Snowflake is a leading cloud-native data warehouse. Integration patterns include batch data integration, Zero ETL, and near real-time data ingestion with Apache Kafka. This blog post explores the different approaches and discovers their trade-offs. Following industry recommendations, it is suggested to avoid anti-patterns like Reverse ETL and instead use data streaming to enhance the flexibility, scalability, and maintainability of enterprise architecture.

Blog Series: Snowflake and Apache Kafka

Snowflake is a leading cloud-native data warehouse. Its usability and scalability made it a prevalent data platform in thousands of companies. This blog series explores different data integration and ingestion options, including traditional ETL/iPaaS and data streaming with Apache Kafka. The discussion covers why point-to-point Zero-ETL is only a short-term win, why Reverse ETL is an anti-pattern for real-time use cases, and when a Kappa Architecture and shifting data processing “to the left” into the streaming layer helps to build transactional and analytical real-time and batch use cases in a reliable and cost-efficient way.