Deep Dish Data Virtual Series
2 Year Anniversary Special | State of Data Engineering
April 29, 2025 | 8 AM PT / 11 AM ET / 4 PM GMT

Thursday, June 6 | 9:30 AM - 2:00 PM CAT | Workshop17, Johannesburg

Deep Dish Data - 2 Year Anniversary - Streaming Live from Denver

We're turning 2—and you're invited! Join us for a special live edition of Deep Dish Data as we celebrate with a conversation that’s real, relevant, and rooted in the big questions shaping the future of data engineering.

Date: April 29, 2025
Time: 8 AM PT | 11 AM ET | 4 PM GMT

What to expect:

This isn't just another webinar. We're going live from Denver with a few of our favorite voices in the data world to talk honestly about how engineering is evolving—what’s working, what’s not, and where we go from here.

We’ll also unpack key findings from Matillion’s brand-new Data Integration and AI Readiness Report and hear firsthand how teams are adapting their strategies in 2025.

You’ll hear from:

Mark Balkenende – Host, VP of Product Marketing at Matillion
Joe Reis – Resident co-host, Chief Nerd and co-author of Fundamentals of Data Engineering
Carly Taylor – Founder of Rebel Data Science

Key topics:

The current state of data engineering
The evolution of workflows, orchestration, and automation
Highlights from Matillion’s Data Integration and AI Readiness Report
What’s next for modern data teams

Why attend?

Hear from respected voices in the data industry
Learn what’s top of mind for data leaders in 2025
Explore new findings on AI readiness and data integration
Participate in live Q&A and audience polls
Gain practical takeaways for your team and strategy

Can’t make it live? No worries! Sign up now and we’ll deliver a fresh recording straight to your inbox!

Register Now

Our live product demos include:

Connecting any data source to Snowflake or your cloud data warehouse
Creating no-code batch and change data capture pipelines
Orchestrating complex business logic via visual patterns in a low-code tool
Speeding up development with a live sample of your data at design-time
Taking advantage of Snowflake's native features with modern ELT and how that is faster than traditional ETL patterns

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