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	<title>stream-processing &#8211; Noise</title>
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		<title>Enabling near real-time data analytics on the data lake</title>
		<link>https://noise.getoto.net/2024/02/23/enabling-near-real-time-data-analytics-on-the-data-lake/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Fri, 23 Feb 2024 00:22:10 +0000</pubDate>
				<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[kafka]]></category>
		<category><![CDATA[Real-time]]></category>
		<category><![CDATA[stream-processing]]></category>
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					<description><![CDATA[Introduction

In the domain of data processing, data analysts run their ad hoc queries on the data lake. The lake serves as an interface between our analytics and production environment, preventing downstream queries from impacting upstream data ingest...]]></description>
		
		
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		<title>Streaming SQL in Data Mesh</title>
		<link>https://noise.getoto.net/2023/11/03/streaming-sql-in-data-mesh/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 03 Nov 2023 21:48:50 +0000</pubDate>
				<category><![CDATA[Big Data]]></category>
		<category><![CDATA[data-mesh]]></category>
		<category><![CDATA[event-streaming]]></category>
		<category><![CDATA[flink]]></category>
		<category><![CDATA[stream-processing]]></category>
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					<description><![CDATA[Democratizing Stream Processing @ NetflixBy Guil Pires, Mark Cho, Mingliang Liu, Sujay JainData powers much of what we do at Netflix. On the Data Platform team, we build the infrastructure used across the company to process data at scale.In our last bl...]]></description>
		
		
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		<title>Supporting large campaigns at scale</title>
		<link>https://noise.getoto.net/2022/04/01/supporting-large-campaigns-at-scale/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Fri, 01 Apr 2022 00:20:00 +0000</pubDate>
				<category><![CDATA[batch processing]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[kafka]]></category>
		<category><![CDATA[Scheduled job]]></category>
		<category><![CDATA[Scheduling]]></category>
		<category><![CDATA[stream-processing]]></category>
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					<description><![CDATA[Introduction

At Grab, we run large marketing campaigns every day. A typical campaign may require executing multiple actions for millions of users all at once. The actions may include sending rewards, awarding points, and sending messages. Here is what...]]></description>
		
		
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		<title>Abacus &#8211; Issuing points for multiple sources</title>
		<link>https://noise.getoto.net/2022/03/01/abacus-issuing-points-for-multiple-sources/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Tue, 01 Mar 2022 00:20:00 +0000</pubDate>
				<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Event Processing]]></category>
		<category><![CDATA[Optimisation]]></category>
		<category><![CDATA[stream-processing]]></category>
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					<description><![CDATA[Introduction
Earlier in 2021 we published an article on Trident, Grab’s in-house real-time if this, then that (IFTTT) engine which manages campaigns for the Grab Loyalty Programme. The Grab Loyalty Programme encourages consumers to make Grab transactio...]]></description>
		
		
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		<title>Auto-Diagnosis and Remediation in Netflix Data Platform</title>
		<link>https://noise.getoto.net/2022/01/14/auto-diagnosis-and-remediation-in-netflix-data-platform/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 14 Jan 2022 04:23:22 +0000</pubDate>
				<category><![CDATA[auto-remediation]]></category>
		<category><![CDATA[automation]]></category>
		<category><![CDATA[Big Data]]></category>
		<category><![CDATA[data-platforms]]></category>
		<category><![CDATA[stream-processing]]></category>
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					<description><![CDATA[By Vikram Srivastava and Marcelo MaywormNetflix has one of the most complex data platforms in the cloud on which our data scientists and engineers run batch and streaming workloads. As our subscribers grow worldwide and Netflix enters the world of gami...]]></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>
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					<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>Optimally scaling Kafka consumer applications</title>
		<link>https://noise.getoto.net/2020/10/13/optimally-scaling-kafka-consumer-applications/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Tue, 13 Oct 2020 02:13:54 +0000</pubDate>
				<category><![CDATA[Backend]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Event Sourcing]]></category>
		<category><![CDATA[Go]]></category>
		<category><![CDATA[Kubernetes]]></category>
		<category><![CDATA[Platform]]></category>
		<category><![CDATA[stream-processing]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/optimally-scaling-kafka-consumer-applications</guid>

					<description><![CDATA[Earlier this year, we took you on a journey on how we built and deployed our event sourcing and stream processing framework at Grab. We’re happy to share that we’re able to reliably maintain our uptime and continue to service close to 400 billion event...]]></description>
		
		
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