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Struggling with massive blobs clogging up your Cassandra Change Data Capture (CDC) pipeline? 😫 You're not alone. Processing large unstructured data like images, videos, or documents can saturate your Kafka topics, cripple consumers, and skyrocket your costs. But what if there was a better way? In this practical session from the Cassandra Track at Community Over Code 2025 in Minneapolis, Bernardo Botella Corbi unveils a powerful architectural pattern to solve this exact problem. Discover how to intelligently combine the speed of Apache Cassandra CDC, the reliability of Kafka, and the cost-effective scalability of object storage (like S3, GCS, or MinIO) to build a robust and efficient data pipeline. In This Talk, You'll Learn: The Core Problem: Why handling blobs directly within CDC and Kafka is inefficient and often leads to system failure. The "Claim Check" Pattern: A deep dive into this elegant solution where the large payload is offloaded to object storage, while a lightweight reference ("claim check") flows through Kafka. Step-by-Step Implementation: Explore the technical details of integrating these powerful open-source technologies. Performance & Cost Benefits: Understand how this approach drastically reduces network traffic, improves throughput, and lowers operational costs. Real-World Use Cases: See how this architecture unlocks new possibilities for event-driven systems, analytics, and ML pipelines that rely on large binary data. Whether you're a data engineer, architect, or a developer working with Cassandra, this talk will provide you with the blueprint to build more resilient and scalable systems. Stop letting large blobs dictate your architecture! 👍 If you find this video helpful, please like, share, and subscribe for more content on big data and distributed systems! 👇 Drop your questions or share your own experiences with Cassandra CDC in the comments below! #ApacheCassandra #ChangeDataCapture #Kafka #ObjectStorage #BigData #DataEngineering #Microservices #CommunityOverCode #DataArchitecture #S3