Grab’s real-time data streaming platform - has been operating Kafka on Kubernetes with Strimzi in 
production for about two years. In a previous article (Zero trust with Kafka), we explained how we leveraged Strimzi to enhance the...]]></description>
		
		
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		<item>
		<title>An elegant platform</title>
		<link>https://noise.getoto.net/2023/11/30/an-elegant-platform/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Thu, 30 Nov 2023 00:00:10 +0000</pubDate>
				<category><![CDATA[data]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[data streaming]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Platformisation]]></category>
		<category><![CDATA[Product]]></category>
		<category><![CDATA[Real-time streaming]]></category>
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					<description><![CDATA[Coban is Grab’s real-time data streaming platform team. As a platform team, we thrive on providing our internal users from all verticals with self-served data-streaming resources, such as Kafka topics, Flink and Change Data Capture (CDC) pipelines, var...]]></description>
		
		
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		<title>Data Movement in Netflix Studio via Data Mesh</title>
		<link>https://noise.getoto.net/2021/07/26/data-movement-in-netflix-studio-via-data-mesh/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 26 Jul 2021 18:00:56 +0000</pubDate>
				<category><![CDATA[Data Pipeline]]></category>
		<category><![CDATA[data streaming]]></category>
		<category><![CDATA[data-mesh]]></category>
		<category><![CDATA[data-movement]]></category>
		<category><![CDATA[stream-processing]]></category>
		<guid isPermaLink="false">https://medium.com/p/3fddcceb1059</guid>

					<description><![CDATA[By Andrew Nguonly, Armando Magalhães, Obi-Ike Nwoke, Shervin Afshar, Sreyashi Das, Tongliang Liu, Wei Liu, Yucheng ZengBackgroundOver the next few years, most content on Netflix will come from Netflix’s own Studio. From the moment a Netflix film or ser...]]></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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		<item>
		<title>Real-Time In-Stream Inference with AWS Kinesis, SageMaker &#038; Apache Flink</title>
		<link>https://noise.getoto.net/2020/11/27/real-time-in-stream-inference-with-aws-kinesis-sagemaker-apache-flink/</link>
		
		<dc:creator><![CDATA[Shawn Sachdev]]></dc:creator>
		<pubDate>Fri, 27 Nov 2020 17:50:10 +0000</pubDate>
				<category><![CDATA[Amazon API Gateway]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[apache flink]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[data streaming]]></category>
		<category><![CDATA[Kinesis Data Analytics]]></category>
		<category><![CDATA[Kinesis Data Streams]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=808b673d13a6eee099e25cf00fb1d237</guid>

					<description><![CDATA[As businesses race to digitally transform, the challenge is to cope with the amount of data, and the value of that data diminishes over time. The challenge is to analyze, learn, and infer from real-time data to predict future states, as well as to detect anomalies and get accurate results. In this blog post, we&#8217;ll [&#8230;]]]></description>
		
		
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