Here is what you can get done in under 15 minutes if the silver layer of your Lakehouse is automated.

AI Agent runtimes could be considered a foundational compenent of the most modern infrastructure stacks - and maybe it's time you considered them if you're not already! That's what we cover in this week's Podcast - available now on all Podcast platforms and YouTube.

Most of the work in building a Data Vault isn't the code. It's the design. Reading metadata, profiling samples, identifying business keys, capturing definitions nobody updates later. We filmed a walkthrough of the IRiS Assistant doing exactly that work. One source table, start to finish: profiled, modelled, definitions captured, production-ready metadata pushed to the repository, in a single conversation. If you've ever sat through the design phase of a Data Vault build, this is worth eleven minutes. The IRiS Assistant is live in beta: https://ignition-data.com/iris-assistant

It is proving that the data means what the enterprise says it means. That is why the launch of IRiS Assistant last week caught my attention. For Azure and Microsoft Fabric teams working with Data Vault, this could be a game changing signal. Not because AI has arrived in modelling, but because the assistant is targeting the awkward phase zero work: ➡️ Source profiling, ➡️ Business-key discovery, ➡️ Relationship mapping and metadata capture. This week’s newsletter looks at what matters, what still needs proving, and why Fabric data engineers should be paying attention. Check out this week's video edition of www.datapro.news 👇 IRiS Assistant announcement: https://ignition-data.com/iris

97 issues in, I wanted to stop and look at where we are heading. Not the weekly news. Not the tool releases or the model benchmarks. The bigger picture: What has actually happened to us as data engineers over the last two years, and what does it mean for where our careers go from here? The profession has split into two groups. One group is more in demand than ever. The other is already feeling the automation squeeze - and oftentimes they're not aware of it yet. This week's newsletter is a full retrospective across all 97 previous editions. I've traced the three forces that reshaped the job (one economic, one regulatory, one physical), named what separated the engineers who thrived from those who struggled, and mapped what the next horizon actually looks like from here. Checkout datapro.news Video edition here 👇 What is your read on where the profession is heading?