Big Data London 2025: Key Takeaways and Maia Highlights

We’ve just got back from Big Data London 2025 – and what a week it’s been! The event brought together data professionals, engineers, and business leaders from across the globe, all eager to explore the latest in AI, data engineering, and cloud-native solutions.

Matillion showed up with a booth you couldn’t miss – putting Maia (our agentic data team) front and center. We spent the event showing how agentic AI is transforming the way enterprise data teams work, and the real results early adopters are already seeing.

Let’s recap our favourite moments from BDL 2025…

Maia Leads the Way

There’s no doubt about it – Olympia was buzzing with chatter about Maia. Over the two-day event, word spread quickly about Maia’s game changing capabilities, and our booth was overflowing with attendees eager to see it in action.

We were more than happy to dive into live demos, showing visitors how an agentic data team can eliminate 95% of manual data engineering work. 

Our demos highlighted Maia’s ability to:

• Author complex pipelines using natural business language
• Build connectors in seconds rather than days
• Debug and troubleshoot in real time
• Create sophisticated data models, like star schemas and data vault, with minimal manual intervention

Real-World Success Stories from the Stage

It’s always a privilege to see our AI experts on stage, sharing real-world insights and examples of Maia’s impact. Two sessions stood out in particular:

EDF’s Data Transformation Journey

Alex Read, Senior Enterprise Data Product Manager at EDF, shared how the UK’s largest supplier of zero-carbon electricity transformed their data capabilities to support their net zero mission. Starting in 2022, EDF consolidated their tooling and quickly transitioned analysts into data engineers.

The results? A remarkable productivity boost and expanded access to data across multiple business units, powering mission-critical products.

The transformation enabled EDF to achieve a 75% productivity gain, letting federated data teams deliver advanced analytics, machine learning, and AI use cases that were previously impossible.

Live Demo: Building Complex Pipelines in Minutes

Another highlight was a live demo showcasing Maia tackling a real-world data challenge. In just 10 minutes, Maia accomplished what would normally take weeks to complete:

• Reading data landscape documentation
• Building ingestion pipelines from PostgreSQL sources
• Creating a star schema model
• Generating predictive analytics for revenue forecasting
• Documenting the entire process with natural language summaries

The demo also showed off Maia’s multimodal capabilities, handling multiple programming languages and database platforms while adhering to organizational standards and best practices.

The Era of Agentic AI is Here

BDL 2025 confirmed it: we’re entering a new phase where agentic AI is central to data engineering. Unlike traditional automation tools, Maia acts as a collaborative team member, understanding business intent, adapting to organizational standards, and providing full transparency and governance.

Key differentiators that stood out:

• The combination of agentic AI integrated into a battle hardened, tried and tested data integration platform: delivering working data pipelines, not code soup
• Natural language interaction: removing technical barriers for business users
• Real-time feedback and error correction: eliminating the usual back-and-forth of manual debugging
• Context-aware pipeline building: adhering to organizational standards and best practices
• Full visibility and control: allowing users to inspect, modify, and understand generated code

Looking Ahead

Big Data London reinforced that the future of data engineering is human-AI collaboration. 

• For data engineers, it means moving from manual tasks to strategic problem-solving.
• For business users, it opens the door to building and modifying pipelines with natural language.
• And for data leaders, it ensures AI initiatives scale safely with built-in governance and observability.

Maia embodies this future: enabling data engineering at the speed of thought, so teams can focus on innovation and impact, instead of infrastructure maintenance.

As one attendee put it after seeing the Maia demo: “What used to take months now takes minutes.” That’s the power of agentic AI.

It’s time to see Maia working in your environment. Bring us your toughest data challenges and leave with a roadmap to data and AI success.