Driving the Enterprise Data Highway: Why McDonald's Ditched Self Managed Kafka

As enterprises scale globally, self-managed streaming platforms often struggle with operational complexity, cost, and real-time data demands. In this session, we share how McDonald’s modernized its data streaming platform by migrating over 200+ workloads and 2,000+ topics across multiple kafka clusters processing over 300 million events/day (~25 TB/day) to a fully managed, cloud-native streaming service as part of a broader GCP transformation.

Beyond the migration effort, we established a centralized Enterprise Data Highway as our foundation; designed to connect 40,000+ edge locations, digital channels, and IoT devices enabling real-time, event-driven data streaming across the enterprise where the data is always served fresh and fast while laying the groundwork for McDonald’s NEXT and proving the future of innovation is something we’re all lovin’.

This session will highlight key architectural patterns, including bi-directional data replication, hybrid public/private cluster strategies, large-scale schema governance, future proofing, and robust edge-to-cloud synchronization with built-in offline resilience.

Relevance to Real-World Data Streaming:

Many enterprises face similar challenges: fragmented kafka deployments, high operational overhead, limited scalability, and difficulty enabling real-time use cases across distributed environments.

This session provides insights on how to tackle these challenges:

** How to transition from self-managed to fully managed Kafka without disrupting business operations at scale

** How to design secure, multi-region streaming architectures with hybrid connectivity

** How to support edge-to-cloud patterns at massive global scale with unstable network conditions using NATS connectivity to Confluent Cloud

** How to move from the Wild Wild West to an established governed, reliable data streaming platforms that enable enterprise-wide reuse and making it future ready for Agentic consumption and predictive Inferences

Audience Takeaways:

This session provides actionable insights for architects and platform teams looking to reduce operational overhead, execute complex and incremental migration with no impact to business continuity. Also focuses on concrete architectural and operational patterns including:

** Enabling an Enterprise Data Highway, migrating existing topics from MSK to Confluent Cloud using replicators, cluster linking and on-demand bi-directional sync

** Cluster isolation strategies (public/private) to enforce security, tenancy, and data segmentation

** Schema governance models for enforcing compatibility and evolution across domains at scale, cost optimization using compression to move data efficiently

** Edge-to-cloud synchronization patterns, including buffering and recovery mechanisms for intermittent connectivity

** Incremental migration approach with on-demand bi-directional sync, zero-downtime cutover strategy for idempotent and non-idempotent consumers.