Snowflake Integration Patterns: Apache Kafka vs. Zero ETL and Reverse ETL
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 its 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.
This is part one of a blog series:
- THIS POST: Snowflake Integration Patterns: Zero ETL and Reverse ETL…
