Nick is a data architect with a strong background in handling enterprise data and machine learning, specializing in leveraging cloud technologies to architect scalable and innovative solutions. Proud for a track record of success driven by a focus on using modern technologies while making sure they are proven and fit for purpose. Lately focusing on cloud-native databases, and helping organization change the way they view data.
Data Mesh has transformed how organizations manage analytical data at scale, yet many struggle with the practicalities of implementation—from ensuring organization buy-in and creating efficient team structures, to tooling and architectural choices. This session distills real-world lessons from a successful Data Mesh rollout, providing actionable insights on teams structure, technologies selection, and designing a scalable cloud-based system.
We’ll explore the core principles—treating data as a product and decentralized ownership—while diving into key architectural decisions implementing them, such as designing interoperable data products, managing metadata, ensuring governance at scale, and integrating well known technologies like Databricks, Airflow, and Great Expectations.
Beyond architecture, we’ll also address common concerns: How do you empower domain teams while maintaining ecosystem cohesion? What roles and responsibilities are essential for a successful Data Mesh? How do you balance decentralization with shared standards for security, interoperability, and quality?
This is not just theory—expect real-world strategies, best practices, and tooling recommendations to help an organization navigate the complexities of Data Mesh. The session will wrap up with an interactive Q&A, offering deeper discussions and shared learning.
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