The Ravit Show

Ravit Jain

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. 1 hr ago

    How AI Is Redefining Enterprise Integration with Capgemini and Boomi

    For years, integration was viewed as plumbing. Today, it is becoming one of the most important foundations for AI. At Boomi World Tour London, I had the opportunity to speak with Rahul Murudkar from Capgemini about how AI is reshaping the integration landscape and why enterprises are rethinking the way they connect applications, APIs, and data. The conversation came at an exciting time for Capgemini, which was recently recognized as Boomi FY26 EMEA Growth Partner of the Year, highlighting the momentum the company is seeing across integration, automation, and digital transformation initiatives. What I found particularly interesting was how the discussion shifted from technology to business impact. As organizations pursue AI initiatives, integration is no longer just about moving data from one system to another. It is about creating intelligent, connected environments where information can be discovered, accessed, and acted upon in real time. In our discussion, we covered: * How AI is changing the role of enterprise integration * The challenges organizations face with disconnected systems and integration debt * How modern integration platforms are helping teams work more efficiently * The impact of AI on API management and automation * What measurable outcomes customers are seeing from modern integration strategies * Why integration is becoming a critical enabler for enterprise AI A great conversation for anyone thinking about the future of enterprise architecture, integration, and AI. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld ##BoomiAmbassador #theravitshow

  2. 3 days ago

    How Cognizant and Boomi Are Helping Enterprises Modernize Data, AI, and Automation

    One thing I've noticed across nearly every AI conversation this year is that organizations are becoming much more focused on outcomes than technology. At Boomi World Tour London, I sat down with Azin Nylander from Cognizant to discuss what customers are actually asking for when they begin their AI, automation, and modernization journeys. The answer is often simpler than many people think. They want faster decision-making, better access to data, reduced complexity, and the ability to scale innovation without constantly rebuilding their technology stack. We explored: • The business challenges driving enterprise modernization initiatives • How organizations are approaching AI and automation investments • What successful data modernization projects have in common • The role integration plays in creating AI-ready enterprises • Why customer outcomes remain the most important success metric I particularly enjoyed hearing Azin's perspective on where enterprise transformation efforts are headed and how customer expectations continue to evolve. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld ##BoomiAmbassador #theravitshow

  3. 4 days ago

    How AWS and Boomi Are Helping Enterprises Build AI Agents at Scale

    Everyone is talking about AI agents. What fewer people are talking about is what happens behind the scenes to make those agents actually work. At Boomi World Tour London, I spoke to Andrea Bureca from AWS to discuss the growing intersection of AI, data management, and enterprise integration. One theme that emerged throughout our conversation was that successful AI initiatives are rarely just about AI. They depend on access to trusted data, reliable integrations, governance, and the ability to connect systems across the organization. We discussed: • The patterns AWS is seeing across enterprise AI deployments • How customers are approaching agentic AI initiatives • Why data management is becoming increasingly important for AI success • Common mistakes organizations make when scaling AI projects • The value of partnerships in helping customers move faster and reduce risk • What customers can expect from the AWS and Boomi relationship moving forward If your organization is thinking about AI agents, this conversation offers a practical perspective on what it actually takes to make them successful. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld ##BoomiAmbassador #theravitshow

  4. 6 days ago

    Why AI Fails at Scale: The Rise of Context Engineering

    Most enterprise AI projects don't fail on the model. They fail on context. That's the line that stuck with me from my conversation with Geetesh Iyer at Data + AI by Databricks Summit on The Ravit Show, right after his talk on the rise of the AI Context Engineer!!!! The pattern he laid out is one a lot of data teams will recognize. Accuracy looks great in the pilot. Then you scale, the inputs get messy, and the answers start breaking down. People blame the model or the data. Geetesh makes the case that both are usually fine. What's missing is the context, which definition of revenue to trust, why a metric changed last quarter, how leaders actually read the numbers. That knowledge lives in people's heads, not in the system. His answer is a new role built from the analyst seat: the AI Context Engineer. The person who encodes that business context so AI can be trusted at scale. We got into: - What an AI Context Engineer actually is, and why the role is showing up now - Why accuracy holds in pilots but falls apart at scale - Why the model and the data usually aren't the problem - The four layers of enterprise context, and the one most companies miss - Whether the harder part is the skills or the organizational buy-in - The one thing a leader should do tomorrow if this hits home His framing for all of it: AI is the engine, context is the fuel. And the people best positioned to provide that fuel are already on your payroll. Full interview below. Worth a watch if you're trying to get AI analytics past the pilot stage. #data #ai #databricks #wisdom #theravitshow

  5. 10 Aug

    Why the Way You're Giving AI Agents Data Access Is Probably Wrong

    Data + AI Summit by Databricks is in full swing!!!! Just finished talking with Steven Touw, CTO at Immuta, on The Ravit Show, about one of the problems nobody is talking about yet but everybody will be talking about in six months. The problem: an AI agent needs access to data inside your Databricks lakehouse. What do most enterprises do right now? They plug in the agent with a user’s OAuth token. The agent inherits everything that user can access. Simple. Done. Here is what actually happens next: the agent now has a user’s full permissions. If the agent gets compromised, your data does too. If the agent runs a query you did not intend, it looks like that user ran it. If you need to revoke access, you have to revoke the whole user. The audit trail tells you a person did the work when a machine did it. Steve calls this the authentication-authorization gap for agents. Everyone is solving for “can the agent prove who it is” and ignoring “can we control what it actually does.” The alternative is what he calls “on behalf of” access. The agent can act on behalf of a user but does not inherit their full permissions. It gets a scoped token. It can only touch the specific tables and columns it needs. It can only do the operations it was designed to do. If it breaks, the damage is bounded. The audit log is honest. Revocation is surgical. This is not an Immuta problem. This is a security architecture problem that every company building production agents needs to solve right now. Watch the full conversation in the video below. This is the kind of problem that separates the companies shipping agents safely from the ones that are going to have a very bad incident next year. #data #ai #access #security #databricks #api #immuta #theravitshow

  6. 6 Aug

    The Enterprise AI Bottleneck Nobody's Talking About

    Everyone wants AI in production. Very few are talking about the biggest thing holding it back. I had a great conversation with Matthew Carroll, CEO and Co-Founder of Immuta, at the Databricks Data + AI Summit, and one message came through loud and clear: AI doesn’t scale if people can’t securely access the data they need!!!! We discussed why Immuta has evolved from being known as a data security company to focusing on data provisioning in the AI era. Some of the topics we covered: * Why many AI projects don’t fail because of the model, but because teams can’t get access to the right data at the right time * What it really means for an enterprise to become agent-ready * Why manual data access requests are becoming one of the biggest bottlenecks for AI adoption * And the practical steps data leaders can take today to move from slow approval processes to policy-driven access As more organizations move from AI pilots to production, conversations like these are becoming increasingly important. The full interview is now live. #data #ai #databricks #immuta #theravitshow

  7. 4 Aug

    Everyone Talks About AI Models. It's Time to Turn our Attention to Agent Memory and State

    I've done 750+ interviews on The Ravit Show. Everyone asks about models. Frameworks. Platforms. Almost nobody asks the question that actually decides whether an AI agent works: where does the memory live? So I sat down with Ed Huang, Co-Founder and CTO of TiDB, powered by PingCAP in Mountain View, and we went deep on the layer everyone is ignoring. A few things from this conversation that stuck with me: → "Memory is the surface, state is the system." Your agent remembering your name is memory. Your agent forgetting what it already tried three steps ago? That's a state failure — and it looks like a dumb agent, even on a frontier model. → Teams stitch together relational + vector + cache + sync pipelines. It works in the demo. It dies at scale. Ed breaks down why collapsing it into one distributed SQL engine matters beyond just "fewer parts." → The laptop-return story: an agent confidently answering from a 2023 policy doc. Better embeddings can't fix it. Ed explains why the retrieval accuracy gap is an architecture problem, not a model problem. → Manus runs 1.2M database clusters — and 99% were created by agents, not engineers. What breaks when your database's "user" is an agent instead of a DBA? Almost everything you assumed. → And the big one: three years out, when everyone has access to the same models, what do AI products actually compete on? Ed's answer — the most reliable memory wins, not the biggest model. If you're building agents, this is the conversation about the layer underneath everything else. Thank you, Ed, for the depth and honesty in this one. #data #ai #agenticai #TiDB #PingCAP #theravitshow

  8. 3 Aug

    How AtScale Extends Snowflake Semantic Views to Power BI and Excel

    AtScale's latest announcement with Snowflake highlights a reality many organizations are just beginning to realize: AI is only as smart as the business context behind it. That's where the Semantic Layer comes in. What do you think? That was one of the key takeaways from my conversation with Luis Maldonado, Chief Product Officer at AtScale, during Snowflake Summit on The Ravit Show. For years, organizations have struggled with a simple problem: different teams looking at the same data but arriving at different answers. Finance has one definition of revenue, sales has another, and operations has a third. The result is confusion, duplicated effort, and a lack of trust in analytics. The Semantic Layer changes that. It creates a common business language that sits between data and the people, applications, dashboards, and AI systems consuming it. Instead of every team building its own logic and calculations, everyone works from the same trusted definitions. What makes this particularly interesting is the collaboration between AtScale and Snowflake. As enterprises move beyond dashboards and into AI-powered decision making, trusted business context becomes critical. AI systems need more than data. They need to understand what that data actually means. The message from AtScale was clear: the future is not just about storing and processing data. It's about ensuring consistent business definitions across Power BI, Excel, analytics platforms, and AI applications. As AI adoption accelerates, I believe we'll hear a lot more about Semantic Layers. They may very well become the foundation that helps organizations move from AI experiments to trusted AI outcomes. #Data #AI #SnowflakeSummit #Snowflake #AtScale#DataAI #EnterpriseAI #AgenticAI #Analytics #TheRavitShow

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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
    2022 - 2026
  • Episodes
    585
  • Rating
    Explicit
  • Copyright
    © Ravit Jain
  • Show Website
    The Ravit Show

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