In this session, we’ll share our transformation journey from a traditional, centralised data warehouse to a modern data lakehouse architecture, powered by data mesh principles. We’ll explore the challenges we faced with legacy systems, the strategic decisions that led us to adopt a lakehouse model, and how data mesh enabled us to decentralise ownership, improve scalability, and enhance data governance.

Learn how to transform your data warehouse for AI/LLM readiness while making advanced analytics accessible to all team members, regardless of technical expertise. 

We'll share practical approaches to adapting data infrastructure and building user-friendly AI tools that lower the barrier to entry for sophisticated analysis. 

Key takeaways include implementation best practices, challenges encountered, and strategies for balancing technical requirements with user accessibility. Ideal for data teams looking to democratize AI-powered analytics in their organization.

Data and marketing are often treated as separate functions, but the real opportunity lies in bringing them together. In this fireside chat, Ed (Data Solutions Manager at DinMo) interviews Andrew (Head of Marketing at Brand Alley), who brings a rare dual perspective: before leading marketing, he founded and scaled a data tool for dynamic audience segmentation.

They’ll explore why data and marketing are natural teammates and how aligning the two can unlock powerful business outcomes. From enabling real-time audience activation to translating data capabilities into campaigns that drive measurable growth, the discussion will highlight practical ways to bridge the gap between teams.

Attendees will discover how to maximise the ROI of their data warehouse by embedding it into marketing workflows, ensuring data initiatives deliver clear returns through hyper-personalised customer journeys. They will walk away with actionable insights on how to make data indispensable to marketing, prove its commercial value, and create experiences that drive long-term growth.

A paradigm shift is underway; the primary consumer of data is evolving from human analysts to AI agents. This presents a strategic challenge to every data leader: how do we architect an ecosystem that satisfies relentless, machine-scale demand for governed data without overwhelming our most valuable human experts? A chaotic free-for-all, with AI agents querying sources directly, is a regression that would erase a decade of progress in data warehousing and governance.

To solve this machine-scale problem, we must deploy a machine-scale solution. This session casts a vision for the future, exploring why current models are ill-equipped for the AI era. We will introduce the concept of the virtual data engineer—an AI-powered partner designed to augment and accelerate human capabilities on a collaborative platform. Discover how to evolve your team and architecture to turn this challenge into a strategic advantage, ensuring you lead the way through this transformation.

The data landscape is fickle, and once-coveted roles like 'DBA' and 'Data Scientist' have faced challenges. Now, the spotlight shines on Data Engineers, but will they suffer the same fate? This talk dives into historical trends.

In the early 2010’s, DBA/data warehouse was the sexiest job. Data Warehouse became the “No Team.”

In the mid-2010’s, data scientist was the sexiest job. Data Science became the “mistaken for” team.

Now, data engineering is the sexiest job. Data Engineering became the “confused team”. The confusion run rampant with questions about the industry: What is a data engineer? What do they do? Should we have all kinds of nuanced titles for variations? Just how technical should they be?

Together, let’s go back to history and look for ways on how data engineering can avoid the same fate as data warehousing and data science. This talk provides a thought-provoking discussion on navigating the exciting yet challenging world of data engineering. Let's avoid the pitfalls of the past and shape a future where data engineers thrive as essential drivers of innovation and success.

So you’ve heard of Databricks, but still not sure what the fuss is all about. Yes you’ve heard it’s Spark, but then there’s this Delta thing that’s both a data lake and a data warehouse (isn’t that what Iceberg is?) And then there's Unity Catalog, that's not just a catalog, it also does access management but even surprising things like optimise your data and programmatic access to lineage and billing? But then serverless came out and now you don’t even have to learn Spark? And of course there’s a bunch of AI stuff to use or create yourself. So why not spend 30 mins learning the details of what Databricks does, and how it can turn you into a rockstar Data Engineer.