The article focuses on leveraging Apache Kafka and Apache Flink to optimize data processing costs within a Snowflake data warehouse environment. It argues that this approach, referred to as a 'shift left architecture', allows businesses to reduce expenses, enhance data quality, and increase processing efficiency.
The article highlights Snowflake's increasing cost as a major concern, especially as it transitions into a data cloud. The proposed solution directly tackles this issue.
A core concept is the 'shift left architecture', where data processing is moved earlier in the pipeline. This allows for more efficient data handling and reduced costs associated with processing large datasets in Snowflake.
The combination of Kafka, Flink, and Snowflake enables real-time capabilities and the unification of transactional and analytical workloads using Apache Iceberg. This facilitates new use cases and a best-of-breed approach without vendor lock-in.
The article is part of a larger series examining Snowflake data integration and ingestion options, including traditional ETL/iPaaS and data streaming using Kafka. The series further explores why point-to-point Zero-ETL and Reverse ETL might not be sustainable long-term solutions, while highlighting the benefits of a Kappa architecture.
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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