The analytical data warehouse engine typically used is Snowflake or BigQuery, which unlocks the shift of pulling business logic out of various ETL pipelines, queries, and scripts and centralizing it in SQL in a clean, version-controlled git repo managed by dbt. Materialize is the operational complement to this approach, providing a streaming computation model that allows for real-time automation, engaging customer experiences, and new operational data products. To compute results continuously, Apache Kafka is often used, but its industry-standard approach comes with significant management requirements. WarpStream is an alternative that runs directly on top of commodity object stores like S3, GCP GCS, or Azure Blob Storage, incurring zero inter-AZ bandwidth costs and having no local disks to manage. It implements the Apache Kafka protocol but separates compute from storage and offloads scaling and maintenance operations to cloud provider object storage. This results in a significantly more cost-effective and operationally burden-free solution that can be easily adopted by deploying stateless containers like Nginx into infrastructure. WarpStream's "Bring Your Own Cloud" deployment model enables seamless integration with Materialize, allowing for real-time streaming SQL aggregations and automated data maintenance.