Hi Data & AI Pros,

Did you know that the average enterprise organization uses over 600 software applications?  And that number is growing daily.  

With the volume of data growing exponentially and so many hands working in business applications, it can be a burden to update critical information and meaningfully manage all that scattered data.

Join us on May 28, 1pm ET to learn how to simplify and scale while continuously replicating your business data to any database, data lake or data warehouse.

Make it easier for you and your team to access analytics, create reports, build AI modeling and to implement machine learning.

In this live demo with CData’s Technologist Cameron LeBlanc, you will learn how Sync can:

• Automate replication to popular databases, data lakes, & data warehouses

• Save time by automating tedious and manual pipelines.

• Enrich your CRM data with reverse ETL

• Connect to any JSON or XML source through Sync’s API Connector

Looking forward to seeing you!

The CData Team

What is Agentic Data Engineering?

BREAKING – Agentic Data Engineering is LIVE!!!!

Over the past few weeks, I’ve been listening closely to data engineers talk about what slows them down the most:

-- Constantly checking if pipelines broke (and why)
-- Manually documenting lineage and logic for onboarding
-- Chasing down schema changes after they cause issues
-- Writing status updates that don’t reflect the real impact of their work
-- Feeling like half their time is spent managing tools—not building

That’s why Ascend.io’s announcement on Agentic Data Engineering is getting a lot of attention right now—because it speaks directly to those problems.
Here’s what they’ve launched: https://hubs.li/Q03n44B60

An intelligence core that tracks everything via unified metadata

This includes:

-- Schema versions
-- Pipeline lineage
-- Execution state
-- Diffs across time

And it does this automatically, with no extra config.

A programmable automation engine
Engineers can write their own triggers, actions, and logic tied to metadata events. It goes beyond traditional orchestration—because the system knows what’s happening inside each pipeline component.

Native AI agents built into the platform

These aren’t just chat interfaces. They operate on real metadata and help engineers:

- Flag breaking changes while you were OOO
- Convert components (like Ibis to Snowpark)
- Create onboarding guides for new teammates
- Trace full lineage of any column
- Suggest QA and data quality checks
- Summarize your weekly work for 1:1s
- Even help prepare resumes by pulling your real impact from work you’ve done

The biggest takeaway I’ve heard from engineers so far?

This actually feels like it was built with us in mind.

Not to replace the role—but to remove the repetition, surfacing the knowledge we usually have to explain again and again.

It’s early days, but this looks like a shift in how modern data platforms could be designed: metadata-aware, programmable, and agent-powered from the start.

If you want to take a look at the full experience and the agent capabilities, check it out here.

I’m curious—what part of this would help your team the most? Or what’s missing from your current stack that a system like this could take off your plate?

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Invest today for $2.80/share.

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Best,

Ravit Jain

Founder & Host of The Ravit Show