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Hydrate Your Lakehouse for Exceptional AI Outcomes, Agents for Customer Data, AWS Marketplace Announcements

Author

Ravit Jain
December 08, 2025

Hi Data & AI Pros,

Databricks CEO Ali Ghodsi recently shared that the biggest gap between average and exceptional AI outcomes lies in the data still locked in on-premises systems. 


Join us December 10 at 1 p.m. ET to see how CData Sync extends Databricks Lakeflow to deliver faster, fully governed, and AI-ready data across hybrid and multi-cloud environments.

In this demo, Databricks advanced user Andrew Chabot, Senior Manager of Data Engineering, will reveal how to: 

  • Accelerate lakehouse hydration from hundreds of enterprise sources 

  • Strengthen governance and consistency through Unity Catalog–aligned replication 

  • Empower teams with trusted data for AIBI and Genie to drive faster insights 

  • Simplify hybrid and multi-cloud pipeline management 

Don’t miss this opportunity to learn from an enterprise practitioner driving Databricks success at scale. 

Most companies want AI agents talking to their customers. Very few have the data to back it up. I had a blast chatting with Ali Behnam, Founder of Tealium on The Ravit Show at AWS re:Invent. If you work with customer data or AI, this one is worth watching.

Tealium helps organizations bring customer data together in one place and use it in real time across marketing, product, and AI use cases. I keep hearing their name when people talk about getting data ready for AI, so I sat down with Ali to go deeper.

We spoke about

- Who uses Tealium and the main problem they solve for enterprises that are serious about customer data

- Why agentic AI is such a big theme this year and what is actually changing inside large companies

- Why so many AI agent projects get stuck in pilots and what is missing to make them work in the real world

- How Tealium works with AWS, and what being built on AWS unlocks for customers

- What Ali wants customers to be able to do by next re:Invent that they cannot easily do today

If you are trying to make AI agents useful for real customers, not just in demos, I think you will find this helpful.

AWS has also shared three key pages that are worth a close look. You can use these as a starting point to explore what is now possible.

This blog explains how AWS Marketplace is being tuned for the AI era:

  • Smarter discovery, including AI driven search

  • More solution based offers, not just single products

  • Faster private offers and smoother deployment paths

For teams, this means less time on contracts and glue work, and more time getting agents into real workflows.

This page is a focused catalog for AI agents and tools. It groups offerings into:

  • Software that already has agents embedded

  • Pre built AI agents you can adopt quickly

  • Agent tools like context, knowledge bases, and guardrails

  • Agent development and infrastructure solutions

  • Professional services for strategy and rollout

Instead of hunting across dozens of pages, you have a single place to:

  • Browse agent solutions

  • Compare types of offerings

  • Filter by use case and delivery method

  • Keep discovery, purchase, and deployment inside your AWS environment

Agentic AI Competency Blog:
This blog introduces the new Agentic AI categories and lists the 60 partners that have met the bar. It also explains how this work connects to AWS services designed to run agents at scale in production.

Read this to understand:

  • Which partners AWS has already validated for serious agent workloads

  • How AWS is thinking about responsible, governed agent systems

  • Where to start if you want to stay close to AWS native patterns

A simple 12 month playbook

If you put all of this together, you get a clear way to plan your next 12 months with agents on AWS:

  1. Use off the shelf agent applications for quick wins
    Start with validated applications from the competency list and the AI agents and tools page for use cases like support, analytics, sales operations, or coding help. You do not need to start from zero for every problem.

  2. Use validated tools when you need flexibility
    When you have custom data, strong security needs, or deep integration requirements, pick tools from the AI agents and tools catalog and from among the Agentic AI partners. You keep control, but you are not rebuilding the foundation yourself.

  3. Use consulting partners for core or regulated systems
    Once you touch core systems or regulated data, involve consulting partners that have passed the Agentic AI competency bar. They have already dealt with governance, monitoring, and failure modes in real projects.

This is how you move from isolated demos to a portfolio of agent systems that people inside the business can actually trust.

These 60 partners are an early signal of who is already on that path.

What I will be watching at re:Invent

As I walk the floor and talk to these partners, I will be looking at:

  • How they are using the new Marketplace features in real customer deals

  • What kinds of agent use cases are live in production today

  • How customers are balancing off the shelf applications, tools, and consulting help

I will share more conversations and concrete examples as the week goes on.

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Best,

Ravit Jain

Founder & Host of The Ravit Show