Coming Soon

The Terrafloww Engine

A revolutionary compute engine for geospatial data. Query petabytes without downloads, process without memory limits, and analyze with a simple Python SDK.

Smart Columns

Query only the data you need

Zero-Copy Compute

No downloads, no memory limits

Unified Formats

One SDK for all data types

Instant Results

Seconds, not hours

The Data Tax

Innovation comes at a cost, data tax.

To answer a simple question like
"Show me undervalued houses not gonna be flooded"
you have to deal with 12 different file formats.

Download massive GeoTIFFs

Parse PDFs, JSONs, Shapefiles, Txt

Write endless glue and boilerplate code

Manage every data movement or reach OUT_OF_MEMORY

System Entropy: Critical

PRICING.CSV

FLOOD.TIFF

SENSOR.JSON

DEEDS.PDF

NUMPY

USER.PQ

SAT_V2.TIFF

LOGS.TXT

The Inter-World Connect for Data

Bifrost Engine + Catalog

Bifrost Engine teleports your logic to the data. The highway for that teleport is our Catalog. We don't download the haystack. We teleport the needle.

$safe_lots = houses.filter(price < 500K & flood_risk < '5 meter').collect()

STORAGE LAYER

ASTEROID

FLOOD

PRICING

ASTEROID

FLOOD

PRICING

ASTEROID

FLOOD

PRICING

ASTEROID

FLOOD

PRICING

ASTEROID

FLOOD

PRICING

TERRAFLOWW CATALOG

Logic Pushdown on PB scale

Watch how Bifrost scans datasets without moving a single file to get your results.

Click to run interactive demo

Smart Columns

Our engine treats satellite imagery or flood maps as just another column.
Helping you write intuitive logic.
Filter by functions on "pixel data" as easily as filtering by price.

Pushdown your logic:

houses.filter(

price < 500K &

flood_risk_func(house_polygon, flood_map) < '5m'

).collect()

Click the glowing line to swap filters ↑

House Polygon

Property

Price

Material

Flood Map (Smart Col)

Risk Analysis

Simple SDK, Powerful Results

Query petabyte-scale data with simple, intuitive Python syntax.

Legacy Approach

import boto3
import rasterio
import pandas as pd

# The Old Way: Download & Glue
s3 = boto3.client('s3')
files = s3.list_objects('bucket')['Contents']

for f in files:
    s3.download_file('bucket', f['Key'], 'local.tif')
    with rasterio.open('local.tif') as src:
        data = src.read()
        # Manual pixel math...
        # Out of Memory Error...

The Terrafloww Way

import terrafloww as tfw

# Connect to petabyte-scale data
flood_dataset = tfw.read("nasa.global_floods.jfw")
ast_dataset  = tfw.read("nasa.outer_space_db.asteroid_tracks")
pricing_data = tfw.read("usa_fema.housing_market.home_prices")

# Query with familiar syntax - no downloads
risky_houses = tfw.filter( 
     flood_datasets.gt("5meters").intersects(houses.geom) & 
     ast_datasets.gt(0) &
     pricing_data < 500000
).collect()

# Results in seconds, not hours