The Ravit Show

Host of "The Ravit Show" I love to interview folks on my LinkedIn and YouTube Live. Anything and everything that's trending can be a topic on the show.

Founder and Host

Gartner

Gartner Ambassadors are active leaders who are eager to drive the discussion, share their knowledge, make new connections, and build their personal brand.

Gartner Ambassador

EM360

Board Advisor

AtScale

I look after social media, content, podcast production and community!

Head Community Evangelist

Story by Data

Affiliate Member

Open Data Science Conference (ODSC)

Packt

Senior Content Marketing Editor

As a Content Marketing Editor in the Expert Network team and look after content marketing materials produced by and for Expert Network authors and their titles. I am responsible for a portfolio of content marketing assets for each title, liaising with Producers, Technical Editors, and the wider EN team, and individual authors and technical reviewers as necessary. I am also responsible for liaising with other teams in the business to make appropriate marketing content available, on a title by title basis, as requirements arise, and sourcing further material where there are gaps.

- Create a repository of content marketing assets for each title, visible and available to other stakeholders
- Maintain and develop further content marketing assets as required in support of the individual title marketing for each EN author
- Ensure the completeness, accuracy, and availability of all promotional title information and materials for Expert Network titles.
- Create and maintain the Expert Network forthcoming catalogue to share with internal and external partners.
- Create, source, and curate content for the Outreach team, PTN team, Direct marketing team, and social channels.

Responsibilities:

I work with the Producer and Technical Editor of a title, and other team stakeholders, to pre-emptively produce or curate the following assets for each title across the lifecycle of the project:

- Content articles for the Hub and other partner channels in phases right from pre-pub to post-pub.
- Generate podcasts for authors to talk about the topic and promote the book.
- Providing a suggested social content plan across Expert Network titles, for integration and execution by the Packt social team
- An early pre-release extract, a “sneak preview” in the month before launch, and a highly shareable article immediately post-launch.
- An article based on an author interview with questions provided by the editor.
- The content and images for an Amazon A+ page, based on the author interview.

Content Marketing Editor

- Primary and secondary research on new development in the field of tech
- Getting insights from an industry expert on your projects to give an edge and value to your project making it unique
- Preparing questionnaires for the authors and the clients on our product line
- In-depth market forecasting and analysis of various sectors
- Creating roadmaps while working closely with multiple stakeholders on different projects in accordance with the TAT
- Prepared market engineering sheets at regular intervals that help you visualize complex data via MS Office Tools
- Internal and external stakeholder management including presentation to CXOs
- Understanding the business need, evaluating various processes and controls present in the organization, communicating control deficiencies and other issues encountered during the research process
- Keeping a tap on the commercial viability of the products

Associate Category Manager

S K Patodia & Associates

- Look after different domains of the business with accordance to the clients
- Managing team and preparing questionnaires for the training of research
- Handling accounts and budgets related to the projects
- In-depth primary research related to the projects
- Handling clients PAN India, while closely associating with internal stakeholders
- Forecast new areas in terms of commercial viability

Assistant Manager Research 

Hexa Research Pvt. Ltd.

- Prepare reports on different domains such as media, ICT, Chemical, Tech, FMCG and consumer insights
- Handling client reports while closely working with different internal and external stakeholders 
- To prepare reports with accordance with the client requirements from across the United States, Europe, APAC, Middle East
- Market forecasting considering the factors affecting the industries by Primary Research
- Understand the budget aspects as per the client's requirement
- Training the team and finishing the projects in given turn around time

Research Associate

Reliance Industries Limited

Working for Indirect Taxation on CENVAT credit input.

Finance Intern

Founder & Host of "The Ravit Show". I love to interview Data/AI Leaders on The Ravit Show. Hosted 500+ Data Science/AI leaders on The Ravit Show.

Ravit Jain is well known for --

- Hosting live events
- Data Community Building
- Community Evangelism
- Product Marketer
- Share free resources with Community
- Helping Data Science/AI Community

As the Founder & Host of The Ravit Show, my mission is to illuminate the intersection of media and technology by engaging with Data and AI leaders. With a focus on new business development and compelling communication, I've established a platform that resonates with industry professionals and enthusiasts alike.

I leverage my expertise in B2B influence to foster thought leadership and community building. Our team is dedicated to sharing knowledge, driving conversations, and creating meaningful connections within the tech community, with a special emphasis on the burgeoning fields of Data and AI.

You can reach out to me at Ravitjain0909@gmail.com for The Ravit Show queries :)

Agents are here. Governance decides who wins.

I just published my interview with my friend Marinela Profi from SAS. Marinela breaks down agentic AI in a way leaders can use today. Clear. Practical. Actionable.

What we covered:
- What makes agentic AI different and why enterprises should care
- Autonomy levels, decisioning, orchestration, and the human-AI balance
- Where teams go wrong: hype vs readiness, data maturity, governance, and missing orchestration
- Real use cases on Viya across banking, insurance, and manufacturing
- Why “LLMs are not agents” and how to combine deterministic and probabilistic methods with governance
- What a CIO or CDO should do now to move from pilots to production

Marinela is sharp and grounded. She touched the important points and kept it real for enterprise teams.

The interview is live. Watch it and share your takeaways.

Ravit Jain

Founder & Host of "The Ravit Show". I love to interview Data/AI Leaders on The Ravit Show. Hosted 155+ Data Science/AI company leaders on The Ravit Show.

Ravit Jain is well known for --

- Hosting live events
- Data Community Building
- Community Evangelism
- Product Marketer
- Share FREE resources with Community
- Helping Data Science/AI Community

You can reach out to me at Ravitjain0909@gmail.com for The Ravit Show queries :)

Founder & Host of "The Ravit Show" | Influencer & Creator | LinkedIn Top Voice | Startups Advisor | Gartner Ambassador | Data & AI Community Builder | Influencer Marketing B2B | Marketing & Media | (Mumbai/San Francisco)

If you are building AI agents, this is the most practical guide I have seen in a while.

Most teams hit the same wall. The demo looks great. Then users push the product and everything breaks. Retrieval is noisy. Memory is fragile. Latency creeps up. The MongoDB Atlas guide on AI Agents shows a cleaner path: keep your documents, vectors, full-text search, and agent memory in one place, wire it to your orchestration, and ship a reliable agent that learns over time.

Read it here: https://fandf.co/3KZBBLe

What stood out for me:
• Agentic RAG done right
Blend semantic and keyword signals. Pre-filter by metadata. Rank what matters. This is how you move from “it works sometimes” to “it works daily.”

• Real agent memory
Store long-lived context next to your app data. No extra service to manage. You get consistent state across sessions, not just a short chat history.

• Tool use with guardrails
Index the right content. Define tools that are actually useful. Log what the agent retrieves and why. You can debug, test, and improve instead of guessing.

• Latency and scale without glue code
One platform for vectors, text search, filters, and write-backs. Fewer hops. Cleaner ops. Better p95s when traffic spikes.

• Patterns you can copy this week
Data modeling for documents and embeddings. How to choose chunking and indexing. How to fuse scores. How to evaluate relevance before you scale.

For founders and product teams, this is the difference between a flashy demo and a product people trust. When your agent can retrieve the right facts, remember the right details, and act with the right tools, you get real usage, not just clicks.

A simple way to start:

1. Load a real slice of your customer content into Atlas
2. Create a Vector Search index and add the fields you care about for pre-filters
3. Turn on full-text search and enable hybrid ranking
4. Persist agent memory in the same database as your app data
5. Add evaluation early. Track relevance, latency, and failure modes from day one

If you want your agent to move from “interesting” to “indispensable,” this guide is worth your time.
Read it here: https://fandf.co/3KZBBLe

Thank you to MongoDB for this collaboration!

#MongoDB #Atlas #AI #VectorSearch #RAG #AgenticAI #Startups #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 | Original creators of Apache Flink®, 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. 

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.

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

Live from POSSIBLE by Teradata with Sumeet Arora, Chief Product Officer. had a blast chatting with him about five things: Trends. New announcements. Data and AI trends inside the enterprise. Real use cases. What is next for the industry.

New announcements -- 

- Autonomous Customer Intelligence. Turning customer signals into timely actions across the journey.
- NewtonX research on AI for customer experience. Where leaders are investing and where gaps still exist.

Trends in Data and AI

- Moving from pilots to production with tighter links between trusted data and AI.
- Clear governance and cost control built in from the start.

Use cases

- Retention and growth with real-time signals and simple next best actions.
- Smarter personalization without copying data all over the place.

Future

- Faster paths from idea to impact.
- Smaller, focused teams shipping measurable outcomes.

#data #ai #possible2025 #teradata #theravitshow

We did it again!!!! BREAKING from NVIDIA GTC DC. I’m on the ground in Washington, D.C., covering it as Content Creator/Press. Always a thrill to meet Jensen Huang, Founder & CEO, NVIDIA in person, the rockstar of the tech world.

What stood out to me -- 

- AI Factory blueprint Omniverse DSX gives teams a practical recipe to design, simulate, and run large AI factories. Facilities, power, cooling, hardware, networking, scheduling, and safety modeled together so leaders can plan output and cost with confidence.

- Networking built for trillion-token scale
BlueField-4 DPUs and ConnectX-9 SuperNICs push 800 Gb/s so training, inference, storage, and security keep pace under real multi-tenant loads. Less bottleneck. More consistent throughput.

- Open models and open data
 New open models and curated datasets across language, biology, and robotics reduce day-zero friction. Builders can fine tune and deploy faster without starting from scratch.

- Quantum meets GPU with NVQLink
A hardware and software path to link quantum processors to GPU clusters. Hybrid quantum-classical pipelines become practical for teams that need it, without bespoke integration work.

- Aerial open sourced
Researchers and operators can experiment with AI-native 6G concepts faster. Open components help align academia, startups, and carriers around common building blocks.

- Physical AI at the edge
IGX Thor targets factories and hospitals where safety and real-time decisions matter. Think inspection, robotics, imaging, and clinical devices that need deterministic performance at the edge.

- Robotaxi momentum
NVIDIA and Uber outlined steps toward a Level-4 robotaxi network with a clear hardware and software stack.

- Science and health acceleration
DOE’s planned AI supercomputer with Oracle targets cross-discipline discovery. Lilly is standing up a pharma AI factory on Blackwell to compress timelines in drug development.

- AI Physics for engineering
GPU-accelerated physics and microservices shorten design loops in aerospace and automotive. Faster iteration means better products with fewer prototypes.

- Partners and infrastructure at American scale
A broad push with vendors and states to stand up reliable AI infrastructure, supply chains, and workforce. This is how you go from pilots to plants.

Why this matters --
We are moving from scattered AI projects to a single, integrated product that enterprises can operate. Compute, networking, simulation, data, safety, and operations now ship as one stack. That means faster R&D cycles, stronger supply chains, and real productivity in the physical world.

My take -- 
This is the most complete playbook I have seen for national-scale AI. Blueprints you can follow. Open pieces you can extend. Sector paths you can start now in healthcare, telecom, mobility, manufacturing, and public sector. 

I will share my on-site interviews and deeper breakdowns through the week!!!!

#data #ai #nvidiagtcdc #nvidiagtc NVIDIA AI #nvidia #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 track

Who should watch:
• Security leaders who own mainframe risk
• Infra and ops teams running mission-critical workloads
• Anyone tightening their ransomware playbook

Watch the interview now and share it with your team!!!!

#cyberresilience #ransomware #mainframe #security #incidentresponse #bmc #theravitshow

BREAKING: Live from Flink Forward Barcelona 2025. The keynote made one thing clear. Real-time AI is ready for production. Here is what was announced and why it matters. I am excited to share my take below -- 

- Flink Agents collaboration
Alibaba Cloud, Ververica | Original creators of Apache Flink®, Confluent, and LinkedIn are joining forces to bring agent building blocks into Apache Flink. Agents can read events, reason, call tools, and take actions on live data. The standout detail is action consistency tied to Flink checkpoints so side effects happen once even under failures. Built for Python and Java with LLM and vector store integrations. This is aimed at high-scale, low-latency use cases.

- VERA-X engine
A native vectorized runtime for Flink that keeps your APIs the same but speeds up execution under the hood. The team showed single-digit millisecond latencies on stateful jobs, higher throughput on SQL streaming benchmarks, and lower CPU use in tests. If you manage hot pipelines, this can cut both latency and cost without a rewrite.

- Apache Fluss in Ververica's Unified Streaming Data Platform
Fluss treats data as both a log and a table. You capture changes once and query them with sub-second freshness. It is positioned to serve features for ML so teams do not juggle separate online and offline stores. That reduces drift and simplifies pipelines.

- Ververica Platform 3.0
Platform 3.0 ties engine, storage, and AI together. Batch and streaming share one model. There are native hooks to use LLM and RAG patterns inside Flink SQL. The goal is to enrich streams with context and build assistants on fresh data without adding extra systems.

Why this is important?
Most AI projects slow down because data is late or the stack is complex. These updates push in the other direction. Faster runtime. Storage built for fresh reads. Agent patterns that respect exactly-once behavior. This is how you build applications that learn and act in the moment.

My take from the room --

If you run heavy state, benchmark one critical job on VERA-X and measure end-to-end latency, checkpoint time, and CPU per record. 

If you fight online versus offline feature drift, move one feature pipeline to Fluss and compare freshness. 

If you are exploring autonomous workflows, trial Flink Agents in a narrow path with a safe external action and watch how exactly-once plays out. Then look at Ververica Platform 3.0 as the surface to run all of this with fewer moving parts.

As usual great work by Ververica | Original creators of Apache Flink® Team and Open Source Contributors, 

Alexander Walden, 🐿️ Ben Gamble🧑🏾‍🦯, Karin Landers, Vladimir Jandreski, Giannis Polyzos, Jark Wu, Yuan Mei, Xintong Song, Qingsheng Ren and many more!!!!

Sharing all links to the announcement below!!!!

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

EXCITED!!!! Small Data SF: I will be on site covering it live. I did it last year and we will be there again this year. What a lineup!

I will be in San Francisco for Small Data SF, a two-day event focused on simple, local-first data and practical AI. I will be on the ground filming conversations and sharing takeaways in real time.

What I will cover

- Keynotes and talks on right-sized data and agent workflows
- Workshops that move from idea to working demo fast
- Panels that focus on real outcomes in production

5 reasons this will be amazing

- Builders on stage
You will learn from people who ship products every day. Expect hard lessons and clear patterns you can use.

- Actionable sessions
Talks will focus on performance, cost control, and smaller models that still deliver. You will leave with playbooks you can run.

- Small data mindset that scales
The program will show how simple architectures and local compute can cut waste and speed up delivery.

- Cross-stack perspective
Engineering, product, and research leaders will share what is working across the stack so you see the full picture.

- High-signal community
I will meet founders, engineers, and data leaders who care about doing the work. You will see fast takeaways from these conversations.

The speaker lineup is stacked. You will hear from Adi Polak, Barr Moses, Barry McCardel, Rebecca Bruggman, Benn Stancil, Brian Douglas, Colin Zima, Dennis Hume, elvis kahoro, George Fraser, Glauber Costa, Holden Karau, Jacob Matson, Joe Reis, Jordan Tigani, Ravin Kumar, Russell Garner, Sahil Gupta, Sam Alexander, Scott Haines, Shelby Heinecke, PhD, Tahlia DeMaio, Teague Sterling, Thierry Jean, Tristan Handy, Upal Saha, Zain Hasan, and Zulfikar Qureshi.

If you will be there, say hi. I will share live clips and a full recap after the event.

#data #ai #motherduck #smalldatasf #theravitshow

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 report

My 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 moments

If you care about reliable AI, faster recovery, and clear impact on productivity and revenue, this one will help.

Full interview below.

#data #ai #conf2025 #splunk #splunkconf25 #SplunkSponsored #theravitshow

#EVOLVE25 London was a clear signal that Big Data is entering its third era, and it is about outcomes, not buzzwords. I sat down with Sergio Gago, CTO, Cloudera and we went straight to the shift everyone is feeling. This era is defined by convergence. The work now is to bring on-prem and cloud together so teams can move fast, stay compliant, and keep costs in check. That is where the real AI wins will come from.

Here is what we covered in the interview I am publishing next:

- What truly defines the third era of Big Data and how it differs from the last decade
- Why convergence matters now for performance, cost, and control
- Where Cloudera wins today, and where it chooses not to compete
- How a unified data foundation raises trust in AI
- The new Cloudera + Dell “AI-in-a-box” approach for private, trusted AI
- A five-year view of on-prem, cloud, and AI working together
- Cloudera’s vision to support this shift end to end

If you care about building trustworthy AI on real enterprise data, this conversation will be useful. 

#data #ai #EVOLVE25 #cloudera #theravitshow

Real-time data isn’t just fast—it’s collaborative. At Data Streaming Summit 2025, I spoke with Rayees Pasha, CPO, RisingWave, a key partner of StreamNative, about how streaming ecosystems are evolving together instead of in silos. 

Rayess shared how RisingWave’s real-time database helps teams run complex analytics directly on live data, and how their partnership with StreamNative brings true interoperability to customers.

We discussed the future of streaming over the next year, where streaming meets AI, and intelligent pipelines start driving automated, data-driven actions. He also appreciated the summit’s open and multi-technology approach, which encourages collaboration rather than competition.

His favorite moments came from the AI + Stream Processing track, where discussions focused on turning streaming data into real-time intelligence.

This conversation captures how openness and partnership are powering the next wave of streaming innovation.

#data #ai #streamnative #datastreamingsummit #theravitshow

Stop guessing what “AI-ready data” means. Come see it working end to end.
I’m excited to join a live demo where the CData Software team shows how CData Sync turns scattered sources into reliable pipelines for analytics and AI. 

You’ll watch them connect to 400+ sources in minutes, standardize schemas, and use CDC for fast incremental loads that keep lakes and warehouses fresh. 

They’ll apply simple transformations to prep features for BI and ML, schedule and monitor runs so pipelines don’t break at 2 a.m., and refresh vector stores so RAG, search, and personalization stay current.

The value is clear: cleaner inputs lift model quality, consistent schemas cut debugging time, and fresh data speeds real use cases. You’ll leave with a step-by-step view to build and schedule pipelines, a short checklist to make data AI-ready, and practical tips to replace ad hoc glue code with governed flows.

Great for data engineers, analytics leaders, and architects simplifying mixed stacks across SaaS, databases, and files.

Save your seat: https://lnkd.in/dD2vsVRq

See you there.

#data #ai #cdata #theravitshow

Pumped for NVIDIA GTC in Washington DC starting tomorrow. I can’t wait to be back on the ground, meet Jensen Huang, Founder, NVIDIA again this year, and share what teams are really building.

I’ll be on site interviewing leaders and posting fast takeaways from the floor. If you’re attending, add these stops to your plan and say hi:

• Hewlett Packard Enterprise - Booth 125
• Hammerspace - Booth 157
• DDN - Booth 241

I’ll cover how they’re powering real workloads, what’s working, and what others can use right away. 

Drop your questions and I’ll try to bring them into the interviews. 

See you at GTC.

#data #ai #gtc #nvidiagtc #nvidiagtcdc #creator #ddn #hpe #hammerspace #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 impact

Why it matters
Most pilots fail without context and memory. Graphs give agents structure, reasoning, and traceability. That is how you ship production outcomes, not demos.

Full interview is live now.

#Neo4j #GraphSummit #GenAI #AgenticAI #GraphIntelligence #TheRavitShow

I sat down with Ultralytics’ Founder & CEO Glenn Jocher at YOLO Vision 2025 to unpack the new Ultralytics YOLO26 models and what they change for builders. While YOLO26 has been announced at YV25, the new models will become publicly available at the end of October 2025, so stay tuned!
Learn more: https://lnkd.in/d49uBeXC

During our interview with Glenn, we break down how the YOLO26 models differ from prior generations, what changed under the hood, and why it matters for training time, memory use, and results in production. Glenn explains the standout innovation in this line and how it helps with tougher cases like small objects, crowded scenes, and varied lighting.

We talk through where the impact lands first. Manufacturing quality checks, retail analytics, smart cities, drones, healthcare imaging, and safety use cases all see clear benefits. You will hear simple guidance on picking the right model size, setting up data, and moving from a pilot to a stable release.

We also cover what this launch means for the Ultralytics community. Glenn shares how the team is working with developers and researchers on feedback, documentation, and examples. 

Ultralytics recently closed a funding round, and we discuss the priorities that the investment supports, including product hardening, ecosystem tools, and education. Partnerships come up as a growth lever, with a focus on making deployment simpler across edge and cloud. 

We close on the 3 to 5-year view for the YOLO ecosystem and how the community will shape it. 

#data #yolo #ultralytics #theravitshow

BREAKING: I used the Reflex launch to build one AI app that can now track all social platforms in one place. Reflex launched a Python-only AI app builder. Front-end in Python. Back end in Python. Same performance and control I expect from a JavaScript app, but I stayed in Python end-to-end. I used it to solve a daily pain: scattered analytics across YouTube, LinkedIn, X, Instagram, TikTok and my newsletter.

What my app does
- Pulls fresh metrics from every channel and puts them on one screen
- Compares growth side by side with day over day and week over week deltas
- Ranks top posts across platforms and explains why they worked
- Flags underperformers and suggests a simple fix for the next post
- Sends a morning snapshot so I can adjust the plan before publishing

Why this matters
- One truth source beats five tabs and guesswork
- Short feedback loops help fix the next post, not next month
- Creators and teams can move from vanity metrics to decisions

Reflex made this fast because I never left Python and I could use any package on PyPI without waiting for a plugin. If you want to try a similar build, start with one dashboard and add channels as you go: https://lnkd.in/d4YxUihS

#AI #ReflexBuild #Data #TheRavitShow

Real time without the noise.

At Data Streaming Summit 2025, I spoke to Matteo Merli, Co-founder and CTO at StreamNative. We talked about why streaming matters now and what his team is building to make data in motion simple, scalable, and open. Matteo expects the next 12 months to be about streaming meeting AI in practical ways. Think instant enrichment, faster feedback loops, and agents that act safely on live context.

He liked the summit’s open, multi-technology format. It mirrors how real systems get built. His favorite threads connected Architectural Innovations with AI plus Stream Processing and the Streaming Lakehouse story.

Catch the conversation to hear Matteo’s take on where streaming is headed and why openness across the ecosystem will decide who moves fastest.

#data #ai #streaming #datastreamingsummit #theravitshow

BREAKING!!!! I am so excited to be here in Los Angeles watching it from the front row at Possible by Teradata. Big day for Customer Experience and agentic AI. Check below all announcements by Steve McMillan, President and CEO of Teradata -- 

Breaking announcements:

- Autonomous Customer Intelligence is here. Teradata is packaging software and services to turn raw data and customer signals into real-time actions across your stack. It is built for hybrid environments and scale.

- Customer Intelligence Framework connects the pieces: reusable data products, analytics and feature engineering, an Enterprise Vector Store, ClearScape Analytics, AI Workbench, and a signals layer that feeds activation engines like VCX. On top of that sit AgentBuilder, pre-built Teradata agents, and finished AI apps.

- First agentic use case: Customer Lifetime Value (CLV). The focus is not only to predict CLV but to lift it with live, signal-driven decisions at enterprise scale.

- Services and readiness. New AI Services plus a Customer Intelligence Maturity workshop to help teams find gaps, set up foundations, and govern the rollout.

- Timing. Autonomous Customer Intelligence is available now. AgentBuilder enters private preview in Q4.

Why it matters:

- CX teams want agents that do real work, not demos. That means data products, signals, governance, and activation living together

- Signals are the new unit of action. One strong signal can drive smarter moves across marketing, service, risk, and product

- Agentic AI needs trust. Model governance, lineage, and secure activation are built into the approach, not bolted on

My take from the front row at Possible:

- Data products over dashboards. Reuse beats rebuild.
- Operate on live context. Agents need fresh signals, not stale extracts.
- Build for day two. Governance, safety, and integration paths are what make this real.

I’ll share clips, conversations, and deeper analysis from Possible as the day unfolds. If you are exploring agentic CX, this is one to watch.

#data #ai #possible2025 #teradata #theravitshow

Here’s something practical for anyone building with AI right now. Most enterprise AI stalls because it cannot reach the right data. CData Software’s new Connect AI fixes that. It is the first managed MCP platform that lets AI connect to your enterprise systems in minutes, not months.

What it means in plain English: plug your AI into Salesforce, NetSuite, Jira, and 300 plus other sources without heavy ETL. Work with live data in place. No copies. No stale snapshots.

Why this stands out:

- Connect everything: AI assistants, workflows, and agent platforms
- Live access in place: no replication required
- Semantic intelligence: AI understands relationships, not just fields
- Identity-aware security: users only see what they are allowed to see
- Fast setup: point and click, up in minutes

It is built on the same connectivity stack trusted by Google Cloud, Palantir, and Salesforce Data Cloud.

If you want to try it, there is a 14-day free trial. Link below.

https://lnkd.in/dGuRbgv3

#data #ai #cdata #connectai #theravitshow

Heading to London for Cloudera #EVOLVE25 this week and I’m excited to be on site. This one is built for leaders who want real progress on trusted data and practical AI. The London stop is part of a global series focused on bringing AI to your data wherever it lives always fun to be there third trip in two weeks for us!!!!

Why attend

- Hear what’s next directly from Cloudera leadership on product strategy and where the platform is heading.

- See how a unified, hybrid data platform makes AI secure, governed, and ready for production.

- Learn from customer stories across industries and meet experts in the expo to see solutions in action.

What I’m looking forward to

- Main stage updates on roadmap, strategy, and how Cloudera is aligning data, governance, and AI for enterprise scale.

- Breakouts on agentic AI, AI Studio and Agent Studio, and deploying AI inference with NVIDIA NIM microservices.

- Meeting customers and partners to capture real implementations and day-one takeaways.

Key talks to watch

- Vision and roadmap from the executive and product teams.
- Deep dives from technology leaders on the convergence of data platforms, governance, and AI.
- Sessions focused on customer impact, growth, and regional strategies.

If you’re in London, this is a strong use of a day. You’ll hear the vision, see the tech, and meet the teams building it. I’ll be there capturing insights and conversations for my audience. 

Come say hello if you want to swap notes on trusted data and AI in production.

#data #ai #cloudera #evolve #theravitshow

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