Observed Signal · Apr 4, 2026 · Technical Implementation · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
Lightweight AWS Lambda ETL with DuckDB and Snowflake
An AWS Community Builder implemented an event-driven ETL pattern that uses DuckDB inside AWS Lambda to perform SQL-based, in-memory transformations on Parquet files in Amazon S3 and then load the processed data into Snowflake via the Snowflake Python Connector. The post explains why Snowpipe is insufficient for more complex preprocessing and shows sample code that filters rows before uploading. It documents a critical limitation: snowflake.connector.pandas_tools.write_pandas fails when targeting a Snowflake Catalog-Linked Database (Iceberg) because the function creates a temporary stage internally, and Catalog-Linked Databases disallow creating such Snowflake objects. Workarounds demonstrated include using direct INSERT statements in chunks or creating a stage in a different database and routing the load through it.
Practical implementation and a documented connector limitation are useful for data engineers building low-cost, event-driven lakehouse ETL pipelines; the Snowflake Iceberg (catalog-linked) write restriction may affect architecture and operational choices.
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Key Takeaways & Evidence Grounding
- Implemented an event-driven ETL pipeline using AWS Lambda + DuckDB to read Parquet from Amazon S3, transform data, and write to Snowflake via the Snowflake Python Connector.
- Sample code performs SQL filtering in DuckDB (example: WHERE VendorID = 1) and converts Arrow results to pandas before uploading.
- snowflake.connector.pandas_tools.write_pandas fails when writing to a Catalog-Linked Database (Iceberg) because it creates a temporary stage, which Catalog-Linked Databases do not allow.
- Workarounds include using batched INSERT statements or creating a stage in a different database and executing the insert from there.
- Article argues Snowpipe is convenient for ingestion but insufficient for cases needing preprocessing, complex filtering, or combining multiple events.
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