These are technology stack questions. I'm asking: When storing data, which format do you choose? In Snowflake, you can store data in native Snowflake format or Iceberg. In Databricks, you can store data in Delta Lake or Iceberg. Which format would you pick and why?

Choosing a storage format depends on query patterns, cost, and ecosystem integration. Snowflake’s native format is columnar, compressed, and automatically optimized for fast queries; it also supports zero‑copy cloning and time‑travel. Iceberg adds open‑source table format support with schema evolution and partition pruning, and it works well with both Spark and Hive. In Databricks, Delta Lake provides ACID transactions, schema enforcement, and efficient upserts, making it ideal for streaming and batch pipelines. Iceberg in Databricks offers similar benefits but with better compatibility across multiple engines. If the workload is primarily analytical and you want minimal management, Snowflake’s native format is the simplest. If you need cross‑engine compatibility or plan to use Spark for ETL, Delta Lake is preferable in Databricks. For hybrid scenarios, storing in Iceberg can give you the best of both worlds, but it requires additional tooling for performance tuning.