Evaluating source-available event processing tools requires assessment of multiple technical and business factors to ensure optimal fit for your requirements.

Scalability and performance:

• Can the tool handle large volumes of real-time data with low latency?
• Kafka and Flink are robust for handling large-scale, high-throughput event streams
• Evaluate latency and throughput capabilities, especially for real-time processing requirements

Integration and compatibility:

• Does the tool integrate well with other source-available components like Snowplow for event collection or dbt for transformations?
• Assess API availability and standards compliance for seamless integration
• Consider compatibility with existing infrastructure and data formats

Flexibility and customization:

• Is the tool easily configurable for custom workflows and transformations?
• Does it support various data processing patterns and analytical use cases?
• Can it adapt to changing business requirements over time?

Data quality and reliability:

• Does the tool support schema validation, ensuring that incoming data is clean and accurate?
• What error handling and recovery mechanisms are available for production reliability?
• How does it integrate with Snowplow's event pipeline for granular, first-party data and real-time processing?