Combining Kafka with dbt creates a powerful event-driven architecture for comprehensive data processing and analytics.

Event streaming foundation:

• Kafka streams real-time events from various sources including Snowplow trackers, applications, and IoT devices
• Provides reliable, scalable event delivery to multiple downstream consumers
• Enables real-time and batch processing patterns within the same architecture

Stream processing layer:

• Use Kafka Streams or Apache Flink to process event data in real-time
• Apply enrichments, transformations, and aggregations as events flow through the pipeline
• Implement complex event processing for behavioral analytics and real-time insights

Data transformation with dbt:

• Use dbt to model and transform data within your data warehouse after ingestion via Kafka
• Create analytics-ready datasets from raw event data for business intelligence and reporting
• Implement data quality testing and documentation as part of the transformation process

End-to-end orchestration:

• Combine Kafka and dbt to enable comprehensive event-driven pipelines from ingestion to insights
• Support both real-time streaming analytics and batch analytical processing
• Enable data teams to build reliable, scalable analytics infrastructure using modern data stack principles