The end-to-end platform for building, deploying, and observing data pipelines. This interactive tour walks you through every step — from onboarding to production monitoring.
Authenticate securely using your GitHub Enterprise account. Sensyze integrates with your existing identity provider for seamless SSO.
Enterprise SSO via GitHub
Get the native desktop application for your platform. Canvas Sensyze runs your pipelines locally in an air-gapped environment.
v2.4.1 · Released June 2026 · 148 MB
Launch Canvas Sensyze and run the one-time setup wizard. This clones the Sensyze engine, installs Docker containers, configures Spark local mode, and prepares your local environment.
This will install the Sensyze engine locally
After setup, log in as the superadmin to configure your workspace. The superadmin has full control over users, licenses, billing, and system settings.
Invite team members by email or GitHub username. Assign roles with granular permissions — Viewer, Builder, Admin, or Superadmin.
Activate premium features by entering your BSL license key. The Business Source License includes unlimited nodes, Spark distributed processing, priority support, and a 4-year conversion clause to OSS.
Sensyze offers two ways to build pipelines: the AI Builder (describe in plain English) or the Visual Builder (drag-and-drop canvas). You can switch between them at any time.
Choose how you want to build your pipeline
Describe your pipeline in plain English. The AI generates the complete configuration.
Drag nodes onto the canvas, connect sources to transforms to sinks.
Sensyze generated your pipeline as a clean YAML configuration. Copy this and paste it into Canvas Sensyze to run locally. No compilation required — just execution.
Your pipeline has been successfully loaded into Canvas Sensyze. All connectors are validated, transformations are parsed, and the Spark runtime is initialized. Click "Run Pipeline" to execute locally.
4 steps · Ready to run
Pipeline loaded successfully in Canvas Sensyze. Your pipeline is now ready for local execution.
Watch live logs as the pipeline fetches 847 Shopify orders, transforms them, runs Spark aggregation (daily revenue), and upserts results to PostgreSQL — all in 20.3 seconds.
4 steps · Ready to execute
Monitor pipeline performance in real-time. When failures occur, the durable workflow engine pauses, logs the error with full stack traces, and prepares for self-healing retries.
Automate your pipeline with cron-based scheduling. Set expressions like 0 */2 * * * for every 2 hours. Monitor execution history with row counts and durations.
You've seen the full journey — from GitHub login through team setup, licensing, pipeline building with AI or visual canvas, local execution via Canvas Sensyze desktop app, observability, and scheduled production runs.
Build, deploy, and observe data pipelines with Sensyze Cloud.
Your infrastructure, your data, your rules.