Use DuckDB with the httpfs extension as a lightweight transformation engine over Parquet files in S3
domain: duckdb.org · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Run INSTALL httpfs; LOAD httpfs; once per DuckDB environment to enable S3-compatible object storage access.
Create a secret for S3 credentials with CREATE SECRET (TYPE s3, KEY_ID '<key>', SECRET '<secret>', REGION '<region>'), or use PROVIDER credential_chain to pick up credentials automatically from the environment or an IAM role.
Query Parquet files directly from S3 with SELECT * FROM read_parquet('s3://bucket/path/*.parquet'), including reading a glob of multiple files as a single relation.
Run transformation SQL (joins, aggregations, filters) directly against the S3-backed Parquet relations and write results back out with COPY ... TO 's3://bucket/output.parquet' (FORMAT PARQUET).
Embed this pattern as a lightweight pipeline step (e.g. a scheduled script or orchestrator task) for ad hoc or small-to-medium transformation jobs that don't warrant a full Spark or warehouse job.
Known gotchas
DuckDB executes locally (single-node); very large S3 datasets that exceed available memory/disk on the executing machine won't scale the way a distributed engine would, even though pushdown-friendly Parquet reads can still be efficient.
CREATE SECRET credentials are process/session-scoped by default (or persisted to disk depending on secret persistence settings); treat persisted secrets on shared machines as a credential-exposure risk.
httpfs must be explicitly installed and loaded in every fresh DuckDB session/process; forgetting LOAD httpfs after a prior INSTALL results in read_parquet('s3://...') failing.
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