Snowflake is a leading cloud data warehouse and transitions into a data cloud that enables various use cases. The major drawback of this evolution is the significantly growing cost of the data processing. This blog post explores how data streaming with Apache Kafka and Apache Flink enables a “shift left architecture” where business teams can reduce cost, provide better data quality, and process data more efficiently. The real-time capabilities and unification of transactional and analytical workloads using Apache Iceberg’s open table format enable new use cases and a best of breed approach without a vendor lock-in and the choice of various analytical query engines like Dremio, Starburst, Databricks, Amazon Athena, Google BigQuery, or Apache Flink.
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.
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