Krishnan Bhagavath

After a six-month pilot, we’re officially launching Practo in the US. For us, this isn’t just about entering a new market; it’s about seeing something built in India work at a global scale. An India-built healthcare platform is now being used to navigate care in one of the world’s most complex healthcare systems. With deep data, an outcomes-led approach, and a focus on trust and clarity, we’re excited to begin this next chapter, bringing better healthcare navigation to more people, across more geographies. Onwards.

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ambaricloud: 108 recipes. 🥭 https://lnkd.in/gS6fuAtu Ravi Kiran Gurram Anil Koripelly Bhushan Lakhe Raja Purkayastha When this recipe e-book was first published on January 1, 2025, our goal was to give engineers, architects, and data practitioners a hands-on guide to Apache Iceberg. In the months since, the pace of change has been remarkable. Platforms such as Snowflake, Databricks, Dremio, RisingWave, ClickHouse, Cloudera, Lakekeeper, Trino Software Foundation, and Amazon Web Services (AWS) have delivered significant new integrations, and Apache Iceberg itself has reached version 1.10.0, introducing capabilities that reshape how practitioners interact with the format. To reflect this momentum, we are undertaking a ground-up rewrite. Our priorities include: ✅ Iceberg 1.10.0 alignment and deprecation cleanup – removing outdated recipes that no longer reflect best practices while upgrading all demos, examples, and exercises to incorporate the changes introduced in Iceberg 1.10.0. For example, Snowflake’s support for catalog-linked databases to external catalogs makes earlier object integration recipes obsolete. ✅ Ecosystem validation – testing and refining recipes across Databricks, Snowflake, Dremio, and AWS to ensure seamless interoperability with Iceberg 1.10.0. We expect this rewrite to take two to three months. Once complete, this edition will provide readers with a technically accurate, future-ready reference for Iceberg 1.10.0 and its surrounding ecosystems--Snowflake, Databricks, Dremio, RisingWave, ClickHouse, Cloudera, Lakekeeper, Trino Software Foundation, and Amazon Web Services (AWS) Please 🕺 suggest 🔪 comment 🥃 recommend ✅ Spark Variant ---Iceberg1.10.0 spports now. https://lnkd.in/gBmaNrPH

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Enjoyed the fireside chat with Ahmed Bashir Santoshi Kittur and Shuchi Mahajan at Effortless 2025, Mumbai. One of the questions was how do you differentiate your AI powered solution for clients when powerful foundational models are becoming commodity. These are three points which I talked about: * Retrieving the right context to be passed to LLM using hybrid search and personalized reranking * Providing the most relevant skills (or tools) to the LLM powered agent to do the right job and * Using finetuned smaller LLMs for specific use cases

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Best session so far at #procureconwest- the keynote panel on AI. The focus of all the professionals on real life use cases driving the solutions their adopting was refreshing. I talk to so many teams putting the cart before the horses and trying to adopt AI for the sake of saying they are.. Ashish G. Mentioned the importance of having access to the right data at the right time, instantly and the importance of user and organizational context as opposed to generic responses which personally as you all know is close to my heart. BRYAN REDMOND talked about the importance of data governance especially in the context of #vendorlifecyclemanagement which to me is the most important thing in procurement that isn’t discussed enough. James Chang emphasized the importance of the journey- agentic is exciting, but we need to start small, building trust before we jump into blind automation. As opposed to other sessions on AI which were more of the same hype and talk about data this one was real life focused and actually insightful! ProcureCon US Opstream The Clorox Company Umpqua Bank Symetra #procurement #procurementai #procurementorchestration #digitaltransformation #procurementdatasynthesis

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Analytics India Magazine recently interviewed Rohit Vyas & I, where I reflect upon something we see every day with our customers: AI ambitions are rising fast, but batch data is holding many teams back. What excites me most is seeing AI agents move beyond chat interfaces to operational intelligence: detecting issues, triggering actions, and improving experiences autonomously. That’s the frontier we’re building towards with Confluent Intelligence. Read the full interview here: https://lnkd.in/gxuWWJGs

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