Sandeep Mendiratta

Enterprise data pulls in two directions. On one side: governed BI workloads — structured, compliant, and built for business reporting. On the other: unstructured pipelines — streaming feeds, heavy transformations, and machine learning at scale. Most organizations now run Snowflake and Databricks in parallel to handle this dual reality. Yet stitching them together creates new challenges: deciding which workloads belong where, moving data without waste, and keeping governance consistent across environments. Each platform brings distinct strengths to the table — Snowflake excels at governed analytics and BI reporting, while Databricks powers heavy transformations, streaming, and machine learning. When combined thoughtfully, these strengths converge into a unified architecture that supports the full data lifecycle. > Watch the on-demand discussion on how Janus Henderson Investors designed a unified architecture that balances both worlds: https://lnkd.in/eG-8YBqm In a financial services setting where accuracy, performance, and cost discipline matter most, Janus Henderson’s approach connects Snowflake’s strengths in analytics and governance with Databricks’ capabilities in engineering and ML. #unifieddataarchitecture #snowflake #databricks #usecase

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‘’It is a time of maximum disruption, and many successful companies will disappear’’  – Snowflake CEO Sridhar Ramaswamy Chatbots are rewriting support, workflows will flip in two years, and only resilient leaders will win. I sat with Snowflake’s CEO, and here’s what we discussed: • How AI is reshaping whole categories • Why 2 years will rewrite most business workflows • What “AI at work” really needs - from data to evals Watch our full conversation here: https://lnkd.in/dPRgcsw6

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Let’s be clear: Snowflake and Databricks are not data unification platforms. We’ve been seeing more and more cloud data platforms suggest they can do what Reltio does. Here’s why that claim doesn’t hold up—and why it matters. ✅ They store data. ❌ They don’t unify it. Yes, these platforms can ingest and analyze data—but they don’t resolve duplicates, don’t create trusted golden records, and don’t provide the real-time, cross-domain, relationship-rich profiles that power AI agents, operational systems, and customer experiences. ✅ They support analytics. ❌ They don’t deliver operational trust. Data warehouses are optimized for batch analytics—not for real-time, governed, enterprise-wide data delivery. “Good enough” data might be passable for dashboards. But for autonomous AI agents making real-time decisions? It’s a risk. ✅ They are general-purpose. ❌ They’re not purpose-built. They offer a flexible toolkit—but you build everything yourself. No prebuilt governance. No master data logic. No native matching. It’s a blank canvas—and a long road. ✅ They speak SQL. ❌ Business doesn’t. If your data platform needs a SQL query to answer “Who is my customer?”, you don’t have a data platform—you have a technical debt machine. ✅ They are rooted in relational logic. ❌ But today’s enterprise is not. Modern data is messy, interconnected, multimodal. Business relationships matter. Time and context matter. Graph-native architecture is no longer a nice-to-have—it’s a requirement. ✅ They work well with Reltio. ❌ They don’t replace Reltio. Reltio Data Cloud™ is the agentic data fabric that sits upstream—unifying, governing, and enriching data before it enters your warehouse, app, or agent. Snowflake and Databricks? They’re consumers of that clean, unified data—not creators of it. In the age of agentic AI, trust and context in data aren’t optional. They’re the foundation. That’s why global leaders choose Reltio to unify and mobilize the data that drives their business. ✅ Let’s stop confusing pipelines with platforms. ✅ Let’s get real about what it takes to fuel AI with confidence. ✅ Let’s start with trust. #Reltio #AgenticAI #DataUnification #Snowflake #Databricks #RealTimeData #DataTrust #MDM #DataCloud  

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Everyone in data is talking about the semantic layer again. Here’s why it matters and where the hype meets reality. Why the renewed interest? ✦ AI & LLMs need structured context to reason about data ✦ Headless BI & composability demand consistent metrics ✦ Governance & contracts live best at the semantic level ✦ Tooling (dbt, Cube, Transform, etc.) is maturing fast But let’s be honest... ✦ Most orgs still struggle with ownership and naming chaos ✦ It’s hard to get adoption across the stack ✦ LLMs might learn semantics from usage, not definitions The future? Semantic layers won’t just serve dashboards, they’ll: → Power AI agents with guardrails → Trigger smarter, impact-based observability → Explain “why” when decisions are made on your data The role of the semantic layer is evolving. In an AI-native stack, it's less about metrics governance and more about making data intelligent and trustworthy by default. Curious how others are thinking about it; agree, disagree? #semanticlayer #dataengineering #datagovernance #aireadiness

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"We all need to stop thinking of AI as magic" Here's how Snowflake CEO, Sridhar Ramaswamy, looks at what AI can do for us Watch our full conversation here: youtu.be/d9v8gwTxx_o

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