1. Data Processing Approach: Kafka is a distributed streaming platform designed to handle real-time data streams while Kapacitor is a data processing engine that enables real-time streaming data processing and monitoring. While Kafka focuses on data transportation between systems, Kapacitor focuses on processing, alerting, and monitoring of streaming data captured using tools like Kafka.

2. Architecture: Kafka follows a publish-subscribe architecture where producers write data to topics and consumers read from those topics, ensuring high throughput and fault tolerance. In contrast, Kapacitor utilizes a modular architecture with both stream and batch processing capabilities, allowing users to define complex data processing tasks using its processing nodes for real-time alerts and anomaly detection.

3. Use Cases: Kafka is primarily used for building real-time data pipelines and streaming applications such as log aggregation, monitoring, and tracking user activity. On the other hand, Kapacitor is commonly utilized for applications that require real-time event monitoring, anomaly detection, and alerting based on streaming data from various sources.

4. Data Transformation: Kafka facilitates the handling of large volumes of raw data by efficiently storing, replicating, and distributing data streams across clusters, ensuring fault tolerance and scalability. In contrast, Kapacitor enables data transformation through its rich library of functions and performs transformations on streaming data through its scripting language, allowing for real-time data processing and analysis.

5. Integration with Ecosystem: Kafka seamlessly integrates with several data storage systems like Hadoop, Spark, and Elasticsearch, making it a vital component in modern data architecture. Kapacitor integrates well with platforms like InfluxDB for time-series data storage and retrieval, enabling users to build end-to-end monitoring and alerting solutions for time-sensitive applications.

6. Scalability and Performance: Kafka is known for its high scalability and performance, capable of handling millions of messages per second and supporting thousands of partitions. In comparison, Kapacitor offers efficient distributed computing with horizontal scalability, allowing users to process high volumes of streaming data effectively.

In Summary, Kafka and Kapacitor differ in their primary focus on data transportation vs. processing, architecture, use cases, data transformation capabilities, integration with ecosystems, and scalability/performance.