CLOUD-NATIVE GEOSPATIAL
Building scalable geospatial data infrastructure using modern cloud technologies, distributed computing, and optimized data formats.
DASK
Process terabytes of geospatial data with Dask - Python's library for parallel computing. Scale from your laptop to cloud clusters seamlessly.
- → Parallel processing of large raster and vector datasets
- → Integration with Xarray for multidimensional data
- → Dask-GeoPandas for distributed vector operations
- → Kubernetes-native deployments on cloud clusters
import dask.array as da
import dask_geopandas as dgd
raster = da.from_delayed(
delayed_reader("s3://bucket/data.tif"),
shape=(10000, 10000),
dtype='float32'
)
result = raster.mean().compute()
df = dgd.read_parquet("s3://bucket/*.parquet")
aggregated = df.groupby('region').sum() apiVersion: apps/v1
kind: Deployment
metadata:
name: dask-worker
spec:
replicas: 4
template:
spec:
containers:
- name: worker
image: geocrafter/dask-worker
resources:
requests:
memory: "16Gi"
cpu: "4" KUBERNETES
Deploy scalable geospatial processing pipelines on Kubernetes. Auto-scaling workers, GPU support, and cloud-native architecture.
- → Helm charts for Dask clusters on Kubernetes
- → GPU-accelerated processing with NVIDIA CUDA
- → Horizontal pod autoscaling based on workload
- → GitOps workflows with ArgoCD
S3-COMPATIBLE STORAGE
All geospatial formats optimized for cloud object storage. Direct access without data transfer.
OVH Object Storage
Primary cloud storage with lifecycle policies and intelligent tiering.
HETZNER Object Storage
Another cost effective S3 compatible solution.
RustFS Object Storage
The high performant S3 on-premise solution!
CLOUD-NATIVE FORMATS
Modern geospatial data formats designed for cloud storage and efficient access patterns.
Cloud Optimized GeoTIFF
Raster
GeoTIFF format optimized for HTTP range requests. Process only the data you need without downloading entire files.
Key Features
- • Lazy loading with HTTP GET range requests
- • Overviews for fast visualization
- • TIFF compression (DEFLATE, LZW, LZMA)
- • GeoTIFF with embedded georeferencing
GeoParquet
Vector
Apache Parquet format with GeoParquet metadata. Columnar storage for fast analytical queries on billions of features.
Key Features
- • Columnar storage for selective reads
- • Predicate pushdown for fast filtering
- • Zstd compression
- • Interoperable with BigQuery, DuckDB, GeoPandas
Zarr
Multidimensional
Chunked, compressed n-dimensional arrays. Perfect for time-series satellite data and scientific raster datasets.
Key Features
- • Variable chunk sizes per dimension
- • Multiple codecs (blosc, zstd, gzip)
- • Cloud-native with S3 backend
- • STAC metadata integration
Icechunk
Time-Series
Zarr-based format with transactional writes and versioning. Built for collaborative analysis of changing data.
Key Features
- • Snapshot isolation for concurrent reads
- • Group-based versioning
- • S3-optimized consensus protocol
- • Integrates with Xarray and Zarr-Python
STAC
SpatioTemporal Asset Catalog specification for standardized geospatial data discovery. Programmatic access to your data catalog.
- → OGC API - Features for catalog search
- → Item-level metadata for individual assets
- → Cloud-optimized item browsing
- → Integration with OpenDataCube and Pystac
{
"type": "Feature",
"stac_version": "1.0.0",
"id": "S2A_L2A_20240101",
"properties": {
"datetime": "2024-01-01T10:00:00Z"
},
"assets": {
"cog": {
"href": "s3://bucket/S2A.tif"
}
}
}