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.

types.ExecutionStatus

created_atstring

CreatedAt is when the execution was created

desired_state object

DesiredState is what state the execution should be in

details object

Details is a map of additional details about the state.

property name*string

messagestring

Message is a human readable message describing the state.

state_typetypes.ExecutionDesiredState (string)

StateType is the current state of the object.

Possible values: [pending, running, stopped]

details object

Details contains structured metadata about the current execution state (e.g., error codes, hints, component info from lib/errors)

property name*string

next_execution_idstring

NextExecutionID is used for tracking and triggering the next execution, such as during an update

observed_state object

ObservedState is the actual state of the execution on the node

details object

Details is a map of additional details about the state.

property name*string

messagestring

Message is a human readable message describing the state.

state_typetypes.ExecutionStateType (string)

StateType is the current state of the object.

Possible values: [pending, starting, validating, running, degraded, completed, failed, lost, stopping, stopped]

previous_execution_idstring

PreviousExecutionID is used for tracking the previous execution in a sequence

revisioninteger

Revision is incremented on any change to the execution (state changes, status updates, etc.)

stable_atstring

StableAt is when the execution passed deployment validation and became stable. Once set, this execution will not trigger autonomous rollback if it fails later (preventing infinite rollback loops when a previously-validated execution is resurrected). Zero value means the execution hasn't been validated yet (still in validation window).

updated_atstring

UpdatedAt is when the execution was last updated

types.ExecutionStatus
{
  "created_at": "string",
  "desired_state": {
    "details": {},
    "message": "string",
    "state_type": "pending"
  },
  "details": {},
  "next_execution_id": "string",
  "observed_state": {
    "details": {},
    "message": "string",
    "state_type": "pending"
  },
  "previous_execution_id": "string",
  "revision": 0,
  "stable_at": "string",
  "updated_at": "string"
}