Vikram Koka

At the core of effective engineering leadership is the ability to stay connected with your team. This is perhaps the most challenging aspect because retaining the respect of your people is difficult unless you remain technically involved yourself. In my journey as an engineering leader, staying true to the individual contributor role that originally earned their respect has kept me close. This approach extends beyond technical expertise; it's about maintaining a deep connection with the core activities that propel product development forward at Gearflow. One of my biggest professional fears is becoming irrelevant or disconnected from my team and our work. This fear motivates me to continually reinvent and retool in response to market demands. Next month, I'll be sharing about my adventures generating synthetic data recently. And while I'm no expert when I start preparing for these talks, the process is incredibly valuable. It's less about delivering a perfect talk and more about how the preparation expands my thinking and exposes me to new capabilities. This helps me connect the dots to opportunities at Gearflow that bring real innovation to our customers. Speaking regularly holds me accountable and pushes me to learn ahead of the curve, ensuring I remain relevant and impactful day-to-day. Every talk is a journey of discovery, helping me to lead with humility, communicate clearly, and inspire a culture of innovation through a deep, technical understanding of our work.

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GraphQL makes your APIs better. The days of #GraphQL vs #REST are over, now it is all about how both technologies can work together to deliver value to your end users and customers as part of cohesive #API strategy. They can exist side by side and enable each other. I’m genuinely excited about what Apollo GraphQL and our product roadmap will be delivering to show the world just what is possible! Check out this great interview with Matt DeBergalis by Intuit Open Source and hear his thoughts on how the the two technologies can build on each other 👇🏻 #EnterpriseArchitecture #PlatformEngineering

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In building robust, scalable distributed systems, it’s tempting to lean on events as a primary abstraction. But are they really the best approach? In this blog post I explore why events often fall short for complex systems and how a reimagined abstraction can simplify development and reduce failures: 👉 Covered topics * The pitfalls of an event-driven approach. * How durable execution offers a better path for distributed architectures. * Why this rethink could simplify and improve your distributed system. If you’re working with distributed systems, this article might just offer the perspective shift you need. #DistributedSystems #SoftwareArchitecture #EDA #Temporal #Rethink

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Modern distributed systems still employ micro-services as a main design interaction. At Synadia we frequently talk about changing the "how", not the "what", by utilizing NATS.io. This video is a great explanation on micro-services with NATS.io. https://t.co/gXQuPC1s3S

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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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Developers love Redis because it’s fast. Liftoff uses Redis Cloud as a high-availability, low-latency database, increasing their ROI by improving ad relevance and quality. Hear more about the benefits of their improved ad relevance and quality straight from the director of engineering at Liftoff Mobile. Learn more about Liftoff's soaring ad exchange with Redis Cloud's low latency: https://lnkd.in/gAWBScEk

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Great SRE Interview Questions 

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Transform your IAC validation process with VR's OPA Policy Generator. This intuitive tool simplifies the creation of secure, compliant policies for Terraform AWS-based infrastructure, cutting through the complexity of OPA's syntax and making it accessible for all developers and security experts. 🔒✨ #VRModuleSpotlight #OPA #PolicyGenerator #IAC #AWS #AWSPartner #APNProud Amazon Web Services (AWS) AWS Partners

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Recently Haytham Abuelfutuh presented about Batch Workloads and how Union and Flyte are used for Large scale batch and near realtime workloads. Check out this post to 1. Learn about the challenges in using Kubernetes for running Batch workloads 2. The problems in the programming model with Batch workloads. Often times its not a single job - but it may start with one 3. The simplification that can be introduced by using Union. Union runs them across multiple clouds, multiple kubernetes clusters, securely accessing large amounts of data, while maintaining efficiency through - caching, smart techniques in which container state is saved, how pods are used, how back pressure is maintained etc. Check out the blog.

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If you are working with Docker... I highly recommend looking at the Slim toolkit. https://lnkd.in/gX_Gb9Kf Why? Slim allows developers to inspect, optimize, and debug their containers using its xray, lint, build, debug, run, images, merge, registry, vulnerability (and other) commands. It simplifies and improves your developer experience building, customizing and using containers. It makes your containers better, smaller and more secure while providing advanced visibility and improved usability working with the original and minified containers. Don't change anything in your container image; minify it by up to 30x to make it secure, too! Optimizing images isn't the only thing it can do, though. It can also help you understand and author better container images. Keep doing what you are doing. No need to change anything. Use the base image you want. Use the package manager you want. Don't worry about hand-optimizing your Dockerfile. You shouldn't have to throw away your tools and your workflow to have small container images. Node.js application images: from ubuntu:14.04 - 432MB => 14MB (minified by 30.85X) from debian:jessie - 406MB => 25.1MB (minified by 16.21X) from node:alpine - 66.7MB => 34.7MB (minified by 1.92X) from node:distroless - 72.7MB => 39.7MB (minified by 1.83X) Go application images: from golang:latest - 700MB => 1.56MB (minified by 448.76X) from ubuntu:14.04 - 531MB => 1.87MB (minified by 284.10X) from golang:alpine - 258MB => 1.56MB (minified by 165.61X) from centos:7 - 615MB => 1.87MB (minified by 329.14X) Rust application images: from rust:1.31 - 2GB => 14MB (minified by 147.16X) Java application images: from ubuntu:14.04 - 743.6 MB => 100.3 MB

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Substack recently moved their ML and AI pipelines over to Modal, from AWS SageMaker. We've had a lot of fun working with them and we just wrote about it on our blog. “Using SageMaker just felt like a very convoluted process. It felt very removed from a normal engineering workflow. With Modal, it’s a lot faster. You don’t feel like you have to wait for notebooks to spin up or remember to turn them off or any of that type of stuff.” – quote from Mike Cohen, Head of AI & ML Engineering at Substack In the process, we actually made several improvements to Modal's system latency, since Substack's use case is very latency-sensitive. These optimizations now benefit all Modal users. Take a look: https://lnkd.in/e8yMCiti

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🌐 Excited to introduce my conversation with Ash Vijayakanthan SVP of Cloud Sales and BD Partnerships at Redis, live from Google Cloud’s Marketplace Exchange! Redis, a favorite among developers, plays a crucial role in the data ecosystem, powering applications with high-performance caching and database solutions integral to GenAI. In this episode, Ash shares how Redis is leveraging Google Cloud Marketplace to meet customer demand for faster procurement and scaling, especially in light of large cloud commitments. Redis’s marketplace strategy not only retains customers but also expands deal sizes and accelerates closing times. Ash discusses Redis’s comprehensive partner program, enabling ISVs, OEMs, and SIs to build on the Redis platform, offering them both growth opportunities and collaborative engagement with Redis’s ecosystem. Ash’s advice to partners? Invest in the marketplace and empower your sales teams to maximize its potential—a strategy that’s driving Redis’s top-down engagements and delivering significant business value. Tune into The Ultimate Guide to Partnering to learn how Redis is redefining co-selling and scaling with Google Cloud! Watch here:https://lnkd.in/e4tFrfuZ

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Is your observability bill out of control? Are you wasting engineering time trying to justify the cost? There's a better solution… https://hubs.la/Q02PsJZM0 #kubernetes #devops #cloud #observability

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Truth. This blog articulates the shortcomings of most tiered storage solutions in delivering predictable performance at scale. Without some rebuild that harnesses cloud primitives like AWS S3 and GCP GCS, mere incremental adjustments to existing products won't adequately serve users. From a product manager's perspective, sometimes incremental adjustments isn't a path to sustainable product adoption and success.

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Anti-Complex Kubernetes: Open Source and Cloud Native – October 31 🚀 Platform builders and engineering leaders, this one’s for you! In this deep-dive session, we'll explore strategies to reversing the complexity in Kubernetes. Joining us this episode is Phill Gibson, Sr. Product Manager at Azure. He’ll share insights on what’s working—and what’s not—in the battle against cloud native complexity. 📅 October 31 – mark your calendar! 🔗 https://hubs.ly/Q02VmzPd0 #CloudNative #Kubernetes #DevOps #OpenSource #PlatformEngineering #TechLeadership

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We are excited to announce the launch of Ursa engine, our Kafka API-compatible data streaming engine that is built on top of Lakehouse. Ursa is not something entirely new; it is built on our many years of experience building and operating #ApachePulsar and #ApacheKafka. To learn more about the #Ursa engine, I recommend our #PulsarSummit keynote presentation and announcement blog post. Here, I’d like to delve deeper into some of its features: 1. Making Kafka API a first-class citizen: In #ApachePulsar, we developed a unified messaging model combining queuing and streaming semantics into one protocol, aimed at enhancing data sharing between business applications. Although powerful and increasingly adopted by enterprises and unicorns, the requirement for application rewrites is impractical in today’s economy. Instead of building data gravity around a protocol, we open up access to the robust stream storage layer by extending support to the Kafka protocol, transitioning from a single protocol to a multi-protocol platform, facilitating greater flexibility and integration. 2. Achieve Stream-Table duality via Lakehouse Storage: Data streams commonly end up in data lakehouses, where integrating and maintaining them can be complex and resource-intensive. Ursa simplifies this by storing data streams as lakehouse tables in object storage. This integration not only streamlines operations but also allows seamless access to operational data for analytical processes. Thanks to its multi-modal storage engine which fosters data sharing across teams and departments. 3. Object storage as the primary storage solution: To better align with cloud economics, particularly for large workloads, Ursa makes ZooKeeper and BookKeeper optional and utilizes a cost-optimized write-ahead log based solely on object storage with leaderless and stateless brokers. This architecture leverages object storage for both data replication and storage, minimizing inter-AZ data transfers and reducing costs for latency-relaxed, high-volume workloads. By integrating these features at the multi-tenant level rather than at isolated cluster levels, Ursa eliminates costly data duplication and movement across clusters, allowing teams to map directly to tenants and be able to share data across teams and departments more efficiently. We believe that the future of data streaming platforms lies in being open and multi-faceted, fundamentally different from traditional data platforms. They are designed not just to store and process data, but to enhance organizational capabilities by fostering data sharing across the organization and integration with other systems. More and more data streaming platforms will look more like Ursa, with multi-protocol, multi-tenant, and multi-modal capabilities. Check out our announcement blog post about Ursa: https://lnkd.in/ghEKTYE6 #ApacheKafka #Lakehouse #DataStreaming

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Human in the loop evals 🧑‍💻👀 → custom, automated evaluation models 🤖📈? https://lnkd.in/gruPtC8d Human in the loop (HITL) is a really popular technique for teams just getting started with LLMs and their LLMOps functions. Given your biggest concern is your model acting ‘not like a human’, it’s easy to see how sticking a human in the loop, to review and edit any potential mistakes or weirdness, can solve that problem. But it’s equally easy to see how challenging this can be to scale, and how it can eat at a lot of the cost-savings promised by automations. So how should teams approach balancing the security and certainty from human-in-the-loop, with an eye towards scaling? Over the weekend I got the chance to experiment with a few approaches to turning HITL datasets into their own custom evaluation models. I also think about how I would balance my human-agent evaluations alongside my model-driven evaluations, to set me up for long-term success. https://lnkd.in/gruPtC8d

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𝐍𝐨𝐫𝐭𝐡𝐟𝐥𝐚𝐧𝐤 𝐬𝐞𝐜𝐮𝐫𝐞𝐝 $22.3𝐌 𝐭𝐨 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐞 𝐜𝐥𝐨𝐮𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 With a Kubernetes-based self-service platform, it empowers developers to build, deploy, and scale workloads without the complexity of brittle tools or rigid third-party platforms. Trusted by companies like Sentry and Writer, Northflank streamlines DevOps, lowers barriers for engineers, and raises productivity. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐚𝐧𝐝 𝐃𝐞𝐯𝐎𝐩𝐬 𝐩𝐫𝐨𝐬: 𝐖𝐡𝐚𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐠𝐨-𝐭𝐨 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐲𝐢𝐧𝐠 𝐜𝐥𝐨𝐮𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐜𝐨𝐦𝐩𝐚𝐫𝐞 𝐭𝐨 𝐍𝐨𝐫𝐭𝐡𝐟𝐥𝐚𝐧𝐤? Andrej Vasilj, Martin Swanson, Paolo Fantini PhD, Krešimir Merdžo, Nicola-Jane Brooks, Modo Money, Igor Krstelj, Kevin Race, David Carmalt, Abdirizak Hersi,. #CloudDeployment #KubernetesInnovation #DeveloperTools. ------ 𝐋𝐞𝐚𝐫𝐧 𝐦𝐨𝐫𝐞 𝐡𝐨𝐰 𝐰𝐞 𝐃𝐨 𝐢𝐭 👉 https://lnkd.in/ed_mKpkF

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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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