What is Expanso and how does it work?

Expanso is a managed platform for deploying intelligent data pipelines at the edge. It processes data where it's generated - reducing bandwidth, latency, and costs. You deploy lightweight agents on your infrastructure, build pipelines using our visual builder or YAML, and control everything from a central SaaS platform.

Can I run AI/ML models directly in my data pipelines?

Yes! Expanso supports running ONNX, TensorFlow Lite, and other models as native pipeline steps. Execute low-latency inference on streaming data, enrich events with model outputs (like risk scores), and make decisions at the edge without cloud round-trips.

How many pre-built components are available?

Expanso provides 200+ pre-built components including inputs (Kafka, HTTP, files), processors (transformations, filtering, PII masking, aggregations), and outputs (S3, Snowflake, Datadog, Splunk). Browse the complete catalog in our Component Reference.

Do I need to write code to build pipelines?

No - use our drag-and-drop visual pipeline builder to create sophisticated pipelines without code. For advanced use cases, you can also write pipelines in YAML or use the Bloblang transformation language for complex data mappings.

How does Expanso help with data governance and compliance?

Expanso includes built-in governance features: automatic PII detection and masking, policy enforcement at the edge, RBAC, SSO integration, and comprehensive audit trails. Mask sensitive data before it ever leaves your network.

Rerun Job

PUT /jobs/:id/rerun

Triggers a new scheduler evaluation without changing the job specification.

What Rerun Means

Rerun creates a new evaluation to force the scheduler to reassess the job. This is different from updating a job (PUT /jobs):

Rerun: Job spec stays the same, scheduler re-evaluates
Update: Job spec changes, triggers new evaluation

When to Use

Job failed to schedule: Force re-evaluation after fixing node issues
Nodes changed: New nodes added or labels updated since last deployment
Stuck evaluation: Evaluation is blocked or pending for too long
Manual retry: After resolving external issues (network, resources, etc.)

What Happens

Creates a new evaluation for the job (with current spec)
Scheduler re-analyzes node matching and placement
Creates/updates/stops executions as needed
Returns immediately (async operation)

When NOT to Use

To change job configuration: Use PUT /jobs/{id} instead
To stop a job: Use POST /jobs/{id}/stop instead
If job is already running correctly: Rerun is unnecessary

Common Scenarios

"Job shows 'blocked - no nodes match selector'" → Added matching nodes → Rerun
"Updated node labels" → Rerun jobs that target those labels
"Network issue resolved" → Rerun jobs that failed to deploy

Request

Responses

Evaluation created

Rerun Job

/jobs/:id/rerun

What Rerun Means​

When to Use​

What Happens​

When NOT to Use​

Common Scenarios​

Request​

Responses​