Practical Data Engineering in 2026

Data engineering used to have a pretty clear job: move data from A to B, clean it up along the way, don't break anything. That job hasn't disappeared, but it's no longer the whole picture. AI changed what "ready" means.

Asha Holla walks through that shift. Pipelines that used to run in isolation, quietly feeding one dashboard or one report, now need to feed something with much higher expectations. AI doesn't tolerate stale data, mismatched schemas, or a pipeline that only half-updates on a bad day. It just fails, often without telling anyone why.

The session covers what it actually takes to move from disconnected pipelines to a platform that can support AI-driven use cases without falling over.

• Where traditional pipeline design breaks down under AI workloads
• What "AI-ready" really requires from a data platform, beyond the marketing version
• How reliability and scalability get harder to fake once AI is in the loop
• What a unified platform looks like versus a collection of pipelines that happen to coexist

The gap between "we have pipelines" and "we have an AI-ready platform" is bigger than most teams realise until something breaks.

• How data engineering's role has shifted now that AI depends on it directly
• What separates a reliable, scalable data workflow from one that's just functional
• How to think about unifying isolated pipelines into a platform built for AI use cases
• Practical steps for closing the gap between where a team's data stands today and what AI actually needs

AI doesn't run on models alone. It runs on data platforms built to hold up under real demands, and that's an engineering problem before it's ever an AI problem.