Tarvinder Singh

#Training and #upskilling our customers and partners with the latest data and AI skills is incredibly important to Databricks. That’s why we have opened a new Data + AI Academy in #India, which will support the training of more than 500,000+ data and AI professionals over the next three years. With an increasing need for AI talent, the academy will meet the demands for advanced data and AI skill sets and have a big impact on our customer and partner ecosystem in EMEA. Take a look at the full story here 👉https://lnkd.in/dEGaAgJR Ed Lenta, Rochana Golani, Ankush Korla, Vinod Marur, Anna Lewis

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It is hard to understand Fabric's long-term strategy. On one hand, it was smart to build it around PBI, the leading BI platform. On the other hand, the key to Power BI's success in dethroning Tableau was its' extremely affordable licensing. Competitive licensing is not an element that Fabric's always-on pricing model can boast of at current rates, especially when the likes of Databricks, Snowflake, and other rising platforms are around. Perhaps Microsoft will be content with Fabric always being the 3rd/4th best option?

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Accuracy doesn’t come from bigger models — it comes from grounding. Microsoft’s Fabric latest release weaves Graph + Maps + MCP into the enterprise stack For CDAOs and data leaders, here’s what matters: 1. Graph + Maps come to Fabric Adding it native to fabric and simplifying use is a reinforcement that context is the new control plane with the rise of AI. 2. Official MCP Server The Model Context Protocol makes the Fabric agent-ready. No more custom plumbing. Fabric assets (tables, pipelines, models) can be more safely exposed to AI agents, right inside and leveraging your Microsoft governance framework. Bonus: It’s already integrated into VS Code and GitHub Codespaces, so developers can build within their daily workflow. 3. LinkedIn Graph under the hood This is the quiet but strategic play. Microsoft is operationalizing LinkedIn’s proven graph tech in Fabric. Today, it grounds agents. Tomorrow, it could bridge LinkedIn’s people/skills/company graph into enterprise solutions and decisioning. 💡 Why it matters: Fabric is shaping up as Microsoft’s answer to the data-to-decision stack: not just unifying data, but grounding AI agents in enterprise context, governance, and graph-rich reasoning. Rivals have MCP servers too, but Microsoft, as it has done in the past, is bundling. Bundling governance, graph, and developer experience into one package. Check out the Microsoft release. I put a link below👇🏻 Question for you: Would grounding your AI agents in a graph and geospatial context change your 2026 roadmap? #Fabric #Microsoft #CDAO #DecisionAutomation #GraphRAG

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15 years back, when we were building voice bots for the US market - one of the key debates used to be whether to declare or not to declare its a bot. Even if not explicitly say things like "I can help you with X,Y, Z queries and you can say things like .... " indicating a limited understanding. Even if the users did not understand what a virtual assistant is - they understood this is not a human being speaking on the other side. European regulations strictly mandate the voice agents to declare before proceeding in a conversation. India is an upcoming market but getting into the space at different stage of maturity of conversational engines - Hence there is a lot of asks to cover up the underlying bot and not by any means show up its a machine. The experience should be "human like" but that does not mean we should hide the machine behind. For an end consumer, its important to know the species they are speaking to and I now see why some of the western paradigms firmly support the declarative approach. Its just more human.

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Big shoutout to our team member Roshni Mishra for publishing an article on Reverse ETL (RETL) using native Snowflake features! In her first Medium post, Roshni walks through how to flip the traditional ETL process by sending insights back into operational systems like sales and marketing apps, closing the loop between analytics and execution. What makes this powerful is that it’s all built natively on Snowflake: secure data sharing, dynamic tables, and Snowpark-powered pipelines that let you operationalize insights in real time without complex data movement or external tools. This is a great example of how teams across Snowflake are pushing the boundaries of what’s possible with data: from silos to solutions, and from insight to impact. Fantastic work, Roshni! Read her post here: https://lnkd.in/gt2_b4yG

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An insightful exploration of the complexities behind building robust Text-to-SQL systems. This article from Google Cloud dives into the technical and contextual challenges involved in generating accurate, persona-aware SQL queries from natural language inputs. A must-read for anyone interested in the intersection of AI, databases, and user-centric design. #GoogleCloud #TextToSQL #AI #NLP #Databases https://lnkd.in/gY-JrSPA

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EDB Postgres AI is here. The first sovereign Postgres platform built for AI. 🔹 Unified control of transactional, analytical, and AI workloads 🔹 Built-in NVIDIA + Red Hat integrations 🔹 Five-line pipelines to production 🔹 Hybrid-native observability across your estate Postgres has already won. Now with EDB, it powers the next generation of intelligent infrastructure. Read the release 👉 https://bit.ly/3TsFwB6 #EDBPostgresAI #SovereignAI #Postgres #HybridCloud #GenAI #OpenSource #AgenticAI #DataSovereignty

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With rapid release of foundational AI models by different providers, real moat is going to be : 1. Domain specific data 2. Solving the last mile use cases of your offerings 3. User experience - Fine detailed orchestration using 1 and 2

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When your analytics cluster sits idle 90% of the time but you still pay for 100% capacity, that's a broken architecture. Fascinating chat with Sudarshan Lakshminarasimhan (Founding Engineer at e6data) on the latest 0P episode. We talk about the world before Agile, building high-performance data engines, and the evolution of scalable data lakehouse architectures - going deep on how E6data is reimagining query engines for highly variable workloads. When one complex query hits your system, why double your entire infrastructure? Their approach is to scale only what you need, when you need it. Really enjoyed digging into this with you, Sudarshan. 🎧 Full episode here: https://lnkd.in/dwZ6Tsb5 Zero Prime Ventures

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📊 While RAG for unstructured text steals the spotlight, text-to-SQL for structured data is quietly transforming how enterprises explore and analyze information.   I recently explored how Databricks is re-imagining data warehousing, making it easier than ever to turn massive datasets into AI-ready insights, all while keeping governance and performance in check.   In my latest piece for Towards AI publication on Medium, I cover: - 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐯𝐢𝐚 𝐔𝐧𝐢𝐭𝐲 𝐂𝐚𝐭𝐚𝐥𝐨𝐠: Metadata, access control, and compliance in one place - 𝐒𝐐𝐋 𝐨𝐧 𝐭𝐡𝐞 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞: Delta tables + Medallion architecture = cleaner, faster analytics - 𝐋𝐚𝐤𝐞𝐟𝐥𝐨𝐰 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: Ingestion to analytics in a single governed pipeline - 𝐀𝐈/𝐁𝐈 𝐆𝐞𝐧𝐢𝐞: Ask questions in plain English, get instant SQL-backed answers   And yes, I even got hands-on with Genie, running queries and testing results… all using Databricks’ free edition!   Databricks isn’t just for data engineers or scientists anymore - it’s an AI-ready warehouse that puts analytics, governance, and natural language querying in the hands of every technical 𝐚𝐧𝐝 business user. 🎥 𝘞𝘢𝘵𝘤𝘩 𝘮𝘺 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘎𝘦𝘯𝘪𝘦 𝘥𝘦𝘮𝘰: https://lnkd.in/g_DZGiD9 📖 𝘙𝘦𝘢𝘥 𝘮𝘺 𝘣𝘦𝘨𝘪𝘯𝘯𝘦𝘳-𝘧𝘳𝘪𝘦𝘯𝘥𝘭𝘺 𝘣𝘭𝘰𝘨 𝘱𝘰𝘴𝘵: https://lnkd.in/gJfxS5Ms   #Databricks #TowardsAI #BeginnerFriendly #DataEngineering

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🌏 Amazon Bedrock Data Automation is now live in the Sydney AWS Region! 🇦🇺 Bedrock Data Automation (BDA) capability is officially available in Sydney, right here in the Asia Pacific along with Mumbai. 🛠️ What is Bedrock Data Automation? BDA harnesses generative AI to help organizations automate insights from all kinds of content—documents, images, audio, and video. It’s built for enterprises needing reliable, fast, and secure automation at any scale. 🚀 What’s new with this launch? Expanded regional availability: With Sydney joining the list, Bedrock Data Automation is now available in 7 AWS regions, including US West (Oregon) and US East (N. Virginia) and Mumbai Enables local data residency: Australia based organizations can run data automation workloads while meeting strict compliance and data residency requirements. Supports cross-region inference: Optimize performance and throughput with seamless routing across regions within your geography. 📈 Why does this matter for Australia? Now enterprises, startups, and developers across Australia can power intelligent document processing, media analysis, and multimodal workflows—while keeping data onshore. This is set to accelerate digital transformation and AI innovation for regulated industries, public sector, and every organization looking to do more with their data. 🎉 If you’re in Australia and ready to scale your AI automation securely and efficiently, now is the time! Head to the AWS Console to get started and see what Bedrock Data Automation can do for you today!

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