I'm Ravit Jain — the Founder & Host of The Ravit Show, a platform dedicated to meaningful conversations with the people shaping the future of Data and AI.

What started as a passion for learning from industry leaders has grown into a global media platform that reaches millions. Today, I spend my time interviewing experts, covering major tech events, and working closely with companies to help them share their stories in a way that truly connects with the community.

Over the past few years, I’ve had the chance to:

Host 550+ interviews with data and AI leaders, founders, engineers, and product thinkers

Collaborate with 200+ companies — from fast-moving startups to nearly every major tech giant

Cover 100+ conferences across the world, sharing real-time insights and backstage conversations

Support 200+ partner campaigns to amplify their product launches, webinars, reports, and community efforts

My focus is always on creating content that is useful, grounded, and easy to understand — no hype, just real value. Whether it's discussing GenAI, data quality, modern architecture, or what it takes to scale AI responsibly, my goal is to keep the conversation practical and community-first.

If you’re building in this space or want to share what you’re working on, I’d love to connect and learn more.

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

Most AI projects do not fail because of the model.

They fail because the data foundation is weak.

That is why I spent time with the ebook Building a Data and AI Platform with PostgreSQL by EDB.  You can get the digital copy here: https://lnkd.in/dKA9S3rq

What stood out to me is the way it treats data, AI, and PostgreSQL as one platform, not three disconnected initiatives that struggle to work together.

The ebook breaks things down in a very clear and practical way:

Why AI needs far more data than traditional applications

What happens when that data is spread across teams and tools

How PostgreSQL can sit at the core of an AI and analytics platform

It stays grounded. You see real patterns for GenAI and RAG, examples built on Postgres, and honest guidance on handling technical debt and data silos.

If you are serious about building AI on PostgreSQL while keeping control over your data, this is worth reading.

#Sponsored #PostgresAIPlatform #EDBPostgresAI #SovereignAI #DigitalSovereignty #PostgreSQL

Ravit Jain

🚨Breaking: Big announcements by MongoDB. I just spoke to Pete Johnson, Field CTO for AI at MongoDB on The Ravit Show. This conversation cuts through the noise around AI and focuses on what actually works in production!!!!

We get into:

- What the Field CTO for AI role really looks like in the field
- How MongoDB thinks about the data and AI intersection
- Which recent MongoDB announcements actually matter
- What enterprises should prioritize as they head into 2026
- Clear advice for developers, data, and DevOps teams building with AI

No hype. No buzzwords. Just practical insight from someone working directly with customers every day.

If you are building, scaling, or planning AI beyond pilots, this episode is for you.

Thank you MongoDB for sponsoring this post!

#data #ai #mongodb #databases #mongodbpartner #agents #agenticai #theravitshow

Most mainframe challenges today are not caused by broken systems.
They are caused by disappearing knowledge.
Senior experts are retiring.
Documentation is incomplete or outdated.

And newer team members are expected to operate some of the most critical systems in the enterprise with very little context.

In Part 1 of this podcast, we focus on where the AI journey really begins.
- Why mainframe teams are under pressure right now.
- What types of institutional knowledge are most at risk.
- And how AI changes the way that knowledge is captured, shared, and used.

Liat Sokolov, BMC Software walks through why AI is showing up at this moment and why traditional approaches have not been enough.

Anthony DiStauro, BMC Software explains how AI helps close the skills gap by shortening learning curves and guiding teams through complex systems with more confidence.

This episode is not about autonomy yet.

It is about preserving what matters and helping people work better with the systems they already depend on.

Part 1 is live now.

Link in comments for full YouTube video.

#data #ai #mainframe #agents #bmi #mainframe #skills #bmc #theravitshow

Most enterprises I speak with are not trying to replace Oracle.
They are trying to make Oracle work in a modern data stack.

That is exactly why I am excited about this one.

Oracle still sits at the core of critical enterprise systems. But getting that data out reliably, at scale, and into cloud platforms for analytics and AI is where teams struggle the most. I hear this problem repeatedly from CIOs, data leaders, and platform teams.

This session on modernizing Oracle data integration with CData Sync hits a very real enterprise pain point. Check out and register here -- https://lnkd.in/dVujQiyP

What stood out to me is the focus on doing this without adding more complexity. No heavy engineering effort. No fragile pipelines. Just reliable, continuous data movement that works across hybrid and cloud environments.

For enterprises, this matters because Oracle data is not going away. But expectations around speed, freshness, governance, and AI readiness have changed.

If your Oracle data still lives in silos or takes days to reach analytics teams, this is a must attend. This is about making Oracle data usable again for modern decision making.

Highly recommend this for enterprise data and analytics leaders who want fewer workarounds and more impact.

#data #ai #oracle #pipelines #enterprise #agents #agentic #cdata #theravitshow

At Microsoft Ignite, I finally got to interview the man himself, Arun Ulag, President, Azure Data, Microsoft, on The Ravit Show. He is one of the best in the data and analytics space, and meeting him in person was so good.

This was not a surface-level chat. We went straight into the big announcements and what they unlock for customers:

 • The real takeaways from his keynote and how they help enterprises move faster with data and AI
 • What Fabric IQ actually is, how it works behind the scenes, and why it is a big deal
 • How customers can start seeing value from Fabric IQ from day one, not after months of setup
 • What makes Azure HorizonDB for PostgreSQL different and where it fits for modern workloads
 • What is coming next for data services in the era of AI
 • How leaders should think about the next 4 to 5 months with all these updates landing at once

If you are trying to understand where the Microsoft data and AI stack is really heading, this conversation will give you an obvious picture!!!!

Link to the complete interview in the comments ⬇️

Most people are still asking what an LLM is. 

Techies in 2026 will be asking which one to use and why.

The stack is getting clearer.

You have platforms that power reasoning and agents.
You have open source models that give you control.
You have tools for memory, search, and context.
You have frameworks to connect everything.
And you have no code options that remove friction completely.

This is no longer about experimenting.
This is about building real products, faster and smarter.

If you are in tech and not tracking this shift, you are already late.

Save this.
You will come back to it.

LLM Tools in 2026

READY for DataDriven 2026!!!! Last year at the DataDriven Conference, I had a conversation with Manish Sood, Founder and CEO of Reltio. We spoke about a simple but critical truth. AI does not fail because of ambition. It fails because data is fragmented, inconsistent, and hard to trust.

Our discussion covered:

- The shift enterprises must make to get real value from AI
- Why fragmented data is still the biggest blocker
- What a unified system of record actually enables in real use cases
- How enterprise data management will evolve over the next few years

That conversation has stayed with me because the problem is even more real today.

This is why I am excited to be heading back to DataDriven 2026.

We are now in the age of agentic AI. Systems that can act and improve on their own are becoming real. But they only work when the data foundation is strong, trusted, and connected. 

DataDriven is one of the few conferences that stays grounded in this reality and focuses on execution, not hype.

This year, the event brings together data and AI leaders who are actually building and running these systems inside large enterprises. The focus is clear. How to unify data. How to govern it. How to make it usable for AI at scale.

Event details:

Feb 23–26, 2026

Marriott Orlando World Center, Orlando

Register here: https://lnkd.in/dzjf2BCr

Use code JAIN for $100 off registration

Looking forward to the conversations, the lessons, and reconnecting with people who are shaping how enterprise AI really works.


#data #ai #datadriven #reltio #theravitshow

Building your first AI agent can feel overwhelming. There are so many tools, frameworks, and steps that it is easy to lose track of where to begin.

To simplify the process, I created a 20-step roadmap that takes you from the idea stage all the way to launch and ongoing maintenance.

Here’s how the journey looks:
	1.	Define the purpose of your agent with clear success metrics and use cases
	2.	Select the right development framework like LangChain, AutoGen, or CrewAI
	3.	Choose a language model such as GPT-4, Claude, or LLaMA 2 based on cost, performance, and needs
	4.	Outline the core capabilities and limits of your agent
	5.	Plan tool integrations, APIs, and databases your agent will need
	6.	Design the agent architecture including input handling, processing, and error management
	7.	Implement memory management for both short-term and long-term interactions
	8.	Create prompt templates that are structured and reusable
	9.	Add context injection for more accurate and personalized responses
	10.	Enable tool calling for real-world task completion
	11.	Equip your agent with multi-step reasoning and planning abilities
	12.	Apply safety filters to prevent harmful or biased outputs
	13.	Set up monitoring for accuracy, latency, and user feedback
	14.	Optimize for speed using caching, async calls, and model efficiency
	15.	Enable continuous learning through retraining, A/B testing, and feedback loops
	16.	Add multimodal capabilities like text, image, speech, and video
	17.	Personalize the experience based on user history and preferences
	18.	Plan deployment strategy across web, mobile, APIs, or on-device
	19.	Launch with a controlled rollout and proper support in place
	20.	Maintain and upgrade regularly to stay secure and relevant

Every step builds on the previous one. Together, they form a structured path for turning ideas into real, functional AI agents.

If you are experimenting with agentic AI or planning to build your first agent, this roadmap can be your starting point. Save it for future reference.

————-

✅  I post real stories and lessons from data and AI. Follow me and join the newsletter at www.theravitshow.com

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 :)

Roadmap for First AI Agent

Dinesh Didel

If you are aspiring to build a serious career in AI in 2026, learning tools is not enough. You need skills that actually compound over time.

Most people focus on prompts or the latest model. That helps you get started. It does not help you stand out.

The biggest shift I am seeing is this. AI roles are no longer about using one tool well. They are about understanding the full system around AI.

That is why I put together a breakdown of the top 15 AI skills almost everyone must know in 2026.

These are not hype skills. These are the skills teams quietly expect you to have.

A few that matter more than people realize:

- AI literacy
You must understand how models think, where they fail, and why hallucinations happen. Without this, everything else breaks.

- Context engineering
Great outputs do not come from clever prompts. They come from feeding the right context, instructions, memory, and examples before the model responds.

- Prompt chaining and workflows
Real work is never one prompt. It is plan, draft, improve, validate, and ship. This is how AI becomes useful at scale.

- AI research and fact checking
Using AI like a consultant matters more than generating text. Sources, comparisons, and insights are the real value.

- AI agents and automation
Delegating tasks to AI requires structure, guardrails, and evaluation. Otherwise agents become expensive demos.

- Evaluation and safety
The most underrated skill. If you cannot measure quality, consistency, cost, and failure modes, you are guessing instead of engineering.

- The key thing to understand is this.
In 2026, strong AI professionals are not judged by outputs. They are judged by reliability, repeatability, and real outcomes.

If you are planning to upskill this year, focus less on tricks and more on foundations.

This is where long term AI careers are being built.

Join The Ravit Show Newsletter - - https://lnkd.in/dCpqgbSN

#data #ai #agents #agentic #enterprises #models #a2a #memory #theravitshow

Top AI Skills Almost Everyone Must Know in 2026

Most people are mixing these up. Agent. AI Agent. Agentic AI. They are not the same thing.

An Agent follows rules.

If this happens, do that.

No thinking. No learning. Just execution.

An AI Agent can think within limits. It understands context. It can use tools.
But it still needs guidance.

Agentic AI is different.
It has goals.
It plans.
It acts.
It learns from feedback and improves over time.

This shift matters.

Because most teams are still building rule-based automation
While the world is moving toward systems that can run, adapt, and improve on their own.

If you are designing products, workflows, or data platforms in 2026
You need to be clear about which one you are actually building.

Not everything needs Agentic AI.
But pretending they are all the same is a mistake.

Save this.
Share it with your team.

Breaking announcement - IBM has just shared an important update around its Partner Plus program, and this one really stood out to me.

I’ve been involved in the IBM partner ecosystem for years. I’ve judged the Partner Plus event, attended every IBM Think, and one of my favorite parts of those events is always meeting partners. Builders, operators, system integrators, ISVs. People doing the real work of turning strategy into outcomes.

Great announcement by my friend, Kareem Yusuf Ph.D, Senior Vice President, Ecosystem, Strategic Partners & Initiatives, IBM. Check out the details here – https://lnkd.in/dSxeSS8B

This announcement is important because it doubles down on something that actually works: a strong, aligned ecosystem.

IBM is accelerating how it supports partners by making enhancements that encourage growth, speed, and execution. The focus is clear.
- Helping partners move faster
- Improving partner economics
- Reducing friction in how deals are built and closed
- Making AI real for enterprises, not theoretical

What stands out most is that this is not just about adding more programs or checking boxes. It’s about alignment. Between IBM teams and partners. Between technology and go-to- market. Between AI ambition and business reality.

When ecosystems work, customers win. Partners grow. Innovation scales.
I’ve seen the energy in this community up close, at Think and beyond. 

This update feels like a natural next step in strengthening that momentum.
Looking forward to seeing how partners take this forward.

#data #ai #ibmpartners IBM Partner Plus IBM Consulting IBM Data, AI & Automation #ibmpartnerplus #ibm #isv #agents #theravitshow

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