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.StreamState

consumer_countinteger

ConsumerCount tracks the number of active WebSocket consumers for this stream. When this drops to 0, streaming should be disabled.

created_atstring

CreatedAt is when the stream was created

endstring

End is the timestamp until which to stream logs. If zero, logs will be streamed indefinitely (live tailing).

idstring

ID is the unique identifier of the stream state

job_idstring

JobID is the identifier of the job associated with the stream

log_levelstring

LogLevel is the minimum log level to stream (e.g., "info", "error")

node_idstring

NodeID is the identifier of the node associated with the stream and the job

startstring

Start is the timestamp from which to start streaming logs. If zero, logs will be streamed from the current time.

streamingboolean

Streaming indicates whether log streaming is enabled for this job/node

updated_atstring

UpdatedAt is when the stream was last updated

types.StreamState
{
  "consumer_count": 0,
  "created_at": "string",
  "end": "string",
  "id": "string",
  "job_id": "string",
  "log_level": "string",
  "node_id": "string",
  "start": "string",
  "streaming": true,
  "updated_at": "string"
}