Ever moved house and had the movers just tip every box in, unlabelled? You've technically moved. But the saucepan's in the garage, the pasta sauce is in a bedroom box, and dinner isn't happening tonight. That's what lift and shift does to a data platform migration. Fast, cheap upfront, and you're still digging through boxes six months later when you try to build anything on top of it, like AI. There's another way to make the same move: unpack and sort as you go, so what lands is actually usable from day one. The catch used to be time. Unpacking properly, by hand, takes months nobody has. That's the part we automate. Read how "move and improve" works 👇 https://ignition-data.com/blog/how-launchpad-helps-you-move-and-improve

Our next DV 2.1 Certification Course runs 18-21 August, and for a limited time seats are 25% off: A$3,750 ex GST per student (standard A$4,975). What's included: - 15+ hours of self-paced learning - New modules on cloud integration, automation, and AI/ML platforms - Hands-on implementation exercises and case studies - Scenario-based certification assessment Taught by Nols Ebersohn, APAC's only Data Vault Alliance Authorised Trainer. Whether you're renewing an existing certification or getting your team certified for the first time, seats are limited and tend to fill fast. Book today: https://www.eventbrite.com.au/e/lakehouses-done-right-data-vault-21-training-18-21-august-2026-tickets-1974954127961

If you're still designing your stack with a separate transactional layer, an analytical warehouse, and a real-time tier stitched together by pipelines — H1 2026 just made that architecture harder to justify. Every major platform shipped something this half that chips away at the need for that middle layer. This week's issue breaks down exactly what ➡️ Snowflake, ➡️ Databricks, ➡️ BigQuery, ➡️ Redshift, and ➡️ MS Fabric each did — and what the pattern means for the rest of the year. Video version of this weeks www.datapro.news below 👇

Most enterprise AI projects don't fail at the model. They fail at the foundation. The pattern is familiar: a promising proof of concept, then the initiative stalls. The cause usually isn't the AI. It's a data foundation without stable entity identity, complete history, or auditable lineage, the conditions that turn confident-sounding output into output you can actually trust. We're joining Scalefree International for a live panel on how to fix the architecture underneath. Data Vault: The Foundation for Trusted AI 📅 Tuesday, 30 June 2026 ⏰ 09:00 CEST On the panel: 🎙️ Julien Redmond, CEO of Ignition and IRiS 🎙️ Alexander Lai, IRiS Product Lead 🎙️ Ole Bause, Senior Consultant, Scalefree We'll get into why the data foundation, not the model, is the real bottleneck for enterprise-grade AI, and how the right architecture delivers the semantic meaning, history, and lineage that reliable AI depends on. Register via the link here: https://us02web.zoom.us/webinar/register/WN_u1dUBz0xSUKSGbF_cftHoA#/registration

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