In the post, the author discusses the importance of building a data stack that utilizes Iceberg as the core data storage layer and allows for flexibility in choosing the compute environment based on specific needs. Different use cases, such as BI reporting, batch data pipeline jobs, and external data customers, require different prioritizations, whether it’s speed, cost, or availability. The author emphasizes understanding unique use cases to tailor the approach accordingly and highlights the importance of reducing latency, managing costs, and considering data sensitivity. The architecture detailed in the post leverages Iceberg, Snowflake, DuckDB, and Cube to create a flexible, cost-effective, and innovative design.
https://blog.twingdata.com/p/building-an-open-data-pipeline-in