Ashutosh Kulkarni

The demand for efficient, open, and accelerated data processing is only growing, and it’s great to see Voltron Data making big strides with its Accenture partnership. Their commitment to Apache Arrow, open-source analytics, and scalable AI infrastructure continues to drive innovation in the ecosystem. Normally the team at Voltron Data would help people deploy on GPU servers they own, but starting today you can access and test running SQL queries on GPUs using Voltron Data's Theseus: https://lnkd.in/gdhZREWy

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Redis to acquire Decodable! "As the world pushes deeper into the age of LLMs, one of the biggest challenges developers will face is ensuring the right context goes to the right place at the right time. We’re helping to solve this by strengthening our capabilities as a real-time context engine that enables instant data access for agents.", Rowan Trollope Check out the announcement: https://lnkd.in/gNkggWWQ

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🚀 Sharing BigQuery's Momentum: 5X Customers, Unified AI & Data, and What's Next! It was a pleasure to sit down with Savannah Peterson and David Vellante on SiliconANGLE & theCUBE at Google Cloud Next recently! ✨ We had a fantastic discussion diving into the incredible scale and innovation happening with our data platform, BigQuery. I was excited to share some key updates reflecting the momentum we're seeing: ✅ 𝟱𝗫 𝘁𝗵𝗲 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀: For the first time, we shared that BigQuery now serves 5 times the number of customers compared to key competitors in the data warehouse/AI platform space – a testament to the trust and value customers are finding. ✅ 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗿𝗴𝗲: We've seen a 300% increase in the use of our migration services over the past year as organizations increasingly choose Google Cloud to modernize their data estates and leverage our AI capabilities. ✅ 𝗠𝗮𝘀𝘀𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗚𝗿𝗼𝘄𝘁𝗵: We're now managing exabytes of data, with a 30% increase in volume over the last year, reflecting the significant workloads customers are running and trusting us with. We also dove into the crucial shift towards truly unifying data and AI 🧠 – bringing AI to the data foundation, not just bolting it on. I explained how our new Autonomous Data Foundation capabilities, powered by agentic AI, are automating complex tasks. We also touched upon how this technology enables powerful real-time activation, like the fantastic example from Mattel, Inc., who used #BigQuery and #Vertex AI to identify and fix product issues within the same production run! It's an exciting time for data and AI, driven by customer needs and groundbreaking innovation. If you'd like to hear more about these trends and the forces driving BigQuery's growth, you can watch the full conversation here ▶️: https://lnkd.in/d-kMGkgR #GoogleCloudNext #BigQuery #DataAnalytics #AI #CloudComputing #DataStrategy #GenerativeAI #theCUBE Tomas Talius; Sanjay Krishnamurthi; Geeta Banda; Sean Zinsmeister; Peter Bailis; Vinay Balasubramaniam; Abhishek Kashyap; Chaitanya (Chai) Pydimukkala; Vijay Venugopal; Adam Wilson; Shashank Gandhi; Sam McVeety; Alon Halevy; Andi Gutmans; Taylor Mikolasy; Katie Camacho

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🚀 Check out this insightful blog by Erica Hughberg and Alexa Griffith on building a scalable, cloud-native GenAI platform with open-source solutions! Tame the complexity of AI workloads with a unified architecture using Envoy AI Gateway, KServe, and more. Learn how to streamline LLM traffic, enhance security, and boost scalability—whether self-hosting or integrating with providers like OpenAI and Anthropic. 🔗 https://lnkd.in/gEWy5hZh

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🚀 Just announced at AI + Datanova: Starburst unveils the first AI-ready lakehouse platform built for the Agentic Workforce. We’re bringing governed data products, metadata, and open vector access together so AI agents—and the humans they support—can reason, decide, and act faster, with full transparency and control. With support for model-to-data architectures, multi-agent frameworks, and open vector search, Starburst empowers enterprises to scale AI without moving data—ensuring sovereignty, compliance, and confidence. 🔍 Key highlights: - Federated, secure AI access to data anywhere - Multi-agent interoperability with Model Context Protocol (MCP) - Built-in observability and usage governance - Visualized answers from Starburst’s agent 📖 Read the full announcement to see how Starburst is defining the future of AI and analytics: https://okt.to/aFv3X6 #AI #DataLakehouse #AgenticWorkforce #Starburst #GovernedAI #Trino #ApacheIceberg

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In a recent episode of Making AI Real, Abhas Ricky, Chief Strategy Officer at Cloudera, shared his valuable thoughts on the gaps that need to be addressed for AI to drive success. From preventing hallucinations to prioritizing security, it's all about having a holistic strategy. Abhas Ricky brings a wealth of expertise in driving corporate strategy, growth, and innovation. As the Chief Strategy Officer at Cloudera, he leads the company's vision, creates impactful business models, and executes key transformational initiatives. Grateful to have your insights here, Abhas. Watch the full episode here: https://lnkd.in/ghz5EX9H #MakingAIReal #ITLeadership #Leadership #AI #DataSecurity #Innovation

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Cloud cost uncertainty isn’t a fluke; it’s by design. Cloud providers profit from complexity: 💸 Granular billing 💸 Shifting discount programs 💸 Opaque pricing for AI workloads 💸 Exploding SaaS and shadow IT The result? Engineering and FinOps leaders are left scrambling to explain unpredictable cloud bills while innovation can’t slow down. Traditional dashboards and static budgets won’t save you. You need adaptive cloud cost management that embeds cost awareness into engineering workflows, detects anomalies early, and drives shared accountability. We break it all down in our latest post, including 5 steps to start leading through the chaos. Read it here: https://lnkd.in/ge4jadxr #FinOps #CloudCost #EngineeringLeadership #CloudComputing #AI #SaaS #Ternary

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Low-code is no longer a trend—it’s becoming core infrastructure. Google’s acquisition of Wiz may be making headlines in cybersecurity, but for those of us watching market signals closely, it marks something bigger: Strategic M&A is back—and it’s going to accelerate across enterprise tech. Having led go-to-market for a low-code platform across Emerging Markets, I’ve seen firsthand how these tools go from tactical quick wins to mission-critical systems. In regions where agility, cost-efficiency, and self-service matter most, low-code isn’t just enabling innovation—it’s driving it. As AI continues to reshape how we work, the platforms that help people move faster—without depending on overburdened IT or lengthy dev cycles—will define the next wave of enterprise software. So, what should we expect? • Low-code/no-code players will move from disruptors to acquisition targets • The real value will lie in platforms that are extensible, enterprise-ready, and AI-integrated • GTM leaders, founders, and investors in this space need to stay sharp—the spotlight is shifting fast I believe we’re entering a new era where low-code becomes the layer that connects strategy to execution—at scale.

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Confluent has been a leader in data streaming for a long time, but it’s become too expensive with the way most businesses rely on the cloud now. Why? It’s built on traditional Kafka architecture, designed for a previous IDC era. While it’s robust, it’s not cost-effective anymore. That’s where AutoMQ comes in. It’s a game-changer. By rethinking Kafka from the ground up and adding shared-storage, AutoMQ cuts costs and works perfectly for cloud-based operations. You can perfectly see the cost gap between Confluent and AutoMQ from this illustration. What’s your take on this cost comparison? Let me know in the comments! #DataStreaming #KafkaAlternatives #CloudNative #CostOptimization #AutoMQ #TechInnovation #ApacheKafka

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