Vincent Lefevre

The final deep dive on change query support in the table formats is ready. This one covers Apache Paimon, focusing on understanding the change query support through the lens of its internals. https://lnkd.in/dFfM_8nA

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I just updated my first table format comparison blog post to fix some missing details of Apache Hudi. https://lnkd.in/dwSUS6qS

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This is a very exciting effort and I think it has the potential to be a real "game changer" #foundationalmodels #AI #ML #pioneeringtechnology #metaverse

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VPC with zone subnets, alb, nat and igw https://lnkd.in/gZxkgdBx

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During this hands-on workshop, our experts will guide you through best practices for optimizing your Kubernetes clusters, leveraging the power of PerfectScale. You'll learn how to: - Identify and safely remove wasted resources - Pinpoint and remediate issues causing latency and downtime - Evaluate the efficiency of your autoscalers - Select the most cost-effective node types based on real-time data https://hubs.la/Q02WgM2c0 #k8s #devops #kubernetes #aws #sre

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Introducing Structured Outputs: By popular demand, we are introducing the ability to provide your own JSON schema to customize the output structure of the OpenAI API response https://lnkd.in/g7g3xmvb

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Sometimes true power can be unlocked from existing technologies if we intentionally choose to use them "the wrong way". The next post describes such a case with AWS SQS and StepFunction. On a different note, I think I should write more. #aws #serverless #sqs #stepfunction #lambda #cloud #architecture

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Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy

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Did you know Vaadin supports GraalVM native image builds out of the box? Compile your app with mvn -Pnative -Pproduction native:compile Then run the native binary in the target dir and ⚡️🤯 https://lnkd.in/gXr-VfXb

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Hey everyone, I am an investor and advisor to Katanemo.... They are reinventing the infrastructure primitives for GenAI and launched Arch - https://lnkd.in/gCrbbkEV - an intelligent prompt gateway. Give the project a look on Github, and star the project if it resonates

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Object storage direct-write Kafka topics are coming to Confluent. In data system design, cost is a kind of unstoppable force that rewrites architectures every decade or so, and that is happening right now for high-volume streams. https://lnkd.in/dHRFnrMB

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There has been a lot of talks about #timeseries models and #GenAI, if you are in to that domain, I encourage you to check this research work, it is very interesting take on time series https://lnkd.in/gfMHHjrs

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When you need to play with your data in Gen Ai, you need RAG and Chunking is one of the main component in the RAG framework while consuming or processing your data. Excellent post below describing how in different ways chunking in RAG can be implemented..

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Adam's post is good (and I like this categorization in general), but there are a couple of things that I don't agree with. First, I'd say time-to-recovery is a strength of CfD rather than CfM. Physalia (from our paper "Millions of Tiny Databases" https://lnkd.in/gepZPA2Y) is the CfD system that provides the metadata for EBS (https://lnkd.in/g7R3MgYk) a vastly bigger CfM system. We chose CfD for Physalia specifically to optimize recovery time, because it needs to recover before the larger system can recover. Second, I think the post is making an assumption that CfM systems are f+1 on the dataplane, use the minimal number of replicas, and don't use quorums. Aurora is an instructive counter example (https://lnkd.in/gNrNTPp7).

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A new Jepsen report from Kyle Kingsbury looking at the Kafka-compatible Buf: https://lnkd.in/gijxaHzr It includes broader findings about the Kafka protocol and implementation too: "We also characterize four issues related to Kafka more generally, including the lack of authoritative documentation for transaction semantics, a deadlock in the official Java client, and write loss, aborted read, and torn transactions caused by the lack of message ordering constraints in the Kafka transaction protocol. These issues affect Kafka, Bufstream, and (presumably) other Kafka-compatible systems, and remain unresolved."

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Query, slice, and dice on attributes anywhere within the full trace. Once again, Honeycomb showing off the incredible power of our storage and query system. https://lnkd.in/eqZXU8kC

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Exactly one month ago, Intuit's Kalyan Kolachala (Director, Product Development), gave a Lightning Talk: Federated Search Over Distributed Observability Data at #ArgoCon | #KubeCon EU. In this talk, he gives insight into the use of technologies like, #OpenTelemetry, #ApacheArrow and #ApachePresto and how it can be used to improve speed, performance and significantly bring down Observability costs (for compute, networking and storage) and illustrates the same using a case study from Intuit. https://lnkd.in/gnv3vESJ #Intuit #IntuitOpenSource #OpenSourceCommunity #Observability #OpenSource #CloudNativeCon

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