The migration on your roadmap doesn't need to take 6 months.
Have a data migration you've been putting off? Maybe you've outgrown your current warehouse and need to move to something that scales with you. Maybe it's Spark jobs that need to become SQL-based pipelines. Or legacy platform exports that are nearly impossible to read, let alone rebuild. Whatever the scenario, the migration keeps getting pushed to "next quarter" because the scope feels impossible — and the longer you wait, the more you're paying to maintain systems you've already decided to leave.
The hard part was never choosing the destination — it's the archeology. Reverse-engineering undocumented logic, mapping hidden dependencies, and manually rebuilding everything in a modern stack while praying the output still matches. Meanwhile, every month of delay means more money spent on legacy licensing, over-provisioned infrastructure, and engineering hours that could be building something new.
In this session, you'll see how AI agents tackle the hardest parts of data modernization and migration — live. We'll walk through a real migration workflow: pointing agents at legacy pipeline logic, generating modern equivalents, and validating that everything matches. And we'll cover the frameworks that make it possible — so you can start realizing the cost and performance benefits of your target platform sooner, not someday.
See an agent-powered migration workflow — live
Reverse-engineer legacy pipeline logic
Watch agents analyze undocumented code in real time — extracting business rules, mapping data flows, and surfacing what a pipeline actually does. Whether it's Spark jobs, stored procedures, or opaque platform exports, agents handle the archeology.
Generate modern pipeline equivalents
See how agents translate legacy logic into modern, maintainable pipelines — whether you're converting Spark to SQL, moving between warehouses as you scale, or migrating off a legacy platform entirely. Faster migrations mean faster time-to-value on your target platform and less money spent maintaining systems you've already decided to leave.
Prove the migration actually worked
See validation workflows that compare legacy and modern outputs — row counts, schema checks, and business logic parity — so migrations are backed by proof, not crossed fingers.
Built for teams staring down a migration
Whether you're outgrowing your current warehouse, moving from Spark to SQL, untangling legacy platform exports, or consolidating data platforms — this session shows you what's possible when AI agents handle the hardest parts of migration.
Learn from the experts
Tessa is a Senior Cloud Data Architect at Ascend.io with 7+ years building data pipelines and cloud infrastructure. She previously taught data analytics at UC Berkeley Extension and loves showing engineers how AI can accelerate their work.
Your migration just got a lot less painful.
Join us for a live session where you'll see AI agents understand, modernize, and validate legacy pipelines — and what agent-assisted migration actually looks like in practice.
Register Now — It's Free
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