Leveraging Snowflake for Data Lake Solutions

Example answer

Choose the integration pattern according to data ownership, format, freshness, workload and total cost. Snowflake can query supported data in a lake or load selected data into managed analytical tables; neither option is universally cheaper.

External tables register file-level metadata for supported files in an external stage and allow SQL queries without first loading those files into a standard Snowflake table. They are read-only and need metadata refresh as the file set changes. Data still travels from object storage to the query engine, so “no copy into a managed table” does not mean zero data transfer or zero compute cost. Check file-format support rather than assuming every semi-structured format works.

For an Iceberg lake, choose the appropriate catalog integration and table-management model. Snowflake-managed and externally managed Iceberg tables have different lifecycle responsibilities and feature limitations. Verify supported read/write behavior, catalog coordination, refresh and maintenance for the selected configuration; do not treat an external table and an Iceberg table as interchangeable.

A practical design might query infrequent raw datasets in place and load a validated, frequently queried subset into curated tables. Preserve source identities, schema/version information and deletion/correction semantics. Apply cloud-storage permissions as well as Snowflake policies, and verify sharing/export boundaries.

Compare correctness, freshness, query latency and total storage, compute, request and transfer costs on representative data. Include failures such as missing notifications, stale metadata and schema changes. Add concurrency capacity only when measured demand warrants it; scaling features do not guarantee unlimited throughput.

Sources

External tables and refresh; Iceberg catalog options and limits.