Shriya Gupta

Demo: how to surface and use Unity Catalog Metric Views in Databricks AI/BI — to enable self-serve access to trusted business metrics. See how to set up central governance with row-level security and column masking, and more.

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Struggling with slow queries on massive datasets? Traditional data lakes often fall short on performance, making real-time analytics a challenge. The Databricks Lakehouse combines the best of data lakes and warehouses to power faster, smarter decision-making.  ⏬ Download the whitepaper to learn how to fast-track your modernization to Databricks Lakehouse with LeapLogic: https://lnkd.in/ddBSRhAB #databricks #dataestate #modernization #cloudmigration #datamodernization #whitepaper

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Struggling with slow queries on massive datasets? Traditional data lakes often fall short on performance, making real-time analytics a challenge. The Databricks Lakehouse combines the best of data lakes and warehouses to power faster, smarter decision-making.  ⏬ Download the whitepaper to learn how to fast-track your modernization to Databricks Lakehouse with LeapLogic: https://lnkd.in/ddBSRhAB #whitepaper #databricks #datalake #datamodernization #workloads

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Data collaboration is the backbone of modern AI innovation, especially as organizations work with external partners to unlock new insights. Databricks Clean Rooms enable privacy-centric collaboration by letting partners run shared analysis on sensitive data without exposing raw records or intellectual property. Teams approve the logic together and control what outputs leave the clean room. We’ve answered the top 10 questions about what Clean Rooms are, how they protect data, and how to get started. https://lnkd.in/gkquXBQp

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Learn how to deploy a PyTorch model in Snowflake ML to generate recommendations for cross sell and upsell opportunities.   Follow along in this quickstart to see how Snowflake ML provides an integrated set of development and inference capabilities that:  ✅ Eliminate silos and governance challenges ✅ Simplify infrastructure management ✅ Maintain consistency and efficiency across training and serving ✅ Scale out end-to-end workflows  Try the quickstart: https://lnkd.in/gXeRbpVB Learn more about Snowflake ML at www.snowflake.com/ml

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I’m thrilled to announce that Gemini 2.5 Flash is now available for in-region ML processing on Google Cloud in Canada! 🇨🇦 This is a game-changer for Canadian businesses needing data residency and compliance. We've moved fast to bring you this capability, ensuring that when you are using Gemini 2.5 Flash, you have the option for your machine learning workloads to stay right here within Canada's borders. What does this mean for you? - Enhanced Data Residency: Meet regulatory requirements related to machine learning processing and data residency with confidence. - Optimized Performance: Gemini 2.5 Flash is built for speed, efficiency, and scale, perfect for high-throughput tasks like summarization, chat apps, and data extraction. - Production-Ready AI: Get stability, reliability, and scalability for your critical AI applications. We're proud to bring this advanced AI power closer to our Canadian customers, empowering you to innovate securely and efficiently. For more details: http://bit.ly/4eR3waS Let's build the future of AI together! #generativeai #ai #cloudcomputing #googlecloud #cloud #google #machinelearning #ml

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Unify cloud cost data and your hybrid and multi-cloud Oracle Cloud Infrastructure (OCI) with Datadog Cost Management (CCM). Now it’s easy to access the same daily cost visibility, allocation, and optimization capabilities for OCI that you already use for AWS, Microsoft Azure, and Google Cloud. In this blog post, find out how OCI support in CCM helps you: ✔️ Get granular visibility into costs ✔️ Find new opportunities for savings ✔️ Avoid added overrun expenses ✔️ Control unpredictable AI costs Learn more: https://lnkd.in/eDAFfwdx

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Databricks set a new direction at the 2025 Data + AI Summit. The new mission is clear: build and deploy full-scale data and AI products on a unified, AI-native platform. Here’s what’s new: • Lakebase – Real-time workloads, fully integrated • Agent Bricks – Build, deploy, and manage AI agents • Lakeflow + Declarative Pipelines – From pipeline code to pipeline intent • Lakebridge – AI-powered onboarding, accelerated • Unity Catalog Metrics – Governed business metrics, centralized • AI/BI – Business intelligence with an AI-native interface • Databricks Apps – Serverless, data-native applications • Databricks One – Unified view of metrics, apps, and agents • Free Edition – No-cost access for small teams and prototyping We broke down what each launch means, and how they’re changing the way teams execute AI at scale. Get the details: https://hubs.li/Q03sMykq0 #DataAISummit #Databricks #Indicium #AIExecution #IndiMesh #ModernDataStack

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Great news for Spanner and BigQuery users. Datastream now provides a seamless, serverless bridge from Spanner’s transactional power to the high-performance analytical capabilities of BigQuery and BigLake. Get the most scalable and available database that runs Google with BigQuery and BigLake’s serverless AI-Native Lakehouse!

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The good news just keeps coming! We are excited to share SQLift: an end-to-end Databricks migration accelerator. A certified Databricks Brickbuilder Solution, SQLift revolutionizes how organizations modernize their data infrastructure. It automatically converts and optimizes legacy SQL from over 20 data platforms into clean, production-ready Databricks workflows. SQLift ensures a faster, more reliable path to Databricks — so your team can focus on delivering insights, not rewriting code. 💡 Learn more about how SQLift is transforming data engineering: https://lnkd.in/e8uJWp2m

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Seeing Microsoft Agent Framework reach Release Candidate landed closer to home than I expected. Across customer conversations in Canada, one pattern keeps repeating: getting an agent to work is easy. Operating agent systems reliably is where teams struggle. That’s why this RC matters. It marks a shift from experimentation to engineering, where multi-agent coordination, observability, and governance stop being afterthoughts. Release Candidate doesn’t mean “done.” It means stable enough to standardize on, teach teams, and design operating models around. The competitive advantage won’t come from who tries agents first. It will come from who can design, deploy, and operate them responsibly at scale without slowing innovation or increasing risk. https://lnkd.in/g4RrWYyq

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𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐦𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐬 𝐡𝐞𝐫𝐞! Discover how Koantek and Databricks are harnessing AI-powered automation to accelerate cloud migrations, optimize data infrastructure, and drive cost efficiencies. 𝐖𝐚𝐭𝐜𝐡 𝐭𝐡𝐞 𝐨𝐧-𝐝𝐞𝐦𝐚𝐧𝐝 𝐰𝐞𝐛𝐢𝐧𝐚𝐫 𝐭𝐨 𝐞𝐱𝐩𝐥𝐨𝐫𝐞: 🔹AI’s role in transforming technology services and unlocking new growth opportunities. 🔹 How Koantek’s Agentic Transpiler automates code translation and optimizes workloads on Databricks. 🔹 AI-powered tools that simplify assessment, planning, and cost forecasting for seamless migrations. Ready to transform your data strategy? 🎥 𝐖𝐚𝐭𝐜𝐡 𝐭𝐡𝐞 𝐨𝐧-𝐝𝐞𝐦𝐚𝐧𝐝 𝐰𝐞𝐛𝐢𝐧𝐚𝐫: https://lnkd.in/gzUmtrzX #DataMigrations #CloudTransformation #Koantek #Databricks

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Engineering at Snowflake means solving problems no one has tackled before. For Principal Software Engineer Hyungtae Kim and the Cortex team, that meant designing GPU-powered infrastructure flexible enough to handle any AI workload, at scale. Get a behind-the-scenes look at how the foundation was built, what makes it different, and how it’s powering the next era of GenAI: https://bit.ly/4kKjGVI

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Still confused between MCP, A2A, and Functional Calling? These three approaches are reshaping how LLMs and agents operate at scale, but each one solves a very different problem. Let’s break it down 👇 1. Model-Context Protocol (MCP) MCP manages dynamic context windows across enterprise LLM infrastructure. It retrieves prior memory, history, and metadata to personalize every response without hardcoding logic into prompts. 2. Agent-to-Agent Protocol (A2A) In A2A, agents talk to each other directly using shared protocols. Think of it as collaborative problem-solving between specialized local agents, especially useful in multi-agent ecosystems. 3. Functional Calling Here, an LLM acts like a planner. It decomposes a task (e.g., comparing market caps), generates a Directed Acyclic Graph (DAG) of subtasks, and then sends each task to appropriate tools (math, search, APIs) for execution. Conclusion - MCP is about managing context, - A2A is about agent collaboration, - Functional Calling is about task execution. Mastering all three is key to building powerful, production-ready AI systems. Save this visual for your next AI project!

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A Manatt team led by Thomas Michael represented Mooncake Labs in its strategic acquisition by Databricks. The team supported the integration of Mooncake’s innovative technology and talent to accelerate Databricks’ vision for both Lakehouse and Lakebase. Mooncake Labs, renowned for its pioneering work in data infrastructure and developer tools, joins Databricks to help advance unified #datamanagement and #AI-driven analytics for organizations around the globe. The team also included John Heber, Eric Bergner, Bill Broome, Burak Ahmed, Lucy White, Tia Thevenin, Melanie DeFiore and Catherine Larson. Learn more here: https://lnkd.in/emCBYdmD

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The game just changed for enterprise AI. With Gemini 3 now native in Databricks, you can build smarter AI agents directly on your enterprise data no setup, no data movement, and full governance.   Run your models securely and seamlessly using SQL, Python, and Databricks-native tools, unlocking faster insights and smarter automation without ever leaving your workspace. At Sparity, we’re embracing this new addition to supercharge our solutions, empower our teams, and stay ahead in delivering AI-driven excellence. #Databricks #Gemini3 #EnterpriseAI #AIAgents #InnovationAtWork #SparityTech

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