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.

Log Processing Examples

Process logs at the edge to reduce bandwidth costs, improve performance, and ensure compliance. These examples demonstrate different approaches to log processing, from simple filtering to production-ready pipelines.

Available Examples

Filter by Severity

Basic log filtering that routes ERROR and WARN messages to separate destinations. Perfect for understanding the fundamentals of log routing.

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Enrich & Export to S3

Add metadata and lineage tracking to logs before batching and exporting to Amazon S3. Demonstrates enrichment patterns and efficient cloud uploads.

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Production Pipeline

Complete production-ready pipeline with HTTP input, JSON parsing, validation, enrichment, PII redaction, and multi-destination routing (Elasticsearch, S3, local backup).

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Common Patterns

These examples demonstrate key patterns used in log processing:

Severity filtering - Route based on log level (ERROR, WARN, INFO, DEBUG)
Metadata enrichment - Add node ID, region, pipeline info for traceability
Multi-destination routing - Send to real-time search, archival storage, and backups
Batching - Optimize bandwidth and API costs by batching uploads
PII redaction - Remove sensitive data before logs leave the edge

Parse Log Formats - Parse JSON, CSV, Apache, Syslog formats
Remove PII - Redact sensitive information
Content Routing - Route data based on content
Bloblang Guide - Transformation language reference

Available Examples​

Filter by Severity​

Enrich & Export to S3​

Production Pipeline​

Common Patterns​

Related Content​

Available Examples

Filter by Severity

Enrich & Export to S3

Production Pipeline

Common Patterns

Related Content