The headless data architecture is an emerging concept that separates data storage, management, optimization, and access from the services that process and query it. This allows for a single logical location to manage permissions, schema evolution, and table optimizations, making regulatory compliance simpler. A headless data architecture can encompass multiple data formats, including streams and tables, providing flexibility in choosing the format suitable for operational, analytical, or hybrid use cases. Apache Kafka is an open-source distributed event-driven streaming platform that has a headless data model since its inception, allowing producers to write about topics independently of consumers. The producer acts as a fully independent head, while consumers are also independent. To support full streaming capabilities, events require well-defined schemas and metadata catalogs. Tables can be integrated into the headless data architecture using Apache Iceberg, which provides table storage and optimization, catalog management, transactions, time travel capabilities, and pluggable data layers. The main benefits of a headless data architecture include saving money and time by not copying data around, eliminating similar-yet-different datasets, and choosing the most suitable processing engine for each use case. A headless data architecture differs from a data lake architecture in that any service can use the data, tables and streams are used interchangeably, and the data layer is modular and composed of different data sources. This architecture enables building data lakes and warehouses by plugging in Iceberg tables, allowing businesses to invest in their own headless data architectures and providing modularity, reusability, structure, and easy access to both streams and tables.