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NIKA GeoEngine, previously called NIKA Worker, is geoprocessing orchestration for your own Kubernetes cluster. Bring Python or R workers; NIKA GeoEngine runs them on the right machine, fans big jobs out in parallel, keeps sensitive analyses to cleared roles and measures every run.
Two complementary products, one worker format. GeoEngine CLI builds and tests workers on a laptop. NIKA GeoEngine runs the same workers at scale in your Kubernetes cluster.

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Why geoprocessing breaks ordinary clusters

Rasters outgrow fixed servers

A 96 GB raster overflows a 64 GB server but fits a 128 GB high-memory workerSize each worker to the job. No 64 GB ceiling.

Hundreds of scenes, one at a time

Scenes processed one at a time versus fanned out in parallelOne batch job fans out to parallel pods: 200 × 15 min becomes ~75 min on 40 pods.

GDAL, CUDA and Python packages keep clashing

Conflicting GDAL, CUDA and Python package versions snapping into one managed runtimePyTorch, rasterio, numpy and GDAL pin each other’s versions. One managed geo runtime under every worker, with your extra packages resolved against it.

Not everyone should run everything

Cleared analysts pass the gate to a sensitive worker; everyone else is blockedRestricted workers run only for roles you clear.
NIKA GeoEngine takes on all four, so your engineers write processing code, not plumbing.

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Architecture at a glance

A control plane decides who may run what, where and when. An execution plane of pods does the work. Both run in your cluster, against storage you own.
NIKA GeoEngine architecture: clients call the gateway; access control checks roles against preset, custom and restricted workers; the orchestrator places tasks on CPU, high-memory and GPU pools that share storage

Solid arrows carry requests and work. Dashed arrows carry roles, versions, metrics and scaling signals. Boxes are functional roles, not an inventory of pods.

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Three kinds of worker, one gate

Every worker is preset (ready-made by NIKA), your own Python or R, or restricted: a sensitive analysis that only the roles you clear can run.
Cleared analysts can run every worker; all other users can run the open workers and are blocked from restricted ones

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What you get

Fan-out and autoscaling

One request, many parallel tasks. Nodes follow the queue.

No memory ceiling

CPU, high-memory or GPU, sized per worker.

GPU-ready, no setup

CUDA and GDAL managed for you.

Role-based access

Sensitive workers stay with cleared roles.

Every run measured

Runtime, CPU, GPU and RAM. Right-size from evidence.

Plugs into your stack

Your pipelines and scripts, FME Flow, imagery servers, any HTTPS client.

AI-ready

Workers are plain Python or R, which AI assistants write well.

Months saved

Skip building and maintaining a geo platform on Kubernetes.

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Concepts

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The life of a job

Seven steps in the life of a job: submit, authorise, accept and fan out, place, run, write results, record
  • Access is checked before capacity is spent. A denied request never creates a pod.
  • Tasks are independent. One failed scene does not stop the others, and results are written per task.
  • Capacity is elastic but bounded by the limits you set on each pool.

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Where it fits in your stack

Pipeline tools decide what runs after what. NIKA GeoEngine runs each heavy step on the right machine, for the right people, and writes results that maps and imagery servers read directly.
Map apps, FME Flow, your pipelines and scripts submit work to NIKA GeoEngine; it writes results to storage that imagery servers and maps read; everything runs on Kubernetes

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Is it right for you?

A good fit

  • Raster pipelines and imagery analysis
  • Jobs that need GPUs or more memory than general nodes have
  • Many scenes, same logic
  • Data that must stay in your cluster, with some analyses limited to certain roles

Probably not

  • One small script that runs fine on a laptop
  • General ETL or ML-lifecycle needs with no geoprocessing
  • No Kubernetes cluster to run it on

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Next steps

Deploy and operate

Licence, prepare the cluster, install, verify, scale.

Workers and jobs

Build workers, run batches, restrict access.

Observability

See what each run used, then right-size it.

Integrations

Your pipelines, FME Flow, ArcGIS Image Server.

Build fullstack map processing with OnlyMapJS

A map front end on a geoprocessing backend.