Srinivas Rao Choudam

# Revolutionizing Database Performance: How AWS EBS io2 Block Express is Changing the Game In the ever-evolving landscape of cloud infrastructure, storage performance remains a critical bottleneck for many enterprise applications. I've been closely analyzing Amazon's latest innovation in this space, and the results are genuinely impressive. ## Why This Matters to Engineering Leaders Database performance issues cascade throughout your entire application stack. When your PostgreSQL server struggles with I/O operations, your users feel it - and your business metrics reflect it. The introduction of EBS io2 Block Express represents a significant leap forward in addressing these challenges. ## Technical Breakthrough Worth Understanding What makes io2 Block Express truly revolutionary is its implementation of Scalable Reliable Datagrams (SRD) protocol within the AWS Nitro System architecture. This isn't just incremental improvement - we're seeing: • Up to 256,000 IOPS per volume • Throughput reaching 4,000 MiB/s • Consistent sub-millisecond latency • Support for volumes up to 64 TiB The engineering behind maintaining this performance at scale while ensuring reliability demonstrates impressive innovation. ## Beyond the Marketing: Real Performance Implications Having worked with numerous database deployment architectures, what stands out in AWS's comparative analysis is the substantive difference between gp3 and io2 Block Express volumes for I/O-intensive operations. This isn't just about benchmarks - it translates directly to system responsiveness and application reliability. While PostgreSQL is highlighted, these benefits extend to Oracle, SQL Server, and MySQL workloads as well. For mission-critical database deployments, this technology warrants serious consideration in your infrastructure planning. ## What's Your Experience? Have you implemented io2 Block Express for your database workloads? I'd be interested in hearing your real-world performance experiences and how it's impacted your application architecture decisions. If you're managing database infrastructure that serves critical applications, now might be the time to evaluate whether your current storage tier is limiting your system's potential. #CloudEngineering #DatabasePerformance #AWS #PostgreSQL #EnterpriseInfrastructure

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Automation and Agentic AI are often used interchangeably, but they’re not the same. Automation follows predefined rules. You design the workflow, and the system executes it. Agentic AI works toward a goal. It can decide the steps, adapt, and handle exceptions. Simple way to think about it: Automation → Follows instructions Agentic AI → Works toward outcomes Both are powerful, but neither scales well without strong data foundations and governance. #DataGovernance #Automation #AgenticAI #ArtificialIntellige

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▶️ Concept Models (Pt 3): Concept Model enabling other artifacts - Example Due to interest from my previous post/presentation, this is the final part of a short series that will go into more detail on Concept Models and how they can fit in with a bigger picture. Pt 1 was defining what a Concept Model (CM) was. Pt 2 was Aligning Business Architecture & Data. 🎉 We have arrived!! Part 3 shows an example of a scoped Concept Model aligned to the same scope portion of the Info Concept Map, then aligned to two value streams which use these concepts as entrance and exit criteria for movement. (Graphic) - Use the Concept Model with business stakeholders for high level understanding of the scope of concepts and how they relate. - Validate the definitions, states and types of those concepts with the business stakeholders, which creates the Info Concept Map. - Map those concepts to your value streams to give them context and validate the states of the concepts/data you will need for tracking! - Use all 3 of these when you collaborate with data stakeholders to provide scope and context they can then use to do more detailed maps and models they need! 🌟 The beauty of using these artifacts when aligning business architecture to data is all of these concepts (the 'what') in context (the 'why') will give factual business meaning to the data needed for true business value! This is where the collaboration between these architecture domains begins! #businessarchitecture #businessanalysis

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The future of DevOps is here — and it’s intelligent. Microsoft unveiled Agentic DevOps at Build this week. — an evolution of the software development lifecycle powered by GitHub Copilot and Azure. One of the most exciting components? The new SRE Agent, designed to proactively monitor, triage, and even self-heal environments. We’re talking about AI agents that: * Auto-create pull requests from issues * Automate build + deploy pipelines * Manage observability and respond to incidents in real time This is more than automation — it’s cognitive collaboration between developers, AI, and the cloud. Catch the full announcement here: https://lnkd.in/gg_UfbVT #AgenticDevOps #GitHubCopilot #Azure #SRE #AI #DevOps #CloudEngineering #Microsoft #CopilotForDevOps

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Thinking… 🤔 Whether digitally transformed or still legacy, AI is quietly finding its way into every cell and vein of the IT ecosystem — rewiring how we build, run, and connect. Gartner calls it AI-First IT. Feels more like AI Infused Everything .. and “I” in IT is getting intelligent than ever … 🤖💡

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No #Data, No #AI - No progress 😟😟 - Werner Vogels “You still need to obsess about high data quality to differentiate with your AI business. If you think the LLMs trained on public internet data will do magic for your business, it won’t”. - Neel Mitra Check out the whole episode below to learn more. #aws #sonatus

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FABCON 2026 in Atlanta was nothing short of mesmerizing-where bold ideas, breakthrough tech, and real-world impact came together seamlessly. An electrifying blend of vision, innovation, and execution that set the tone for the future of data and AI. Now, it’s a wait-and-watch moment to see how much of it proves truly real, scalable, and competitively differentiated. ** AI Foundry: Fabric IQ is positioned as a capability that makes data AI-consumable for agents and AI apps via OneLake ** OneLake Transform transformations are applied on top of shortcut or mirrored data (zero‑copy, zero‑ETL), preserving the original source while exposing a transformed view in OneLake. New Connectors have been added (SharePoint in preview). ** Data and Catalog mirroring / zero copy options across open table formats from snowflake and Databricks into Fabric ** SAP Fabric and Salesforce Fabric version with zero copy and write back functionality ** Availability of CoPilot across various platforms within Fabric exclusively designed to support that platform only ** Fabric AI and Data Agents ** Zero ETL Data Mirroring onto Onelake from Azure data for PostgreSQL. Analytics on Operational workloads made simple. ** Building declarative AI Pipelines made simpler with dspy and pixeltable Thanks to Cognizant leadership for the opportunity. Rajesh SreepadaSatish Hegde FABCON & SQLCON - The Microsoft Fabric & SQL Community Conferences Microsoft Fabric

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Generative AI and Character Design: An overview of Zuraverse's Approach I had a fascinating conversation with my best buddy, Abhishek Sagar , the founder of Zuraverse.xyz, about how his team leverages generative AI for character design. It's a complex challenge, especially for a gaming platform with thousands of unique, NFT-based characters that need to maintain consistency while remaining true to their core identity. Abhishek shared some great insights, particularly their method of creating highly descriptive character prompts. Instead of generic descriptors, they use multi-layered strategy for character design consistency: • Prompt Engineering: They use a "recipe" of descriptive elements for each character. This goes beyond just physical appearance and includes: • Core Visuals: Specific details on body shape, color palette, unique markings (scars, tattoos), and accessories (a glowing sword, a specific type of armor). • Personality & Emotional Cues: Describing a "mischievous rogue with a sly grin" or a "somber sorceress with a furrowed brow" helps the AI generate consistent facial expressions and body language. • Style Cues: They include instructions to maintain a specific art style, whether it's "hyper-realistic," "anime-style," or "dark fantasy," to ensure all characters in a specific game universe look cohesive. • Reference Images: In addition to text, Zuraverse uses a core set of reference images for each character. This allows the AI to learn and replicate a character's "look and feel" with much greater accuracy, even when generating the character in new situations, poses, or wearing different outfits. • Agentic AI for Iteration: Abhishek also gave me a sneak peek into his journey with agentic AI. They are not just using a simple text-to-image generator. They're building AI agents that can take the initial character prompt and a set of rules, and then autonomously iterate on the design. This would allow the system to generate hundreds of variations—different outfits, poses, and expressions—while ensuring each one adheres to the character's core design principles. This isn't just theory—it's a practical application of AI by industry experts to tackle a real-world problem. Abhishek also promised that he will be sharing more details in the form of hands-on examples and podcasts in the coming weeks for the benefit of new explorers in his field. Feel free to checkout what zuraverse is Upto at: Zuraverse Story: https://lnkd.in/gz6Wuc4V Zuraverse: https://zuraverse.xyz Review: https://lnkd.in/gqkTSNxK #GenerativeAI #AIinGaming #Zuraverse #CharacterDesign #NFT #Gaming #AIagents #Innovation

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