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.SchedulerConfig

execution_limit_backoffinteger<int64>

ExecutionLimitBackoff is the duration to wait before creating a new scheduling run when hitting execution limits.

Possible values: [-9223372036854776000, 9223372036854776000, 1, 1000, 1000000, 1000000000, 60000000000, 3600000000000]

max_executions_per_evaluationinteger

MaxExecutionsPerEvaluation is the safety ceiling on total scheduler operations per evaluation (creates + stops + updates + failures). Plans exceeding the ceiling fall back to the delayed-evaluation self-heal path.

max_executions_per_transactioninteger

MaxExecutionsPerTransaction is the maximum number of new-execution writes per planner transaction. The planner splits a plan's NewExecutions into batches of this size, committing each in its own transaction; a final transaction commits job state, follow-up evaluations, and marks the evaluation complete.

queue_backoffinteger<int64>

QueueBackoff specifies the time to wait before retrying a failed job.

Possible values: [-9223372036854776000, 9223372036854776000, 1, 1000, 1000000, 1000000000, 60000000000, 3600000000000]

queue_timeout_neverinteger<int64>

DefaultQueueTimeoutNeverRestart is the default queue timeout for jobs with "never" restart policy. Batch jobs get fast feedback when no matching nodes exist. Set to 0 for no default (wait indefinitely).

Possible values: [-9223372036854776000, 9223372036854776000, 1, 1000, 1000000, 1000000000, 60000000000, 3600000000000]

queue_timeout_otherinteger<int64>

DefaultQueueTimeoutOtherPolicies is the default queue timeout for "always" or "on-failure" restart policies. Services wait for matching nodes (e.g., auto-scaling scenarios). Set to 0 for no default (wait indefinitely).

Possible values: [-9223372036854776000, 9223372036854776000, 1, 1000, 1000000, 1000000000, 60000000000, 3600000000000]

worker_countinteger

WorkerCount specifies the number of concurrent workers for job scheduling.

types.SchedulerConfig
{
  "execution_limit_backoff": -9223372036854776000,
  "max_executions_per_evaluation": 0,
  "max_executions_per_transaction": 0,
  "queue_backoff": -9223372036854776000,
  "queue_timeout_never": -9223372036854776000,
  "queue_timeout_other": -9223372036854776000,
  "worker_count": 0
}