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	<title>Data Analytics &#8211; Noise</title>
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		<title>Streamlining RiskOps with the SOP agent framework</title>
		<link>https://noise.getoto.net/2025/05/08/streamlining-riskops-with-the-sop-agent-framework/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Thu, 08 May 2025 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
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					<description><![CDATA[Introduction

In the blog our previous introduction to the SOP-driven LLM Agent Framework, we the potential of LLM agent framework to revolutionise business operations was discussed. Now, we’re excited to explore a compelling use case: automating Accou...]]></description>
		
		
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		<title>Introducing the SOP-driven LLM agent frameworks</title>
		<link>https://noise.getoto.net/2025/04/25/introducing-the-sop-driven-llm-agent-frameworks/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Fri, 25 Apr 2025 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Experiment]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/introducing-the-sop-drive-llm-agent-framework</guid>

					<description><![CDATA[Introduction

We’re excited to introduce an innovative Large Language Model (LLM) agent framework that reimagines how enterprises can harness the power of AI to streamline operations and boost productivity. At its core, this framework leverages Standar...]]></description>
		
		
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		<title>Improving Hugo stability and addressing oncall challenges through automation</title>
		<link>https://noise.getoto.net/2025/03/20/improving-hugo-stability-and-addressing-oncall-challenges-through-automation/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Thu, 20 Mar 2025 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Data observability]]></category>
		<category><![CDATA[Data Pipeline]]></category>
		<category><![CDATA[Data reliability]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[Platform]]></category>
		<category><![CDATA[System Architecture]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/improving-hugo-stability</guid>

					<description><![CDATA[Introduction

Hugo plays a pivotal role in enabling data ingestion for Grab’s data lake, managing over 4,000 pipelines onboarded by users. The stability of Hugo pipelines is contingent upon the health of both the data sources and various Hugo component...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>Building a Spark observability product with StarRocks: Real-time and historical performance analysis</title>
		<link>https://noise.getoto.net/2025/03/06/building-a-spark-observability-product-with-starrocks-real-time-and-historical-performance-analysis/</link>
		
		<dc:creator><![CDATA[Grab Tech]]></dc:creator>
		<pubDate>Thu, 06 Mar 2025 00:00:10 +0000</pubDate>
				<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[data-engineering]]></category>
		<category><![CDATA[Engineering]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[Real-time Analytics]]></category>
		<category><![CDATA[Spark Observability]]></category>
		<category><![CDATA[StarRocks]]></category>
		<category><![CDATA[System Architecture]]></category>
		<guid isPermaLink="false">https://engineering.grab.com/building-a-spark-observability</guid>

					<description><![CDATA[Introduction

At Grab, we’ve been working to perfect our Spark observability tools. Our initial solution, Iris, was developed to provide a custom, in-depth observability tool for Spark jobs. As described in our previous blog post, Iris collects and ana...]]></description>
		
		
		<enclosure url="" length="0" type="" />

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		<title>Top Architecture Blog Posts of 2024</title>
		<link>https://noise.getoto.net/2025/01/23/top-architecture-blog-posts-of-2024/</link>
		
		<dc:creator><![CDATA[Andrea Courtright]]></dc:creator>
		<pubDate>Thu, 23 Jan 2025 16:59:36 +0000</pubDate>
				<category><![CDATA[Amazon CloudFront]]></category>
		<category><![CDATA[Amazon DynamoDB]]></category>
		<category><![CDATA[Amazon Elastic Container Service]]></category>
		<category><![CDATA[Amazon Elastic Kubernetes Service]]></category>
		<category><![CDATA[Amazon Machine Learning]]></category>
		<category><![CDATA[Amazon Q Developer]]></category>
		<category><![CDATA[Amazon RDS]]></category>
		<category><![CDATA[Amazon Route 53]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[Amazon Simple Storage Service (S3)]]></category>
		<category><![CDATA[announcements]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[architecture best practices]]></category>
		<category><![CDATA[Architecture Center]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS CloudFormation]]></category>
		<category><![CDATA[AWS FIS]]></category>
		<category><![CDATA[AWS Inferentia]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[AWS re:Invent]]></category>
		<category><![CDATA[AWS Trainium]]></category>
		<category><![CDATA[AWS Well-Architected]]></category>
		<category><![CDATA[AWS Well-Architected Framework]]></category>
		<category><![CDATA[AWS Well-Architected Lenses]]></category>
		<category><![CDATA[AWS Well-Architected Tool]]></category>
		<category><![CDATA[Chaos Engineering]]></category>
		<category><![CDATA[Cloud Cost Optimization]]></category>
		<category><![CDATA[cost optimization]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Disaster Recovery]]></category>
		<category><![CDATA[Elastic Load Balancing]]></category>
		<category><![CDATA[Failover]]></category>
		<category><![CDATA[Featured]]></category>
		<category><![CDATA[Let's Architect]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Management & Governance]]></category>
		<category><![CDATA[multi-region]]></category>
		<category><![CDATA[Regions]]></category>
		<category><![CDATA[reInvent]]></category>
		<category><![CDATA[resilience]]></category>
		<category><![CDATA[Resilience Hub]]></category>
		<category><![CDATA[security]]></category>
		<category><![CDATA[serverless]]></category>
		<category><![CDATA[Software as a Service]]></category>
		<category><![CDATA[Sustainability]]></category>
		<category><![CDATA[Thought Leadership]]></category>
		<category><![CDATA[Top 10]]></category>
		<category><![CDATA[Top Posts*]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=f95ffaba56b2d093dde67cf564774fae</guid>

					<description><![CDATA[Well, it’s been another historic year! We’ve watched in awe as the use of real-world generative AI has changed the tech landscape, and while we at the Architecture Blog happily participated, we also made every effort to stay true to our channel’s original scope, and your readership this last year has proven that decision was […]]]></description>
		
		
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		<item>
		<title>Let’s Architect! Learn About Machine Learning on AWS</title>
		<link>https://noise.getoto.net/2024/05/30/lets-architect-learn-about-machine-learning-on-aws/</link>
		
		<dc:creator><![CDATA[Luca Mezzalira]]></dc:creator>
		<pubDate>Thu, 30 May 2024 15:01:20 +0000</pubDate>
				<category><![CDATA[Amazon Machine Learning]]></category>
		<category><![CDATA[Amazon Sagemaker]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[AWS Inferentia]]></category>
		<category><![CDATA[AWS Trainium]]></category>
		<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[Let's Architect]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Software as a Service]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=50730c4a0684d0c1070888b29b2f07c9</guid>

					<description><![CDATA[A data-driven approach empowers businesses to make informed decisions based on accurate predictions and forecasts, leading to improved operational efficiency and resource optimization. Machine learning (ML) systems have the remarkable ability to continuously learn and adapt, improving their performance over time as they are exposed to more data. This self-learning capability ensures that organizations can […]]]></description>
		
		
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			</item>
		<item>
		<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>
		<guid isPermaLink="false">https://engineering.grab.com/enabling-near-realtime-data-analytics</guid>

					<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>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<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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		<item>
		<title>Architecting a Data Lake for Higher Education Student Analytics</title>
		<link>https://noise.getoto.net/2020/10/22/architecting-a-data-lake-for-higher-education-student-analytics/</link>
		
		<dc:creator><![CDATA[Craig Jordan]]></dc:creator>
		<pubDate>Thu, 22 Oct 2020 16:14:50 +0000</pubDate>
				<category><![CDATA[Amazon Athena]]></category>
		<category><![CDATA[Amazon Redshift]]></category>
		<category><![CDATA[Amazon Simple Storage Services (S3)]]></category>
		<category><![CDATA[Architecture]]></category>
		<category><![CDATA[AWS Database Migration Service]]></category>
		<category><![CDATA[AWS Glue]]></category>
		<category><![CDATA[AWS Key Management Service*]]></category>
		<category><![CDATA[AWS Lambda]]></category>
		<category><![CDATA[Customer Solutions]]></category>
		<category><![CDATA[Data Analytics]]></category>
		<category><![CDATA[data lakes]]></category>
		<category><![CDATA[education]]></category>
		<category><![CDATA[Migration]]></category>
		<guid isPermaLink="false">http://noise.getoto.net/?guid=88e4d0198c96c8b00bbe0257cab0b17b</guid>

					<description><![CDATA[One of the keys to identifying timely and impactful actions is having enough raw material to work with. However, this up-to-date information typically lives in the databases that sit behind several different applications. One of the first steps to finding data-driven insights is gathering that information into a single store that an analyst can use [&#8230;]]]></description>
		
		
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