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.

api.GetExecutionHistoryResponse

items object[]

The actual items

Array [

details object

Details contains optional structured metadata about the event Examples: old_state, new_state, node_id, error_code, etc.

property name*string

execution_idstring

ExecutionID identifies which execution this event belongs to (optional - only for execution events)

job_idstring

JobID identifies which job this event belongs to (always set)

job_versioninteger

JobVersion is the version of the job when this event occurred

messagestring

Message is a human-readable description of what happened This is the primary field users see in history timelines

seq_numinteger

SeqNum is a globally incrementing sequence number within the history bucket Used for ordering and as part of the storage key

timestampstring

Timestamp is when this event occurred

]

next_tokenstring

Token for next page

api.GetExecutionHistoryResponse
{
  "items": [
    {
      "details": {},
      "execution_id": "string",
      "job_id": "string",
      "job_version": 0,
      "message": "string",
      "seq_num": 0,
      "timestamp": "string"
    }
  ],
  "next_token": "string"
}