The Ravit Show

AI without observability is guesswork.I har a blast chatting with Patrick Lin, SVP and GM of Observability at Splunk on The Ravit Show. We get straight into how teams keep AI reliable and how leaders turn telemetry into business results.What we cover: • .conf25 updates in Splunk Observability • AI Agentic Monitoring and AI Infrastructure Monitoring • How a unified experience with Splunk AppDynamics and Splunk Observability Cloud helps teams ship faster with fewer surprises • Why observability is now a growth lever, not just a safety net • Fresh insights from the State of Observability 2025 reportMy take: • The nervous system of AI is observability • Signal quality beats signal volume • OpenTelemetry works best when tied to business context • When SecOps and Observability work together, incidents become learning momentsIf you care about reliable AI, faster recovery, and clear impact on productivity and revenue, this one will help.#data #ai #conf2025 #splunk #splunkconf25 #SplunkSponsored #theravitshow

I sat down with Jeff Baxter, NetApp to discuss about the announcements from INSIGHT to go deep on the new announcements today and why they matter for teams building with AI.We covered what this means in practice for customers. The NetApp AI Data Engine moves data to the right place at the right time, manages metadata, lineage, and versioning so work is reproducible, and adds AI powered ransomware detection so teams can ship with confidence. It runs as one platform across on premises and all major clouds, so hybrid stays simple and cost aware.Highlights from our conversation:• From data to innovation. A single data platform that reduces handoffs and cuts wait time for data scientists and engineers.• Reproducible AI by design. Metadata, lineage, and versioning are first class so you can rerun, compare, and promote models with clarity.• Security that keeps pace with AI. AI powered ransomware detection plus built in controls to protect sensitive data without slowing teams down.• One control plane. On prem and across major clouds with consistent operations, cost visibility, and policy enforcement.My take:• This is about operational discipline, not hype. Reproducibility and lineage are the difference between a demo and a dependable AI program• Security has to be native to the data platform. If it is bolted on later, teams hesitate and AI work stalls• Hybrid is the real world. A unified approach across on prem and clouds reduces complexity and keeps options openIf you are scaling AI and want fewer blockers between data and outcomes, this will help.“Explore NetApp AI solutions” AI solutions page: https://www.netapp.com/artificial-intelligence/?utm_campaign=cross-aiml-multi-all-ww-digi-spp-ravit_/_baxter_yt_interview-1760561150310&utm_source=youtube&utm_medium=video&utm_content=video#data #ai #insight2025 #netapp #theravitshow

Ransomware is getting faster. Your recovery needs to be faster.I had a blast chatting with Ryan Howard from BMC Software AMI Security on building cyber resiliency for mainframe environments. Simple, practical, and focused on what actually works in the real world.What we covered:* What cyber resiliency really means for mainframes* The core controls that matter: prevention, detection, isolation, recovery* Common blockers teams hit when rolling out new controls* A real incident walkthrough and how recovery stayed on trackWho should watch:* Security leaders who own mainframe risk* Infra and ops teams running mission-critical workloads* Anyone tightening their ransomware playbookWatch the interview now and share it with your team!!!!#cyberresilience #ransomware #mainframe #security #incidentresponse #bmc #theravitshow

What happens when AI agents become your teammates on the mainframe?I sat down with Anthony DiStauro from BMC on The Ravit Show to explore how agentic AI is moving from hype to real work. We unpacked the building blocks, the use cases, and what this shift means for teams who keep mission-critical systems running.Highlights we covered:- The teammate you didn’t hire: where agents plug in first across monitoring, remediation, change checks, and capacity tuning.- The basics in plain English: AI Agents, Agentic Workflows, and MCP Servers, and how they connect to form an execution layer that can act, not just alert.- Why now: falling hype, rising adoption as teams want safer automation with clear guardrails.- From mundane to strategic: operators focusing on performance engineering, cost optimization, and resilience design while agents handle the repetitive loops.- Capturing know-how: using agents to encode runbooks, tacit fixes, and tribal knowledge so it survives turnover.- Five-year picture: proactive, self-healing mainframes where agents predict drift, test changes, and roll back safely.- Humans + agents: trust comes from transparency, audit trails, and clear handoffs.- Invisible infrastructure: agentic workflows that hum in the background and surface only when needed.- From dashboards to decisions: moving beyond graphs to actions with approval gates for high-risk steps.- Future talent: a shorter learning curve for newcomers, making mainframe roles more attractive.If you care about reliability, cost, and speed on the mainframe, this is the next chapter.#data #ai #mainframe #bmc #theravitshow

What if your mainframe could talk back and guide the fix? I spoke to Liat Sokolov, Product Manager for AI solutions and an AI Evangelist at BMC Software. We explored how teams move from manuals and dashboards to real conversations with the platform. Liat breaks down why “guided resolution” beats “just answers” by giving the next right step in context, cutting time to recovery and reducing errors.We dug into the GenAI knowledge expert that keeps hard-won expertise in house as veterans retire. It becomes a coach for new developers and operators, helping them ramp faster and avoid costly mistakes.We also separated conversational AI from AI Agents and showed why enterprises need both. Picture agents coordinating across dev, ops, and cloud to roll out a change with checks, traceability, and rollback. That is how you modernize with confidence. Liat also explained why the mainframe can be the most explainable platform in the AI era, which matters for trust and safety.We finished with a practical path forward. Start with one high-value workflow, capture the expert playbook, pilot a conversational assistant with guardrails, then add agents as you prove value.If you care about making the mainframe simpler, safer, and faster, this interview is worth your time.#data #ai #mainframe #bmc #theravitshow

What happens when enterprise-grade AI visualization meets on-prem reality? I sat down with Leo Brunnick, Chief Product Officer at Cloudera on The Ravit Show, to talk about a major shift: bringing Cloudera Data Visualization to on-prem environments.We got deep into:-- Why now? What pushed Cloudera to extend this capability beyond the cloud-- How AI Visual and natural language querying are finally breaking barriers for non-technical users - right at the source-- The actual features that make this visualization layer powerful—not just dashboards, but intelligent, explainable insights-- Real business impact: we talked through use cases where organizations are solving high-stakes problems by giving their teams access to AI-powered visualizations on-prem-- And most importantly—where this is all headed. Leo shared a vision that includes GenAI, real-time visualization, and enabling large enterprises to move faster, smarter, and more transparently with their data-- The future of enterprise BI isn’t about choosing between cloud or on-prem. It’s about bringing AI to wherever the data livesIf you're navigating complex environments or looking to scale AI-driven insights inside the firewall, this conversation is worth your time.#data #dataviz #ai #cloudera #theravitshow

During GraphSummit London, Neo4j put $100M to become the default knowledge layer for agentic systems. I spoke with Sudhir Hasbe, President & Chief Product Officer at Neo4j to break it down.What we covered* The $100M push. Why Neo4j is betting on graph as the knowledge layer for agentic systems* Graph Intelligence. Turning disconnected data into explainable context your agents can trust* Aura Agent. Build, test, and deploy agents on your graph data in minutes. Early access now. GA in Q4* MCP Server for Neo4j. A cleaner path to add graph memory to the agents you already run* Use cases. Fast wins in healthcare R&D, procurement and supply chain, and financial operations* What’s next. GA timelines, first milestones, and how customers will measure impactWhy it mattersMost pilots fail without context and memory. Graphs give agents structure, reasoning, and traceability. That is how you ship production outcomes, not demos.#Neo4j #GraphSummit #GenAI #AgenticAI #GraphIntelligence #TheRavitShow

I had a blast at GraphSummit by Neo4j yesterday in London. I spoke to Michael Hunger on The Ravit Show and we went deep on Neo4j’s $100M GenAI push and what it means right now. Neo4j Aura Agent: Create Your Own GraphRAG Agent in Minutes — https://bit.ly/3KTjxlJWe discussed about these developments in the interview • How this investment helps teams move past stalled pilots and get real results on production data • Aura Agent explained in plain language, with the first two use cases to try for fast wins • MCP Server for Neo4j and how it lets existing agents plug into graph memory with natural language and text to query • What “default knowledge layer” looks like on day one, including how to keep results explainable and traceable • Timelines and signals to watch as Aura Agent and the MCP Server move to GA in Q4The interview is live now. If reliability, speed, and explainability are on your roadmap, you will find this useful.#data #ai #neo4j #graphs #theravitshow

I had a blast at Neo4j's GraphSummit in London. I also got a chance to speak with Jesús Barrasa, AI Field CTO about the following topics -- - Customer Use Cases, - Challenges Enterprise Leaders Face - New Book about Graphs and more#Neo4j #GraphSummit #Infinigraph #GenAI #AgenticAI #GraphIntelligence #TheRavitShow

AI at massive scale needs a graph engine that does not blink at 100 TB. Enter Infinigraph.I had a blast at GraphSummit, London, I spoke to Ivan Zoratti, VP Product Management, Neo4j to dig into the Infinigraph announcements and what they unlock for real workloads.What we covered* What Infinigraph is and who needs it now* Property sharding in plain terms. How data spreads across nodes without losing graph semantics* One engine for ops and analytics. How HTAP stays fast without starving either side* Migration path for current Neo4j users. What carries over and what to plan for* Where it shines at 100 TB and up. Boundaries, guardrails, and real results on speed and cost* Timelines to ship and what this means for agentic AI nextWhy it mattersBigger graphs with lower latency change what agents can do. If you want real-time reasoning on live data, the storage and compute model must scale without falling apart.Watch if you care about scale, cost, and a clean path from today’s Neo4j to what is coming next.#Neo4j #GraphSummit #Infinigraph #GenAI #AgenticAI #GraphIntelligence #TheRavitShow