The Ravit Show

Ravit Jain
  • 5.0 (2)
  • Technology
  • Updated Semiweekly

The Ravit Show aims to interview interesting guests, panels, companies and help the community to gain valuable insights and trends in the Data Science and AI space! The show has CEOs, CTOs, Professors, Tech Authors, Data Scientists, Data Engineers, Data Analysts and many more from the industry and academia side. We do live shows on LinkedIn, YouTube, Facebook and other platforms. The motto of The Ravit Show is to the Data Science/AI community grow together!

  1. 7h ago

    Cisco's Vision for AI-Native Operations: Cloud Control, AI Canvas, and the Future of IT

    For years, IT teams have been forced to manage growing complexity with more tools, more dashboards, and more manual effort. What if AI could help bring all of that together? At Cisco Live, I sat down with DJ Sampath, SVP & GM, AI Software and Platform at Cisco The Ravit Show, to discuss Cisco Cloud Control, AI Canvas, and how AI is changing the way IT teams operate. A few key insights from our conversation: * Operational fragmentation continues to be one of the biggest challenges for enterprise IT teams * Cisco Cloud Control is focused on providing a more unified way to manage increasingly complex Cisco environments * AI Canvas is designed to be more than an assistant. It introduces an agentic workspace where people and AI can work together to solve problems * Some IT challenges are too complex for a single tool or a single person. A collaborative, multiplayer approach can help teams move faster and make better decisions * The future of IT operations may be less about navigating dashboards and more about orchestrating outcomes with AI-powered systems One thing that stood out to me: The conversation around AI is shifting from answering questions to helping teams take action. That's a very different future than the one many organizations are planning for today. Great discussion with DJ on what AI-native platforms could mean for enterprise operations over the next few years. #data #cisco #ciscolive #ai #theravitshow

  2. 1d ago

    How Cisco and Splunk Are Building Trusted AI Agents for the Enterprise

    Most AI agent conversations start with what the agent can do. Very few focus on how you manage, monitor, and trust those agents once they're in production. At Cisco Live, I sat down with Kamal Hathi, SVP & GM of Splunk at Cisco on The Ravit Show, to discuss what enterprises need beyond AI models to make agents reliable, secure, and trustworthy. A few key takeaways from our conversation: * Moving AI agents from demos to production requires visibility into how they operate, make decisions, and interact with enterprise systems. * As organizations deploy more agents, observability becomes critical. Without it, AI can quickly become a black box. * Data remains one of the biggest challenges. Enterprises are looking for ways to reduce tool sprawl while maintaining a unified view across their environments. * Security and observability are no longer separate conversations. The faster teams can connect operational issues with security events, the faster they can respond. * Making AI accessible is important, but governance cannot be an afterthought. Innovation and control must go hand in hand. One thing that stood out to me: The future of AI isn't just about building smarter agents. It's about creating the trust, visibility, and governance needed to operate them at enterprise scale. Great conversation with Kamal on the next phase of enterprise AI and the role observability will play in making it successful. #data #cisco #ciscolive #ai #theravitshow

  3. 4d ago

    Future of AI Infrastructure: Building AI Across Multiple Data Centers | Rakesh Chopra

    Everyone is talking about bigger AI clusters. What happens when those clusters need to span multiple data centers? At Cisco Live, I sat down with Rakesh Chopra, SVP & Fellow, Common Hardware Group at Cisco on The Ravit Show, to discuss one of the less talked about challenges in AI infrastructure: scaling AI beyond a single data center. A few key themes from our conversation: -- The industry is moving from scale-up and scale-out to scale-across architectures -- Connecting GPUs across data centers is becoming a critical challenge as organizations build larger AI environments -- Power efficiency is now as important as raw performance, driving innovation in silicon and optics -- Network reliability and low-latency communication are essential as AI clusters stretch across geographic boundaries -- Co-designing networking, silicon, and optics is becoming a requirement rather than an optimization The AI conversation often focuses on models. But the real story may be the infrastructure required to make those models work at scale. #CiscoLive #AI #Networking #Innovation #TheRavitShow

  4. 5d ago

    How AI Agents Will Change Data Engineering Forever

    For years, data engineering has been about building pipelines, warehouses, dashboards, and choosing the right tools. But what if we've been solving the wrong problem? I recently sat down with Sai Sundar from WALT, who has spent decades building data platforms at Apple, Yahoo, LinkedIn, Chime, and GEICO. One idea from our conversation really stood out. Companies don't need more data tools. They need better business outcomes.Sai explained how data teams often work in silos. Engineers build pipelines. Business teams ask questions. Analysts sit in the middle translating requirements. The result is slow decisions, duplicated work, and endless back-and-forth.The next evolution isn't another platform.It's creating systems that understand business goals, work with your existing data stack, and help organizations make trusted decisions faster.Some of the topics we covered:* Why data has historically been treated as a second-class citizen* Why business outcomes matter more than adopting the latest technology* How AI is changing the role of data engineering* Why trust and transparency are becoming essential in enterprise AI* What the future of conversational data engineering could look likeThis conversation isn't just about AI.It's about rethinking how data teams create value for the business.#data #ai #dataengineering #walt #theravitshow

  5. 6d ago

    Why Data Engineering Is Broken and How AI Agents Will Fix It

    Why would someone leave Apple, LinkedIn, and Meta to join an early stage startup? That was the first thing I wanted to ask Ranjith Prabu, CTO when he sat down with me at the WALT AI office in Santa Clara on The Ravit Show. He spent two decades building and scaling data platforms at some of the biggest companies on earth. Now he is the CTO of WALT AI. His answer was simple. Even the best resourced companies on the planet still struggle with data engineering. It is the bottleneck nobody talks about. Engineers build the pipelines but never reach the insight. Analysts have the questions but cannot touch the plumbing. Work gets thrown over the wall, and value leaks at every handoff. Ranjith calls this the chasm. He left to close it. A few things from our conversation that stuck with me. Data engineering used to be locked away. It needed huge teams, huge budgets, and armies of consultants. The way cloud opened up infrastructure, agents are starting to open up data engineering. Determinism matters more than people think. If the CEO asks the same question twice, the answer has to be identical. A model writing fresh SQL every time cannot promise that. That is the line between a demo and production. Tribal knowledge should not live in one person's head. Why you exclude Q2 returns should not walk out the door when an analyst quits. It should live in the system. And data quality is where most data projects quietly die. You can build the most elegant pipeline in the world, but if one number is wrong, trust is gone. Once trust is gone, nobody uses the platform. The part I keep thinking about. Tools give you capability. They do not give you the outcome. The outcome still takes people and months of work. That gap is the real problem, and it is the one Ranjith is now building to solve. Worth your time if you care about where data engineering is heading. #data #ai #dataengineering #walt #theravitshow

  6. Jul 14

    Key Takeaways from POSETTE 2026

    PostgreSQL is no longer just a database conversation. It's becoming a platform conversation. I had the opportunity to sit down with Claire Giordano, Principal Group PM Microsoft near Stanford University right after POSETTE: An Event for Postgres to discuss the biggest takeaways from one of the largest PostgreSQL events in the world. A few themes stood out: * PostgreSQL adoption continues to accelerate across organizations of every size * The ecosystem around Postgres keeps expanding, making it easier to build modern data and AI applications * AI was impossible to ignore, but the conversation wasn't about replacing databases. It was about how databases can provide the context, reliability, and foundation AI systems need * The community remains one of PostgreSQL's biggest strengths, with contributors and companies working together to push innovation forward One of the most interesting parts of our discussion was where PostgreSQL goes next. As organizations look to build AI-powered applications, support real-time workloads, and simplify their data architectures, PostgreSQL continues to find itself at the center of those conversations. The database landscape keeps evolving, but PostgreSQL's momentum shows no signs of slowing down. In this episode, Claire shares: * Her biggest takeaways from POSETTE 2026 * The PostgreSQL trends generating the most excitement * Surprising announcements and discussions from the event * How AI is influencing the PostgreSQL ecosystem * What this year's event tells us about the future of PostgreSQL * What the community should be paying attention to next #data #ai #postgresql #database #opensource #theravitshow

  7. Jul 13

    Collibra on the Future of Enterprise AI: Governing Structured and Unstructured Data

    Everyone wants better AI models. A few days back at Data Citizens on the Road by Collibra, I sat down with Reece Griffiths, Field CTO at Collibra on The Ravit Show, to discuss one of the biggest challenges facing enterprise AI today: unstructured data. For years, data governance focused primarily on structured data. But AI is changing the game. Today, enterprise knowledge lives across PDFs, presentations, images, documents, emails, and shared drives. If that content isn't properly governed, AI systems can quickly run into problems: * Generating answers from outdated or draft documents * Exposing sensitive information due to missing confidentiality labels * Missing relevant content because of poor metadata and classification One concept from our discussion really stood out: Knowledge decay. Even the most advanced AI models will struggle if the underlying knowledge base is stale, incomplete, or poorly maintained. We also discussed why enterprises are moving toward unified semantic models that connect structured and unstructured data, allowing AI systems to understand business context consistently across the organization. The takeaway? The future of enterprise AI won't be determined solely by model performance. It will be determined by the quality, freshness, and governance of the data behind it. #Data #DataCitizens #Collibra #AI #GenerativeAI #DataGovernance #AIGovernance #EnterpriseAI #Metadata #DataManagement #TheRavitShow

  8. Jul 10

    AI Governance Starts with Data Governance

    What if the biggest obstacle to AI success isn't the technology? It's the way organizations are structured. At Data Citizens on the Road by Collibra, I sat down with Joyce Snelders Senior Manager at Deloitte on The Ravit Show to discuss what organizations are experiencing as they move from AI experimentation to enterprise-wide adoption. A few key takeaways from our conversation: * Data governance has gone from a "nice to have" to a business priority because AI is only as good as the data behind it. * Many organizations are building AI agents without common standards, creating duplicate efforts and inconsistent outcomes across teams. * Chief Data Officers are increasingly becoming AI leaders, taking responsibility for both data and AI strategies. * The next phase of enterprise AI is not just about technology. It is about governance, operating models, and change management. * Leaders should start preparing for a future where digital FTEs work alongside human employees. One statement from Joyce stood out: Organizations don't have an AI problem. They have a governance and operating model problem. The companies that solve that challenge first will be the ones that scale AI successfully. #DataCitizens #Collibra #AI #DataGovernance #AIGovernance #EnterpriseAI #DataLeadership #TheRavitShow

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Ratings & Reviews

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About

The Ravit Show aims to interview interesting guests, panels, companies and help the community to gain valuable insights and trends in the Data Science and AI space! The show has CEOs, CTOs, Professors, Tech Authors, Data Scientists, Data Engineers, Data Analysts and many more from the industry and academia side. We do live shows on LinkedIn, YouTube, Facebook and other platforms. The motto of The Ravit Show is to the Data Science/AI community grow together!

Information

  • Creator
    Ravit Jain
  • Years Active
    2021 - 2026
  • Episodes
    570
  • Rating
    Explicit
  • Copyright
    © Ravit Jain
  • Show Website
    The Ravit Show