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Architecture Overview
The proposed system adopts a micro‑services architecture, partitioning the workload into independent services: an Ingestion Manager, a Validation Service, a Data Storage Service, a Web‑Mosaic Tiling Service, a Search and Analytics Service, and a Security Gateway. Each service exposes a RESTful API secured with OAuth 2.0 bearer tokens. The Ingestion Manager is the single entry point for user uploads, coordinating the orchestration of downstream services via a message queue (RabbitMQ). This design decouples ingestion from validation and storage, allowing each component to scale independently and enabling straightforward replacement or augmentation of individual services.
API Design and Authentication
The Ingestion Manager accepts file uploads through a multipart/form‑data endpoint that streams data directly to the message broker, avoiding intermediate local disk usage. The Validation Service consumes the queued payload, runs a predefined schema check against the data (e.g., GeoTIFF or HDF5), and emits a status back to the Ingestion Manager. A status endpoint on the Ingestion Manager allows clients to poll for completion, ensuring a non‑blocking user experience. All services are wrapped behind an OAuth 2.0 bearer token, validated against an external Keycloak server before any request is processed.
Data Ingestion Pipeline
Once validation passes, the validated payload is persisted in a persistent storage layer (Amazon S3, Google Cloud Storage, or a Ceph cluster). Metadata describing the dataset - such as spatial bounds, temporal range, and checksum - are recorded in a PostgreSQL database for quick lookups. The ingestion queue is then signaled, triggering a stateless processing worker to begin on‑the‑fly tiling or 3D stitching as requested. This pipeline supports both 2D and 3D data, allowing users to specify a chain of transformations (e.g., band‑wise scaling, Fourier filtering, or deconvolution) before tile generation.
Validation and Storage
The Validation Service performs two types of checks: a schema validation that ensures the dataset matches the expected format (resolution, CRS, data type) and a content validation that verifies the data integrity by recomputing checksums. If the data fails validation, the service returns a detailed error payload; otherwise it updates the PostgreSQL record and publishes a message to the processing queue. The Data Storage Service is built around an abstraction layer that supports both raw array access and tiled representation. When a new dataset is persisted, the storage service writes the full‑resolution array to the object store and simultaneously generates a Web‑Mosaic compliant tile set in a separate directory for deep‑zoom visualization.
On‑the‑Fly Tiling
The tiling service is designed to be stateless; it reads a source array from the storage layer, applies any requested filter chain, and writes each tile back to the storage layer in the Web‑Mosaic format. This approach eliminates the need for storing full‑resolution copies locally, thereby reducing storage costs and accelerating visual inspection. Because the tiles are written directly to a CDN‑backed bucket, the tiles can be served via a standard CDN edge network, resulting in sub‑second load times for any zoom level. The tiling service also provides a health check endpoint that reports the status of its local GPU and the throughput of the processing queue, enabling automated self‑healing policies in the cluster.
Security and Privacy
The Security Gateway implements OAuth 2.0 bearer token validation and integrates with an external Keycloak server for SSO. Role‑based permissions are encoded as JSON Web Tokens that list the allowed namespaces and operations per user, which the gateway verifies before routing any request. To address privacy requirements, all ingesting endpoints enforce access policies that mask personally identifiable information (PII) and provide differential privacy guarantees for shared aggregate statistics. Moreover, the platform is fully auditable: each operation logs the actor, resource, action, and timestamp to a central audit store, allowing retrospective compliance checks against GDPR or HIPAA regulations.
Observability and Monitoring
Observability is baked into every component by emitting structured logs in JSON format to a Loki backend and pushing Prometheus metrics on standard /metrics endpoints. A dedicated Grafana dashboard aggregates CPU, memory, network, and storage utilization, while the search service visualizes query latency distributions. For alerting, the system uses Alertmanager with templated rules that trigger notifications to a Slack channel or PagerDuty incident when thresholds are breached. The message broker also exposes metrics that can be consumed to ensure message throughput remains within acceptable limits.
Deployment and CI/CD
The entire stack is containerized with Docker and orchestrated via Kubernetes, which allows fine‑grained autoscaling of each micro‑service. CI/CD pipelines are defined with GitHub Actions, which build and push images to Docker Hub, run integration tests, and deploy the updated images to a Kubernetes cluster. Helm charts are used for resource definition and versioning, ensuring that any rollout can be performed in a controlled, declarative manner. The deployment is also configured to use an automated rolling update strategy, guaranteeing zero‑downtime during service upgrades.
Data Versioning and Reproducibility
For reproducibility, the system tracks data provenance by assigning a unique version ID to each ingested array. These IDs are stored in the metadata of the storage objects and can be retrieved via the API to enable exact re‑creation of analysis pipelines. The API also supports data lineage queries, returning the chain of transformations applied to a given dataset, which is invaluable for debugging, reporting, and audit trails. By decoupling data versioning from storage, teams can maintain an immutable audit trail while still enabling flexible, on‑the‑fly transformations for end users.
Deployment and CI/CD
The resulting architecture supports rapid data ingestion, reproducible analysis, and interactive visualization, enabling scientific teams to publish datasets on public portals like Zenodo or institutional repositories while maintaining compliance with institutional review boards. Users can generate high‑quality Web‑Mosaic tiles for embedding in web applications or static sites, and the API layer guarantees backward compatibility for downstream clients. The open‑source nature of the stack encourages community contributions, and the modular design allows teams to swap storage backends (S3, Ceph, or GCS) without touching the ingestion or processing logic.
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