FAQ

What’s the difference between RaQuet and COG?

COG (Cloud Optimized GeoTIFF) and RaQuet serve different layers of the data stack.

COG is ideal for classic raster access patterns: window reads, visualization, GDAL pipelines, and serving tiles to web maps.

RaQuet targets a different problem: making raster data queryable and governable inside data warehouses and lakehouses using Parquet.

What raster formats can I convert to RaQuet?

RaQuet supports any GDAL-readable raster format:

• GeoTIFF / Cloud Optimized GeoTIFF (COG)
• NetCDF (with CF time dimension support — adds time_cf and time_ts columns)
• AAIGrid (Esri ASCII Grid)
• ArcGIS ImageServer (via raquet-io convert imageserver)
• And many more GDAL formats

Is there a size limit?

RaQuet can handle rasters of any size. For very large datasets, consider using raquet-io split-zoom to create separate files per zoom level for optimal query performance.

How does compression compare?

RaQuet typically achieves significant compression compared to ASCII formats (AAIGrid) and often matches or beats NetCDF:

Why Web Mercator?

RaQuet requires data to be in Web Mercator (EPSG:3857) projection. This is a deliberate design choice that enables:

1. QUADBIN spatial indexing — A Discrete Global Grid System (DGGS) that provides hierarchical, efficient spatial lookups
2. Row group pruning — Parquet readers can skip entire row groups based on spatial predicates
3. Consistent tiling — Same tile boundaries as web map standards (XYZ/TMS)

The converter automatically reprojects data to Web Mercator during conversion.

Can I query RaQuet without the DuckDB extension?

Yes! RaQuet files are standard Parquet files. You can query them with any Parquet-compatible tool, but you need to manually filter out the metadata row (block = 0):

-- Basic Parquet query (no extension, manual metadata filtering)
SELECT *
FROM read_parquet('https://storage.googleapis.com/raquet_demo_data/world_elevation.parquet')
WHERE block != 0
LIMIT 10;

-- Get metadata
SELECT metadata
FROM read_parquet('file.parquet')
WHERE block = 0;

However, the DuckDB Raquet Extension provides a cleaner API that handles metadata automatically, plus spatial raster functions:

INSTALL raquet FROM community;
LOAD raquet;

-- Cleaner API (metadata row excluded automatically)
SELECT * FROM read_raquet('file.parquet') LIMIT 10;

-- Get metadata
SELECT metadata FROM read_raquet_metadata('file.parquet');

-- Plus spatial functions like ST_RasterValue, ST_RasterIntersects, etc.

How do I visualize RaQuet files?

Several options:

1. RaQuet Viewer — Browser-based viewer using HTTP range requests (no server needed)
2. Export to GeoTIFF — raquet-io export geotiff input.parquet output.tif
3. Load in CARTO — Upload to CARTO for visualization and analysis

What’s the metadata format?

RaQuet v0.5.0 stores metadata as JSON in the block=0 row:

{
  "file_format": "raquet",
  "version": "0.5.0",
  "width": 32768,
  "height": 14848,
  "crs": "EPSG:3857",
  "bounds": [-180, -60, 180, 85],
  "bounds_crs": "EPSG:4326",
  "tiling": {
    "scheme": "quadbin",
    "block_width": 256,
    "block_height": 256,
    "min_zoom": 0,
    "max_zoom": 7,
    "pixel_zoom": 15,
    "num_blocks": 7424
  },
  "compression": "gzip",
  "bands": [{
    "name": "band_1",
    "type": "float32",
    "nodata": null,
    "STATISTICS_MINIMUM": 0,
    "STATISTICS_MAXIMUM": 8848,
    "STATISTICS_MEAN": 234.5,
    "STATISTICS_STDDEV": 456.7
  }]
}

See the format specification for full details.

Does RaQuet support time series?

Yes! When converting NetCDF files with CF time dimensions, RaQuet adds:

• time_cf — CF time value (numeric offset from reference date)
• time_ts — Standard timestamp for easy SQL filtering

SELECT
    YEAR(time_ts) AS year,
    AVG(ST_RasterSummaryStat(block, band_1, 'mean', metadata)) AS avg_value
FROM read_raquet('climate_data.parquet')
GROUP BY YEAR(time_ts);

How does RaQuet compare to PostGIS Raster?

RaQuet builds on the concepts pioneered by PostGIS Raster, but with key differences:

RaQuet is ideal when you need to query rasters outside PostgreSQL, join with data in warehouses, or process at scale.

Is RaQuet production-ready?

Yes. RaQuet is used in production at CARTO and is supported by the Analytics Toolbox across BigQuery, Snowflake, Databricks, and PostgreSQL.

The format specification is at v0.5.0. The format is stable for production use. v0.4.0 added interleaved band layout and lossy compression; v0.5.0 adds optional per-tile statistics columns for UDF-free analytics on data warehouses.

How do I contribute?

RaQuet is open source under the BSD-3-Clause license. Contributions are welcome!

• GitHub Repository
• Issue Tracker
• Format Specification