Polymarket Data in S3

• s3://your-bucket/polymarket/<year>/<month>/<day>/<market_type>/books, prices, metrics as Parquet (or CSV/JSON).
• A manifest per snapshot risks resolution: 250ms capture, full ladders, verified timestamps.
• Row-group statistics (min/max) in Parquet footers let DuckDB/ClickHouse/Athena prune whole files for a market-window query.
• Partition on market_type and date; cluster within files on (market, ts).

• Athena (GLUE catalog) or DuckDB/ClickHouse reading Parquet directly for point-in-time and settlement studies.
• Glue/partition projection: table schema over the same column names used everywhere (ts, bid, ask, mid, depths, vol).
• Stream/Redshift Spectrum when the lake must join to other internal data.
• Lifecycle: keep raw Parquet; derive hourly aggregates with a scheduled ETL.

S3 separates data ownership from API dependency: your lake holds the archive; the REST API keeps serving slices for apps.

Books, prices, and metrics come back over the same endpoints at the same 250ms resolution, so a dashboard needs one schema, not three.

For data-lake teams the pattern is identical to any vendor: verify checksums, store to object storage, build the catalog once.

Land objects at s3://bucket/polymarket/<date>/<market_type>/books.parquet (plus prices and metrics sibling keys) so Athena and DuckDB prune by partition without a catalog scan.

Write a manifest (per-day JSON with hash and row counts) as the ingest receipt; every pipeline step verifies against it before mutating downstream.

Checksum the Parquet footers against the manifest hash on every ETL run; a silent corruption in a 250ms lake is worse than a missing day, since it reads fine and wrong.

Bulk S3 delivery matches exactly this layout, so a data-lake team&#39;s catalog points at the archive with zero transform.

S3 delivery is the end state for data-lake teams: the archive lands as partitioned Parquet (or CSV/JSON) in your own bucket, and the catalog, Athena, or DuckDB just reads it — the vendor boundary is ownership, not format.

The honesty that matters is provenance: a manifest with checksums per batch is the receipt; lakes that skip it inherit silent 250ms corruption as a feature.

Because the archive&#39;s bulk delivery matches this layout exactly, a lake team&#39;s ingest is a one-step load rather than a transform project.

First lake test: one day of books as partitioned Parquet in a scratch bucket, a manifest with row counts, and an Athena SELECT that returns 34,560 rows for the 250ms day.

Add the checksum verification step before any downstream consumer; once the verify step is in the pipeline, scale to the full archive without renegotiating trust.

S3 delivery is the ownership model data-lake teams actually want: partitioned Parquet in your bucket, a checksummed manifest as receipt, and every reader from Athena to DuckDB speaking the same format with no transform project.

The provenance discipline — verify each batch against the manifest before downstream consumers touch it — is what separates a managed lake from a pile of files, and it is the one habit to build on day one.

Because the bulk delivery matches the standard partition layout, the catalog build is a one-step load; teams that point their playbook at the bucket stop re-implementing the archive entirely.

• API data guide
• Data download
• Order book data
• MCP server