Open Source Software Library

Terraform module for per-environment Azure configuration in Statistics Canada Advanced Analytics Workspaces. It helps platform teams keep analytics workspace environments reproducible and easier to audit.

Terraform module that defines network components for Advanced Analytics Workspaces. It supports standardized deployment of secure analytics networking in Statistics Canada cloud environments.

Go utility that monitors Kubernetes jobs and terminates sidecars after the main job completes. It helps batch-processing workloads finish cleanly in service-mesh or sidecar-heavy environments.

Archived testing repository for containers used in Kubeflow-based Advanced Analytics Workspaces. It demonstrates build and test patterns for data-science images used by public-sector analytics teams.

Open-source extension marketplace for VS Code maintained in the Statistics Canada ecosystem. It provides infrastructure for discovering and distributing editor extensions in controlled or self-hosted environments.

Kubernetes component that injects tolerations for Advanced Analytics Workspace workloads. It helps data-science notebooks and jobs land on appropriate nodes while keeping scheduling policy centralized.

Terraform module for deploying kube-prometheus-stack into Kubernetes clusters. It gives platform teams a reusable way to install observability components for cloud-native analytics services.

Python anonymization package from Statistics Canada for privacy-protection workflows. It supports identification and treatment of sensitive information, helping analysts prepare safer datasets for sharing or processing.

Official Statistics Canada repository for Banff procedures, a modular statistical package for data editing and imputation. It supports production-quality statistical processing and modernization of survey data workflows.

Metadata-driven tool for designing and executing statistical data-editing processes with Banff. It gives statistical programs a more automated way to organize editing steps, parameters, and reproducible processing.