Modern Data Architectures

In a world powered by data, crafting the right architecture has never been more critical—or more complex. With an ever-growing arsenal of tools and solutions, designing a robust, future-ready data architecture requires not just expertise, but pragmatism and foresight. The challenge? Building scalable systems that meet the demands of today while anticipating the needs of tomorrow—especially when AI and machine learning enter the mix.

As companies embrace Data Mesh architectures to decentralize data ownership, they face a cascade of hurdles: scaling infrastructure, integrating real-time data streams, ensuring data governance, and automating agile data pipelines—all while keeping costs in check. The race to innovate is on, and the winners will be those who can turn these challenges into opportunities.

Join us at our Modern Data Architectures track, where industry experts will share the actionable insights, cutting-edge strategies, and real-world case studies you need to navigate this evolving landscape. Whether you're leading your company's data transformation or fine-tuning your existing architecture, this is your chance to discover the best practices in Modern Data Architecture and becoming best equipped to design a future proof data application.

From this track

Session Data Architecture

Reliable Data Flows and Scalable Platforms: Tackling Key Data Challenges

Wednesday Apr 9 / 10:35AM BST

There are a few common and mostly well-known challenges when architecting for data. For example, many data teams struggle to move data in a stable and reliable way from operational systems to analytics systems.

Speaker image - Matthias Niehoff

Matthias Niehoff

Head of Data and Data Architecture @codecentric AG, iSAQB Certified Professional for Software Architecture

Session

Achieving Precision in AI: Retrieving the Right Data Using AI Agents

Wednesday Apr 9 / 11:45AM BST

In the race to harness the power of generative AI, organizations are discovering a hidden challenge: precision.

Speaker image - Adi Polak

Adi Polak

Director, Advocacy and Developer Experience Engineering @Confluent, Author of "Scaling Machine Learning with Spark" and "High Performance Spark 2nd Edition"

Session Data engineering

Building a Global Scale Data Platform with Cloud-Native Tools

Wednesday Apr 9 / 01:35PM BST

As businesses increasingly operate in hybrid and multi-cloud environments, managing data across these complex setups presents unique challenges and opportunities. This presentation provides a comprehensive guide to building a global-scale data platform using cloud-native tools.

Speaker image - George Hantzaras

George Hantzaras

Engineering Director, Core Platforms @MongoDB, Open Source Ambassador, Published Author

Session

The Data Backbone of LLM Systems

Wednesday Apr 9 / 02:45PM BST

Any LLM application has four dimensions you must carefully engineer: the code, data, models and prompts. Each dimension influences the other. That's why you must learn how to track and manage each. The trick is that every dimension has particularities requiring unique strategies and tooling.

Speaker image - Paul-Emil Iusztin

Paul-Emil Iusztin

Senior ML/AI Engineer, MLOps, Founder @Decoding ML

Session Data Architecture

Beyond the Warehouse: Why BigQuery Alone Won’t Solve Your Data Problems

Wednesday Apr 9 / 03:55PM BST

Many organizations mistake the adoption of a data warehouse, like BigQuery, as the golden ticket to solving all their data challenges. But without a robust data strategy and architecture, you’re simply shifting chaos into the cloud.

Speaker image - Sarah Usher

Sarah Usher

Data & Backend Engineer, Community Director, Mentor

Date

Wednesday Apr 9 / 10:35AM BST

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Track Host

Fabiane Nardon

Data Expert, Java Champion & Data Platform Director @totvs

Fabiane is a seasoned computer scientist with years of experience building data-intensive solutions that have made a tangible impact across various industries. She was the chief architect of the Sao Paulo Healthcare Information System project, which was then considered the world’s largest JavaEE application and won the 2005 Duke’s Choice Award for its innovation. At Tail, where she served as partner and CTO, she helped pioneer the use of data science and machine learning in digital advertising.

Fabiane has been an active contributor to the open-source community, having led the JavaTools Community at java.net, where over 800+ projects were created. A frequent speaker at major tech conferences, she is well-regarded for sharing practical insights and real-world experience. She has also served on the program committees for conferences like JavaOne, TDC and QCon, helping to shape industry conversations.

Named a Java Champion by Sun Microsystems in recognition of her contributions to the Java ecosystem, Fabiane now works as the Data Platform Director at Totvs, Brazil’s largest tech company. Her current work focuses on simplifying the development of data-driven solutions, making it easier for organizations to harness the power of data to solve real-world challenges.

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