Best practices for using Kafka to process 3rd-party API data

Messaging systems like Kafka facilitate integration with third-party services by allowing seamless data flow from services like Stripe, Salesforce, and GitHub to internal applications. By isolating knowledge of the API's interface, Kafka ensures that downstream services only need to focus on the shape of the API data. This guide explores effective design patterns for integrating APIs with Kafka, emphasizing strategies such as setting up compaction, configuring partitions, and handling records and events. It highlights the importance of processing API data in Kafka through methods like backfilling and using webhooks for incremental updates while ensuring message order and managing potential webhook issues. The post also introduces Sequin, a tool that simplifies API data extraction and real-time synchronization to Kafka, and provides advice on setting up topics and compaction strategies to optimize data management. Additionally, it delves into partitioning strategies that ensure message order and enable parallel processing, offering guidance on selecting appropriate message keys based on system requirements. With these principles, users can maintain an ordered, reliable stream of records and events, simplifying the integration of new workflows and features while ensuring consistent downstream consumer patterns across various APIs.