Paradime is the pioneer in building the 'Cursor for Data' - a platform for the AI-enabled analytics engineer to achieve 10x productivity. In this lighting talk, we will showcase how Paradime and DinoAI is changing the way analytics engineering is done.

Everyone talks about AI, but few are truly winning with it. In today’s fast-paced analytics landscape, dashboards and chatbot features aren’t enough. Winning in the age of AI takes more: a unified, governed platform that brings data, AI, and people together to drive real decisions at scale.

In this session, discover how Pyramid Analytics helps organizations cut through silos, hype, and complexity to deliver smarter, faster, and more trusted decisions across the enterprise.

AI agents are the next essential enterprise capability, but they bring new complexities in control, safety, and scale. In this interactive session, see how you can design, deploy, and govern agents alongside your existing analytics and models.

Analytics engineers are at a crossroads. Back in 2018, dbt paved the way for for this new kind of data professional, people who had technical ability and could understand business context. But here's the thing: AI is automating traditional tasks like pipeline building and dashboard creation. So then what happens to analytics engineers? They don't disappear - they evolve.

The same skills that made analytics engineers valuable also make them perfect for a new role I'm calling 'Analytics Intelligence Engineers.' Instead of writing SQL, they're writing the context that makes AI actually useful for business users.

In this talk, I'll show you what this evolution looks like day-to-day. We'll explore building semantic layers, crafting AI context, and measuring AI performance - all through real examples using Lightdash. You'll see how the work shifts from data plumbing to data intelligence, and walk away with practical tips for making AI tools more effective in your organization. Whether you're an analytics engineer wondering about your future or a leader planning your data strategy, this session will help you understand where the field is heading and how to get there.

The modern enterprise is increasingly defined by the need for open, governed, and intelligent data access. This session explores how Apache Iceberg, Dremio, and the Model Context Protocol (MCP) come together to enable the Agentic Lakehouse. A data platform that is interoperable, high-performing, and AI-ready.

We’ll begin with Apache Iceberg, which provides the foundation for data interoperability across teams and organisations, ensuring shared datasets can be reliably accessed and evolved. From there, we’ll highlight how Dremio extends Iceberg with turnkey governance, management, and performance acceleration, unifying your lakehouse with databases and warehouses under one platform. Finally, we’ll introduce MCP and showcase how innovations like the Dremio MCP server enable natural-language analytics on your data. 

With the power of Dremio’s built-in semantic layer, AI agents and humans alike can ask complex business questions in plain language and receive accurate, governed answers.

Join us to learn how to unlock the next generation of data interaction with the Agentic Lakehouse.

The growth of connected data has made graph databases essential, yet organisations often face a dilemma: choosing between an operational graph for real-time queries or an analytical engine for large-scale processing. This division leads to data silos and complex ETL pipelines, hindering the seamless integration of real-time insights with deep analytics and the ability to ground AI models in factual, enterprise-specific knowledge. Google Cloud aims to solve this with a unified "Graph Fabric," introducing Spanner Graph, which extends Spanner with native support for the ISO standard Graph Query Language (GQL). This session will cover how Google Cloud has developed a Unified Graph Solution with BigQuery and Spanner graphs to serve a full spectrum of graph needs from operational to analytical.

Data leaders today face a familiar challenge: complex pipelines, duplicated systems, and spiraling infrastructure costs. Standardizing around Kafka for real-time and Iceberg for large-scale analytics has gone some way towards addressing this but still requires separate stacks, leaving teams to stitch them together at high expense and risk.

This talk will explore how Kafka and Iceberg together form a new foundation for data infrastructure. One that unifies streaming and analytics into a single, cost-efficient layer. By standardizing on these open technologies, organizations can reduce data duplication, simplify governance, and unlock both instant insights and long-term value from the same platform.

You will come away with a clear understanding of why this convergence is reshaping the industry, how it lowers operational risk, and advantages it offers for building durable, future-proof data capabilities.

Is your analytics workflow stuck in fragmented chaos? AlphaSights, the global leader in expert knowledge on demand, used to juggle queries, scripts, spreadsheets, and dashboards across different tools just to get one analysis out the door. Manual updates slowed their teams, stakeholders waited too long for insights, and opportunities slipped through the cracks.With Hex, AlphaSights built a fully integrated Research Hub that unifies data queries, API calls, ML-powered enrichment, and reporting — all in one place. They eliminated manual work, automated updates, and empowered business teams to act faster on opportunities.The result: faster reaction times, broader coverage, and measurable commercial impact. Join this session to see how AlphaSights turned fragmented workflows into a seamless, automated pipeline — and learn how your team can build faster, smarter insights too.

Data platform migrations and modernisations take 18 months. 80% fail or are late. Teams waste 35% of their time rediscovering tribal knowledge. 'Vibe coding' causes 2000% compute spikes. Generic AI tools generate code without understanding your business logic, creating production disasters.

Fortune 500 companies are escaping this $280B crisis. One retail giant cut their SAP-to-Fabric migration from 18 to 6 months. Another achieved 85% error reduction in procurement analytics.

This session reveals how global enterprises capture organisational knowledge permanently, automate the entire data product lifecycle, and deliver production-ready analytics 3x faster. Learn the AI-first approach that transforms months of manual work into weeks of automated delivery.

When customers enjoy Gousto's recipe kits, they see the delicious result but not the careful steps it takes to get there. Data works the same way. In this session, Yanick will share how Gousto built a business case for analytics engineering, making an often-invisible discipline central to the company's strategy. He'll unpack how the team moved from ad-hoc outputs to a structured, mesh-ready approach, reducing complexity, proving ROI, and giving leadership confidence in data as a competitive advantage.

Most enterprise AI initiatives don’t fail because of bad models. They fail because of bad data. As organizations rush to integrate LLMs and advanced analytics into production, they often hit a roadblock: datasets that are messy, constantly evolving, and nearly impossible to manage at scale.

This session reveals why data is the Achilles’ heel of enterprise AI and how data version control can turn that weakness into a strength. You’ll learn how data version control transforms the way teams manage training datasets, track ML experiments, and ensure reproducibility across complex, distributed systems.

We’ll cover the fundamentals of data versioning, its role in modern enterprise AI architecture, and real-world examples of teams using it to build scalable, trustworthy AI systems. 

Whether you’re an ML engineer, data architect, or AI leader, this talk will help you identify critical data challenges before they stall your roadmap, and provide you with a proven framework to overcome them.

Edmund Optics stands at the forefront of advanced manufacturing, distributing more than 34,000 products and customised solutions in optics, photonics and imaging to a range of industries across the globe. Just a year ago, Edmund Optics began an ambitious journey to transform its data science capabilities, aiming to use Machine Learning (ML) and AI to deliver real value to their business and customers.  

Join us for an engaging panel discussion featuring Daniel Adams, Global Analytics Manager at Edmund Optics, as he shares the company's remarkable transformation from having no formal data science capabilities to deploying multiple ML and AI models in production—all within just 12 months. Daniel will highlight how Edmund Optics cultivated internal enthusiasm for data solutions, built trust, and created momentum to push the boundaries of what’s possible with data. 

In this session, Daniel will reveal three key lessons learned on the journey from “data zero” to “data hero.” If you’re navigating a similar path, don’t miss this opportunity to discover actionable insights and strategies that can empower your own internal data initiatives.

In today’s landscape, data truly is the new currency. But unlocking its full value requires overcoming silos, ensuring trust and quality, and then applying the right AI and analytics capabilities to create real business impact. In this session, we’ll explore how Oakbrook Finance is tackling these challenges head-on — and the role that Fivetran and Databricks play in enabling that journey.

Oakbrook Finance is a UK-based consumer lender transforming how people access credit. By combining advanced data science with a customer-first approach, Oakbrook delivers fair, transparent, and flexible credit solutions — proving that lending can be both innovative and human-centred.

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.

Discover how Dun & Bradstreet and other global enterprises use Data Observability to ensure Quality & Efficiency, and enforce compliance across on-prem and cloud environments. Learn proven strategies to operationalize governance, accelerate cloud migrations, and deliver trusted data for AI and analytics at scale. Join us to learn how Data Observability and Agentic Data Management empowers leaders, engineers, and business teams to drive efficiency and savings at petabyte scale.

AI-powered development tools are accelerating development speed across the board and analytics event implementation is no exception to this, but without appropriate usage they’re very capable of creating organizational chaos. Same company, same prompt, completely different schemas—data teams can’t analyze what should be identical events across platforms.

The infrastructure assumptions that worked when developers shipped tracking changes in sprint cycles or quarters are breaking when they ship them multiple times per day. Schema inconsistency, cost surprises from experimental traffic, and trust erosion in AI-generated code are becoming the new normal.

Josh will demonstrate how Snowplow’s MCP (Model Context Protocol) server and data-structure toolchains enable teams to harness AI development speed while maintaining data quality and architectural consistency. Using Snowplow’s production approach of AI-powered design paired with deterministic implementation, teams get rapid iteration without the hallucination bugs that plague direct AI code generation.

Key Takeaways:

• How AI development acceleration is fragmenting analytics schemas within organizations

• Architectural patterns that separate AI creativity from production reliability

• Real-world implementation using MCP, Data Products, and deterministic code generation

Multiverse is proud to host the Ministry of Defence (MOD) on stage at Big Data LDN to discuss their pioneering partnership focused on building data skills and capabilities across the defence sector. As organisations worldwide navigate the transformative potential of AI and advanced analytics, investing in staff development has become a strategic imperative. This partnership is already making tangible impact: over 250 MOD employees are currently enrolled in upskilling programmes designed to strengthen data literacy, enhance analytical capabilities, and embed a culture of continuous learning. The initiative equips personnel to leverage data effectively, driving smarter decision-making and supporting the MOD’s ongoing Strategic Defence Reform agenda.

Speakers will share insights into how targeted learning interventions and personalised development pathways can accelerate organisational capability while delivering measurable outcomes. Attendees will hear first-hand how the collaboration between Multiverse and the MOD has delivered early successes, fostered a growth mindset among staff, and positioned the MOD to scale these programmes far beyond their current reach. This session offers a unique opportunity for leaders and practitioners alike to explore the intersection of talent investment, AI adoption, and data-driven transformation, demonstrating how strategic upskilling can future-proof organisations in an increasingly complex data landscape.

Moving data between operational systems and analytics platforms is often a painful process. Traditional pipelines that transfer data in and out of warehouses tend to become complex, brittle, and expensive to maintain over time.

Much of this complexity, however, is avoidable. Data in motion and data at rest—Kafka Topics and Iceberg Tables—can be treated as two sides of the same coin. By establishing an equivalence between Topics and Tables, it’s possible to transparently map between them and rethink how pipelines are built.

This talk introduces a declarative approach to bridging streaming and table-based systems. By shifting complexity into the data layer, we can decompose complex, imperative pipelines into simpler, more reliable workflows

We’ll explore the design principles behind this approach, including schema mapping and evolution between Kafka and Iceberg, and how to build a system that can continuously materialize and optimize hundreds of thousands of topics as Iceberg tables.

Whether you're building new pipelines or modernizing legacy systems, this session will provide practical patterns and strategies for creating resilient, scalable, and future-proof data architectures.

In this session, we will explore how organisations can leverage ArcGIS to analyse spatial data within their data platforms, such as Databricks and Microsoft Fabric. We will discuss the importance of spatial data and its impact on decision-making processes. The session will cover various aspects, including the ingestion of streaming data using ArcGIS Velocity, the processing and management of large volumes of spatial data with ArcGIS GeoAnalytics for Microsoft Fabric, and the use of ArcGIS for visualisation and advanced analytics with GeoAI. Join us to discover how these tools can provide actionable insights and enhance operational efficiency.

In this short presentation, Big Data LDN Conference Chairman and Europe’s leading IT Industry Analyst in Data Management and Analytics, Mike Ferguson, will welcome everyone to Big Data LDN 2025. He will also summarise where companies are in data, analytics and AI in 2025, what the key challenges and trends are, how are these trends impacting on how companies build a data-driven enterprise and where you can find out more about these at the show.