At Gloo.us, we face a challenge in providing platform data to heterogeneous applications in a way that eliminates access contention, avoids high latency ETLs, and ensures consistency for many teams. We're solving this problem by adopting Data Mesh principles and leveraging Kafka, Kafka Connect, and Kafka streams to build an event driven architecture to connect applications to the data they need. A domain driven design keeps the boundaries between specialized process domains and singularly focused data domains clear, distinct, and disciplined. Applying the principles of a Data Mesh, process domains assume the responsibility of transforming, enriching, or aggregating data rather than relying on these changes at the source of truth -- the data domains. Architecturally, we've broken centralized big data lakes into smaller data stores that can be consumed into storage managed by process domains. This session covers how we’re applying Kafka tools to enable our data mesh architecture. This includes how we interpret and apply the data mesh paradigm, the role of Kafka as the backbone for a mesh of connectivity, the role of Kafka Connect to generate and consume data events, and the use of KSQL to perform minor transformations for consumers.