Pallav Rustogi

Open for Noida Location too

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Why? While discussing system design for both trading apps and stock exchanges, I strongly emphasize that they are weak cases for Cloud. While Cloud is really good for scaling and availability, it comes with a tradeoff for Latency. While temporary 300 ms+ latency is OK for most of the apps, a trader can lose a lot of money in that time frame. I am still of the belief that a broker should run its app as close to the stock exchange as possible and latency should trade off for availability . If a person is not able to place order at the. first step, it's still better than seeing order in "Pending" state and then getting executed while stock price has gone down already. https://lnkd.in/d6N9Rp6G

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Our trajectory with Google Cloud epitomizes a transformative strategic pivot. What began as a challenging market positioning has evolved into a compelling narrative of operational resilience and technological leadership. Over seven pivotal years, we've fundamentally reimagined our enterprise value proposition—transitioning from a peripheral cloud solution to a mission-critical AI innovation partner. The financial metrics tell a powerful story: we've systematically converted recurring quarterly deficits into a robust framework for sustainable profitability. Buckle up the next 7 years are going to be epic @ Google Cloud! 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 #google #googlecloud #GCP #gcpcloud

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Oracle executives discuss the evolution in AI code that is helping boost developer velocity and enhance code consistency. In conversation with siliconindia, they highlight Oracle’s role in harnessing AI to drive innovation and enhance product strategy and development processes. https://lnkd.in/gA3yShbW

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Very interesting post on Intuit's use of LLMs to manage K8s, via Aditya Kulkarni at InfoQ: "Intuit Engineering's Approach to Simplifying Kubernetes Management with AI" https://lnkd.in/eRN6_sAK

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In an interview, P Saravanan explains how Oracle's HeatWave GenAI minimizes GPU dependency by integrating AI capabilities directly within the MySQL database, enabling faster and more efficient AI processing while cutting costs. https://lnkd.in/gCfFnhSq

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🚀 Building DAT Systems: A Journey Beyond AWS ? 🚀 When I embarked on setting up DAT AI Systems Pvt Ltd cloud environment, many assumed I’d turn to Amazon Web Services (AWS) , given my history with some fantastic folks there. But, when bootstrapping a startup, managing #AWS as a one-person team can be overwhelming—being the #Architect, #Developer, #SRE, and handling #DevSecOps all at once under the shared responsibility model is a tall order. Instead, I chose to go fully #serverless. I’m still cautious about using #Lambda without fully grasping its implications 😆, so I opted for a different route. Here’s a peek into our stack: - #Compute: Running globally distributed apps in 5 regions on Fly.io . - #Database: Upstash for #Redis and #Kafka with zero cluster management; Neon for #Postgres, offering bottomless storage with branching ,read replicas and #autoscaling with compute suspend. - #Storage & #CDN: Backblaze for #S3 storage paired with bunny.net #CDN for global content delivery, with no egress from #B2. - #Frontend: Built on Retool for low-code development with instant 1-click updates. - CI/CD: CircleCI integrated with GitHub for seamless #CI/#CD. - #Observability: AppSignal for #APM, Sentry for error tracking, and Grafana Labs for #monitoring and #logging. All of this wouldn’t have been possible if I had to wrestle with the stack we built our services on— #Elixir made it happen. Shoutout to the developer tools that made the journey smoother: #Helix, #nerdctl, #lazygit, #direnv, #git-secrets, and #hopscotch, to name a few. So, did I skip AWS? Not exactly. Most of these amazing services run on AWS or leverage its infrastructure. They just gave me the power of AWS while handling the responsibility. Plus, their #startup-friendly plans allow us to dream big. Here’s our current architecture—4 months in and we’ve scaled from 100 messages/month to 150,000 messages/month. Our 6-month goal? 10 million messages/month or 100,000 users, whichever comes first 😉. Reach out to Tasawar Ali to know more. #Serverless #StartupLife #CloudComputing #DevOps #Elixir #Architecture #TechJourney #AWS

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We’re taking fast on tour. Come see us in person at Redis Released, our exclusive customer events in Bengaluru on August 23 and Singapore on August 29 (and coming to New York and London this fall). Connect with the Redis community and see how some of the most innovative companies in the world are using Redis to build the future, fast. Here’s what we’ll be covering: 💡 Product announcements you won’t want to miss 🛠 Hands-on workshops about building GenAI apps with Redis 🤔 What’s next for the world’s fastest data platform Register here: https://redis.io/released/

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Join us to learn how you can maximize your hybrid cloud capabilities with Oracle Cloud VMware Solution and various migration strategies. Watch the on-demand webinar. https://lnkd.in/gYBQYuib

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🔍 Oracle GenAI vs. Azure AI/ML: A Comparative View of AI/ML Capabilities As AI becomes integral to digital transformation, understanding the key differences between platforms can drive smarter choices. Today, I'm diving into a side-by-side look at Oracle GenAI and Azure AI/ML—two powerhouses with unique strengths in the AI/ML space. Key Factors to Consider: *. Deployment Flexibility: Oracle offers robust on-prem options alongside cloud capabilities, while Azure shines with flexible cloud-native deployment. *. Performance & Scalability: Both platforms excel here, but nuances in integration and scalability might impact performance for large-scale deployments. *. Cost Efficiency: Azure’s integration with Microsoft's ecosystem may present unique value, while Oracle GenAI's optimized database solutions can lead to reduced overhead in specific use cases. Choosing the right AI/ML platform ultimately depends on your needs—whether it's deployment freedom, cost, or peak performance. 💡 Which platform aligns with your AI vision? Let’s discuss!

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𝐃𝐞𝐩𝐥𝐨𝐲𝐞𝐝 𝐚 𝐬𝐭𝐨𝐜𝐤 𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐨𝐧 𝐀𝐖𝐒 𝐄𝐂2! Thrilled to share my first success of deploying a Streamlit app on AWS EC2 instance. I’ve always been a firm believer in the philosophy of continuous learning. So I decided to get my hands dirty and build a intrinsic stock valuation app and deploy it. This has been a fun learning experience for me. From the article (https://lnkd.in/gzpjvTn4) ,you will learn: * How to setup an AWS EC2 instance * How to deploy your Streamlit App You can check out the live app using this link below EDITED - https://lnkd.in/g3huUHdv Disclaimer — stock valuations calculated by this app are based on a simplified discounted cash flow (DCF) model and should not be considered as accurate estimates or financial advice. Always consult with a qualified financial advisor before making any investment decisions :) Hope this is helpful! I'd love to hear your thoughts and experiences. Have you deployed a Streamlit app to AWS before? What challenges or tips do you have to share? #AWS, #MLOps, #Streamlit, #AWSEC2, #Deployment

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AlloyDB provides 2x better price-performance than self-managed PostgreSQL. See how in this article from Sridhar Ranganathan and Shesh Ranganath.

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