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.

Get an execution

GET /executions/:id

Returns detailed information about a specific job execution.

What You Get

Execution ID, job ID, node ID, and namespace
Job version and specification snapshot (spec at execution time)
Desired state (what the system wants) vs compute state (actual state on node)
Timestamps (created, updated)
Details map with execution-specific metadata

When to Use

Debug why an execution failed (check compute_state message)
Verify job version running on a specific node
Check execution state alignment (desired vs actual)
Investigate execution lifecycle transitions

State Tracking

Executions track two states:

desired_state: What the system wants (run, stop)
compute_state: Actual state on the node (pending, running, failed, complete) Misalignment indicates ongoing transitions or issues.

GET /executions/{id}/history - See state change history for this execution

Get an execution

/executions/:id

What You Get

When to Use

State Tracking

Request

Responses

Get an execution

/executions/:id

What You Get​

When to Use​

State Tracking​

Related Endpoints​

Request​

Responses​

What You Get

When to Use

State Tracking

Related Endpoints

Request

Responses