Most companies think they have a backup problem. What they really have is a recovery problem. I spent the day at Commvault SHIFT, on the ground talking to customers, partners and the Commvault team. I also spoke with Darren Thomson, Field CTO at Commvault, for a focused chat on what they announced and what it means in the middle of real attacks and real pressure on IT teams.

In the interview, we got into:

-- The new Unity Platform and why Commvault is trying to bring data security, data access, identity patterns and cyber recovery into one view

-- How they are thinking about very fast, clean recovery after an attack, not just “we have backups somewhere”

-- Cloud Native Protection and what that actually changes for existing customers and large enterprises already deep into cloud

-- Identity Resilience and why identity is now part of the data security story, not a separate topic

-- Synthetic Recovery and how they use ML signals from anomalies, entropy, threat scans and third party tools to compose a clean state across multiple backup points

-- Real world use cases from regulated industries, where downtime and data loss are simply not an option

What I liked most is that this was not only about shiny new features, but about how security, backup and AI are starting to come together in one place. For teams who are tired of stitching ten tools to get back up after an attack, this direction will be interesting to watch.

If you care about data, security and recovery, this is worth your time.

Check out all the announcements links in the comments!

#data #ai #cloud #security #cybersecurity #recovery #resilience #commvault #shift2025 #shift #theravitshow

From SHIFT by Commvault New York, I sat with Christopher Mierzwa on culture, clarity, and execution!!!!

What you will get

• Real takeaways from his panel

• Why people, mindset, and culture decide security outcomes

• Practical advice for leaders, CISOs, and CIOs

Highlights

• Culture beats tools when pressure hits. If teams do not trust each other, runbooks stall.

• Mindset sets the tone. Treat incidents as system problems, not hero moments.

• Practice builds confidence. Short drills with clear ownership move every metric that matters.

Advice from Chris

• Start with people. Define roles, practice handoffs, review the tape after every drill.

• Build muscle memory. Run small, frequent exercises across IT, SecOps, and the business.

• Keep the board close. Explain risk in plain language and track progress like product work.

My take

Security is a team sport. The best programs invest in culture first, then controls.

#data #ai #cloud #security #cybersecurity #recovery #resilience #commvault #shift2025 #shift #theravitshow

Stop chasing tools. Start fixing decisions. I spoke to Stephen Sciortino, CEO and Founder of Database Tycoon LLC, at Small Data SF by MotherDuck. Clear takeaways for anyone running or advising a data team.What we covered• The real shift from his Brooklyn Data days to independent consulting• Early signals a team will win vs signs they are in trouble• How AI is changing expectations and what must stay the sameWatch the complete interview! Practical, direct, and worth your time.#Data #AI #SmallDataSF #DataEngineering #AI #Analytics #TheRavitShow

Real-time data is no longer a future problem. At Small Data SF by MotherDuck, I sat down with David Yaffe, Co-Founder & CEO at Estuary, to talk about what has changed in the world of data streaming!!!!

A few years ago, real-time data was something most teams put on their “later” list. Expensive. Hard to scale. Too complex for most use cases.

But as David shared, that story has shifted fast.

Here are some takeaways from our conversation:

- Streaming is now viable for everyone

With cheaper compute, mature tooling, and simpler developer experiences, real-time data isn’t a luxury anymore. The barriers that once made it a niche capability are gone

- Batch vs Real-time: Asking the right questions

Before jumping to streaming, David suggests asking what problems you’re solving — speed for the sake of speed rarely pays off. Sometimes batch is just fine. The goal is fit, not flash

- Architecture matters

Moving from batch to streaming means thinking end-to-end: from schema evolution and error handling to observability. Teams that skip this planning end up redoing pipelines

- CDC done right

Change Data Capture is powerful, but it’s easy to misuse. The most common mistake? Treating CDC as an ETL replacement rather than an event system. Understanding that difference prevents pain later

- The conversation was practical, focused, and refreshing.

Real-time isn’t about chasing trends, it’s about enabling faster insights and cleaner data movement with less friction.

If you’ve been wondering when “real-time” becomes realistic, this one will give you a clear answer.

#data #ai #motherduck #smalldatasf #theravitshow

Small Data. Real outcomes. I covered MotherDuck’s Small Data SF in person and spoke to my long-time connection with Colin Zima, CEO of Omni, to cut through the AI noise in BI. We focused on what actually moves the needle for teams today.

Here is what we got into:

• Where AI is creating real BI value

Practical wins that ship now, not hype cycles

• Flexibility vs governance

How Omni gives analysts room to explore while keeping the shared truth intact

• Why build a modeling layer

What Omni’s own model unlocks for speed, trust, and how far AI can go

• Embedded analytics after the Explo acquisition

When it makes sense to put live insights inside your product and what to avoid

• Simple over clever

Ways AI can remove clicks, clean up metrics, and make BI easier to use

• Common mistake with AI in dashboards

Teams bolt on features before they fix definitions and owners

• The agent future

If agents run dashboards tomorrow, why export to Excel might still matter

If you care about getting answers faster with clear guardrails, you will like this one.

#data #ai #motherduck #smalldatasf #theravitshow

Small Data is having a big moment!!!! I covered Small Data SF by MotherDuck in person and sat down with Brittany Bafandeh, CEO at Data Culture. We talked about the real blockers to impact and how teams can move faster with the data they already have.

Here is what we got into:

When it is not a data problem -

Brittany walked through a case where dashboards, pipelines, and new tools were not the fix. The real issue was slow decisions and unclear ownership. Once they set decision rights and clear KPIs, outcomes changed without buying more tech.

Do you have a data culture or just tools -

As a consultant, she looks for simple signals. Are decisions tied to metrics. Do teams review outcomes every week. Are definitions shared. If the answer is no, that is an infrastructure shell without culture inside it.

Consultant vs in house -

Consultants can push for focus and bring patterns from many teams. In house leaders win by staying close to the business and building habits that last. The best results happen when both mindsets meet.

One modeling habit that breaks things -

Teams jump to complex models too soon. Brittany’s fix is to model around decisions first. Keep names, metrics, and grain simple. Let complexity come only when the use case proves it.

Why this matters

Most teams do not need more tools to get value. They need faster decisions, shared language, and simple models that match the business. Small data, used well, beats big stacks used poorly!!!!

I am publishing the full interview next. If you care about real outcomes with the stack you already have, you will like this one.

#data #ai #motherduck #smalldatasf #theravitshow

Most companies say they are “doing AI.” Very few are actually ready for it. In my new episode of The Ravit Show, I sat down with Simon Miceli, Managing Director, Cisco, who leads Cloud and AI Infrastructure across Asia Pacific, Japan, and Greater China. He sits right where big AI ambitions meet the hard reality of networks, security, and technical debt.This conversation builds on my earlier episode with Jeetu Patel, President and CPO Cisco and goes deeper into what it really takes to get AI working in production in APJC.Here are a few themes we unpacked:-- Only a small group is truly AI ready- Cisco’s latest AI Readiness Index shows that just a small percentage of organizations globally are able to turn AI into real business value. Cisco calls them “Pacesetters.”- They are not just running pilots. They have use cases in production and are seeing returns.-- We are entering the agentic phase of AI- Simon talked about how we are moving from simple chatbots to AI agents that can take action.- That shift changes everything for infrastructure.- Instead of short bursts of activity, you now have systems that are always working in the background, automating processes and touching real operations.-- AI infrastructure debt is the new technical debt*- Many organizations are carrying years of compromises in their networks, data centers, and security.- Simon called this “AI infrastructure debt” and described how it quietly slows down innovation, increases costs, and makes it harder to scale AI safely.-- Network as a foundation, not an afterthought- One of his strongest points: leaders often think first about compute, but the companies that are ahead treat the network as the base layer for AI.- When workloads double, your network can become the bottleneck before your GPUs do. - The Pacesetters are already investing to make their networks “AI ready” and integrating AI deeply with both network and cloud.Three things leaders must fix in the next 2–3 yearsSimon shared a very clear checklist for CIOs and business leaders who are serious about agentic AI: 1. Solve for power before it becomes a constraint 2. Treat deployment as day one and keep optimizing models after they go live 3. Build security into the infrastructure from the start so it accelerates innovation instead of blocking itThis was a very honest, no-nonsense view of where APJC really stands on AI, and what the leading organizations are doing differently!!!!Thank you Simon for joining me and sharing how Cisco is thinking about AI infrastructure across the region.#data #ai #cisco #CiscoLiveAPJC #Sponsored #CiscoPartner #TheRavitShow

These are some of the most exciting times to be in AI. And some leaders are not just watching the shift. They are building it. Excited to share, I sat down with Jeetu Patel, President and Chief Product Officer at Cisco, for a conversation I have been wanting to do for a long time. Cisco is right in the middle of AI, networking, security, and data, and this interview felt like a front row seat to how the next decade is being shaped.

In this episode of The Ravit Show, we spoke about:

- The key AI trends Jeetu is seeing right now and how he explains Cisco’s AI vision

- What being at the intersection of networking, security, and data allows Cisco to do with AI that most pure AI companies cannot

- How AI adoption in Asia Pacific, Japan, Greater China, and India looks different from North America and Europe

- Why India is so important to Cisco, both as a market and as a serious talent hub

- The early career moments that still shape how he leads today

- The one piece of career advice he wishes someone had given him at 25, for everyone starting out in India and across APJC

For me, this was part AI roadmap, part masterclass in leadership at global scale. You can feel how seriously he takes this moment and the responsibility that comes with it.

If you care about AI, infrastructure, or building your career in this space, this is one you will want to watch.

#data #ai #cisco #CiscoLiveAPJC #Sponsored #CiscoPartner #TheRavitShow

Gartner has named K2view a Visionary in the 2025 Magic Quadrant for Data Integration Tools, and they have moved up inside the Visionary quadrant. This is a big signal for anyone who cares about data and AI in the enterprise.I had to cover this news in person and what better place than my friend, Ronen Schwartz’s home in Palo Alto, talking to him about what this actually means. We did not just speak about a report. We spoke about whether data integration still matters in an AI world and why K2View’s approach is getting attention right now.Here is how I see it.- First, data integration is more relevant than ever. Your AI agents, copilots, and analytics are only as good as the data foundation behind them. K2View’s bet has been simple to understand. Give every business domain a clean, real time, governed view of its data, and make it available to any use case, including AI.- Second, the move up in the Visionary quadrant is about more than a label. It reflects how K2View is executing on this idea of “AI ready data,” not just talking about it. They are helping customers move away from scattered pipelines to a consistent way of delivering trustworthy data products into AI, analytics, and operations.- Third, when you compare their position with the large leaders, you see a different angle. The big platforms are broad. K2View is sharp and focused.They model data around real business entities, not just tablesThey support real time views without forcing you into one storage patternThey are designing with GenAI and agentic AI in mind from day oneFinally, the strategic outlook. Ronen is very clear that this is not about selling “yet another integration tool.” It is about being the data layer that lets enterprises move faster with AI while staying in control of privacy, compliance, and performance.For leaders who are serious about AI and tired of slideware, K2View’s move in the Magic Quadrant is one of those signals worth paying attention to.#data #ai #gartner #gartnermagicquadrant #agenticai #agents #k2view #theravitshow

Some conversations shift how you think about the future of AI. This one did. I just sat down with David Flynn, Founder and CEO of Hammerspace, to talk about something enterprises rarely discuss openly: the real engine behind AI is no longer compute. It is data.

We went deep into why NVIDIA’s AI Data Platform has become the blueprint for modern AI architecture and why Hammerspace is emerging as the layer that actually makes this blueprint real for enterprises.

David broke down how the industry is moving from building AI around compute to building AI around data.

He talked about what the AI Anywhere era looks like, and why the next generation of AI systems will need a global, unified view of data across cloud, edge, and physical environments.

We also talked about the partnership with NVIDIA, how it boosts the productivity of agentic AI, and why enterprises will need data that can move as fast as their models.

David shared how Hammerspace is preparing for what comes next in 2026 and beyond, from scale to power efficiency to open standards.

This is one of those conversations that gives you clarity on where the industry is going and why data architecture is about to become the biggest competitive advantage.

#data #ai #nvidia #hammerspace #gpu #enterprise #agenticai #theravitshow

AI doesn’t fail because of GPUs. It fails because of data.

I had a blast chatting with Jeff Echols, Vice President, AI and Strategic Partners at Hammerspace, from NVIDIA GTC in Washington. We talked about the part of AI nobody is fixing fast enough: getting data to GPUs at the speed the GPUs need it.

Jeff breaks down what makes the Hammerspace AI Data Platform different from traditional AI storage. This isn’t “more storage.” It’s orchestration. Move data globally. Feed it to the right workload. Keep GPUs busy instead of waiting.

We also got into MCP and why an intelligent data control layer is now core to any real AI strategy, plus how Hammerspace lines up with the NVIDIA AI Data Platform reference design so enterprises can actually run this in production, not just in a lab.

And we talked Tier 0. If you want GPU ROI, Tier 0 is about one thing: keep the GPUs fed at full speed.

If you’re trying to scale AI past a pilot, watch this.

#data #ai #nvidiagtc #nvidia #hammerspace #gpu #theravitshow

AI in the public sector isn’t a pilot anymore. It’s running in the real world. Check out my conversation from NVIDIA GTC in Washington with Robin Braun, VP AI Business Development, Hybrid Cloud at Hewlett Packard Enterprise, and Russell Forrest, Town Manager of Town of Vail. This one is important because it’s about AI for cities, not just AI for big tech. I had a blast interviewing both Robin and Russell.

We talked about how HPE is using AI to tackle real problems every city deals with: traffic, safety, and energy efficiency. Robin walked through how you build a smarter, more connected city by turning live data into decisions that actually help people on the ground.

Russell brought the city view from Vail. He explained what it takes to move from “we’re testing AI” to “we’re using this in operations.” We got into risk, cost, and how you deploy without adding complexity or slowing down public services.

We also discussed agentic AI. Not as a buzzword, but as something that can help a town react in real time while still keeping humans in control.

Better safety. Better visitor experience. Better use of resources. Same team.

This is AI as public service infrastructure.

Full interview is now live on LinkedIn and YouTube.

#data #ai #nvidiagtc #nvidia #hammerspace #gpu #storage #theravitshow

Public sector AI is moving fast. The big question is how to build it the right way.

I had a blast chatting with Andrew Wheeler, Hewlett Packard Enterprise Fellow and Vice President at Hewlett Packard Labs and HPC & AI Advanced Development at NVIDIA GTC in Washington, DC.

We talked about:

* How HPE helps agencies build and scale sovereign AI ecosystems

* Why the public sector is a core focus for HPE in AI

* Practical steps for data sovereignty, compliance, and security without slowing innovation

* Where sovereign AI shows up first: government, defense, citizen services, large-scale research

* How HPC and supercomputing power national-scale AI

* What quantum could unlock for government programs and where HPE fits

If you care about trusted AI for cities, states, and national labs, this one is worth a watch.

Full interview now live!!!!

#data #ai #hpe #nvidiagtc #gtc2025 #gpu #sovereign #nvidia #theravitshow

AI ROI is now the real test. I got a chance to chat Joe Corvaia, SVP Sales at DDN at NVIDIA GTC in Washington. This one is for CEOs and exec teams who are being pushed to “do AI” but still can’t show a return.

We started with a simple question. Why are some companies actually getting ROI from AI while others are still stuck in pilots. Joe was very direct on what separates the ones who are scaling from the ones who are still presenting slides.

We talked about infrastructure as a board-level strategy. Not just “buy more GPUs,” but “are you using the GPUs you already bought.” Joe walked through how data infrastructure and data flow have to be part of the conversation in the boardroom, not just in IT.

We got into AI factories and the new DDN Enterprise AI HyperPOD. Built with Supermicro and accelerated by NVIDIA, HyperPOD is designed to take teams from first deployment to serious scale. The idea is you should be able to stand up production AI without rebuilding the stack every time you grow the workload.

Joe also broke down why platforms like HyperPOD, and GPU-aware networking and acceleration like NVIDIA BlueField 4, are about more than performance. They are about efficiency. Max GPU utilization. No idle spend. Faster time to value. This matters not just for big tech, but for regulated industries and sovereign AI programs that need capability and control.

We closed on one topic that every CEO is thinking about right now. How do you future proof AI investments. Joe shared the one principle leaders should follow so they are not buying hardware for headlines, but building an AI foundation that still makes financial sense five years out.

If you own AI strategy, budget, or delivery, watch this.

#data #ai #nvidiagtc #nvidia #hammerspace #gpu #storage #theravitshow

I have seen a lot of AI demos this year. Very few make hard, messy work feel simple. Next week I am going live with Sarah McKenna, CEO of Sequentum, for an AI Magic Wand Launch Celebration on The Ravit Show.

What is happening -

We are going to walk through how Sequentum is using AI to change web data work. Not slides. Actual product.

Here is what we will get into during the show:

- AI Magic Wand (beta)

A new feature that turns high level intent into working web data flows. Think less trial and error, more “tell it what you want and refine.”

- Command Templates

How reusable templates help teams stop rebuilding the same patterns and start sharing what works across the company.

- New tools coming in the next weeks

Unblocker, Paginations and more. All focused on enhancing Sequentum’s data collection capabilities

- Latest in standards

The standards and good practices that matter if you want web data and AI that can stand up in an enterprise.

Why I am excited about this one

Most teams I meet are still stuck between scripts, manual fixes, and brittle tools when it comes to web data. Sequentum is trying to give them a cleaner path with AI on top. This session is about showing that work in public and talking through the real trade offs.

If you care about web data, automation, and using AI for real work, this will be a good one to watch.

Think Mumbai was electric. India’s AI build-out just moved into a higher gear.

I sat down with Sandip Patel, Managing Director, IBM India & South Asia at IBM’s Mumbai office. We unpack what Think Mumbai means for teams building with AI, hybrid cloud, and data at scale.

What stood out and why it matters:

IBM and airtel partnership

• Aim: give regulated industries a safe and fast path to run AI at scale

• How: combine Airtel’s cloud footprint and network with IBM’s hybrid cloud and watsonx stack

• Why it helps: data stays controlled and compliant while workloads flex across on-prem, cloud, and edge

• Impact areas: banking, insurance, public sector, large enterprises with strict governance

First cricket activation on watsonx

• What: AI-driven insights powering a live cricket experience

• Why it matters: shows real-time analytics, content, and decisioning are ready for prime time

• Enterprise takeaway: the same pattern applies to contact centers, fraud, supply chains, and field ops where seconds count

AI value for Indian enterprises today

• Start with governed data and clear ownership

• Use hybrid patterns so models run where the work and data live

• Blend predictive models with generative workflows inside watsonx for measurable lift

• Track outcomes in productivity, risk reduction, customer experience, and time to value

Skills as the force multiplier

• Priority skills: data governance, MLOps, orchestration, security on hybrid cloud

• Team model: small core teams operating a shared platform, federated use cases across business units

• Result: faster move from pilots to production with repeatable guardrails

My take

India is moving from talk to build. The blueprint is open, hybrid, and governed. Partnerships that keep control local while staying flexible will unlock scale. Sports gave us a sharp demo of real-time AI. The next wins will be in operations, customer journeys, and risk.

The interview is live now. Link to the complete interview in the comments!

#data #ai #agentic #ibm #ThinkMumbai #governance #cloud #watsonx #IBMPartner #theravitshow

Flink Forward Barcelona 2025 was not just about streaming. It was about what comes next for enterprise AI. I sat down with Qingsheng Ren, Team Lead, Flink Connectors & Catalogs at Ververica, and Xintong Song, Staff Software Engineer at Alibaba Cloud, to talk about something that could change how enterprises build AI systems in production: Flink Agents.

Flink Agents is being introduced as an open source sub-project under Apache Flink. The goal is simple and ambitious at the same time: bring agentic AI into the same reliable, scalable, fault-tolerant world that already powers real-time data infrastructure.

We talked about why this matters.

First, why Flink Agents and why now?

They walked me through the motivation. Most AI agent frameworks today look exciting in a demo, but they break once you try to run them against live data, streaming events, strict SLAs, audit requirements, cost pressure, and real users. There’s a big gap between prototypes and reliable operations. That’s the gap Flink Agents is aiming to close.

Why open source?

Both Ververica and Alibaba made it clear that this is not meant to be a proprietary, closed feature. They want this to be a community effort under Apache Flink, not a vendor lock-in story. The belief is that enterprises will only bet on AI agents at scale if the runtime is open, portable, and battle tested.

How is building an AI agent different from building a normal Flink job?

This part was interesting. A standard Flink job processes streams. An agent has to do more. It has to reason, take actions, call tools, maintain context, react to feedback, and keep doing that continuously. You’re not just transforming data. You’re orchestrating behavior. Flink Agents is meant to give you those building blocks on top of Flink instead of forcing teams to stitch this together themselves.

What kind of companies is this for?

We got into enterprise workloads that actually need this. Think about environments where fast decisions matter and you can’t afford to go offline:

-- Fraud detection and response

-- Customer support and workflow automation

-- Operational monitoring, alert triage, and remediation

-- Real-time personalization and recommendations

-- Anywhere you need an autonomous loop, not just a dashboard

-- And finally, roadmap.

We talked about the next 2 to 3 years. The focus is on deeper runtime primitives for agent behavior, cleaner developer experience, and patterns that large enterprises can trust and repeat.

My takeaway:

Flink Agents is not just “yet another agent framework.” It’s an attempt to operationalize agentic AI on top of a streaming backbone that already runs at massive scale in production.

This is the conversation every enterprise AI team needs to be having right now.

#FlinkForward #Ververica #Streaming #RealTime #DataEngineering #AI #TheRavitShow

Real time is getting simpler. At Flink Forward, I sat down with Josep Prat, Director, Aiven. We discussed about Aiven and Ververica | Original creators of Apache Flink® new partnership and what it unlocks for data teams!!!!

What we covered:

• Why this partnership makes sense now and the outcomes it targets

• Fastest ROI use cases for joint customers

• How Aiven and Ververica split support, SLAs, and upgrades

• The first deployment patterns teams should try. POCs, phased rollouts, or full cutovers

• Support for AI projects that need fresh data with low latency

• What is coming next on the shared roadmap over the next two quarters

If you care about streaming in production and a cleaner path to value, this one is worth a watch.

Full interview now live!!!!

#data #ai #streaming #Flink #Aiven #Ververica #realtimestreaming #theravitshow

Flink Forward Barcelona 2025 was a big week for streaming and the streamhouse.

I sat down with Jark Wu, Staff Software Engineer at Alibaba Cloud, and Giannis Polyzos, Staff Streaming Architect at Ververica, to talk about Apache Fluss and what is coming next.

First, a quick primer. Fluss is built for real-time data at scale. It sits cleanly in the broader ecosystem, connects to the tools teams already use, and focuses on predictable performance and simple operations.

What stood out in our chat:

• Enterprise features that matter

Security, durability, and consistent throughput. Cleaner ops, stronger governance, and a smoother path from POC to production.

• Zero-state analytics

They walked me through how Fluss cuts network hops and lowers latency. Less shuffling. Faster results. More efficient pipelines.

• Fluss 0.8 highlights

Better developer experience, more stable primitives, and upgrades that help teams standardize on one streaming backbone.

• AI-ready direction

Vendors are shifting to AI. Fluss is adapting with functions that support agents, retrieval, and low-latency model workflows without bolting on complexity.

• Streamhouse alignment

The new capabilities strengthen Fluss in a streamhouse architecture. One place to handle fast ingest, storage, and analytics so teams do not stitch together five systems.

We also covered the roadmap. Expect continued work on latency, cost control, and easier day-two operations, plus patterns that large teams can repeat with confidence.

Want to get involved

Join the community, review the open issues, try the latest builds, and share feedback from real workloads. That is how this moves forward.

The full conversation with Jark and Giannis is live now on The Ravit Show.

#data #ai #FlinkForward #Flink #Streaming #Ververica #TheRavitShow

How does PlayStation run real time at massive scale. I sat down with Bahar Pattarkine from PlayStation the team to unpack how they use Apache Flink across telemetry and player experiences.

What we covered:

-- Why they chose Flink and what problem it solved first

-- Running 15,000+ events per second, launch peaks, regional latency SLOs, and avoiding hot partitions across titles

-- Phasing the move from Kafka consumers to a unified Flink pipeline without double processing during cutover

-- How checkpointing and async I/O keep latency low during spikes or failures

-- Privacy controls and regional rules enforced in real time

-- What Flink simplified in their pipelines and the impact on cost and ops

#data #ai #streaming #Flink #Playstation #Ververica #realtimestreaming #theravitshow