For Data Engineering Teams

Your Team Builds Pipelines. They Should Be Shipping Features.

Data engineering teams spend 60-80% of their time on pipeline maintenance, data quality firefighting, and integration work. Expanso shifts data control upstream so your team can focus on what matters.

The Data Engineering Productivity Crisis

Your team is stuck maintaining instead of building

Pipeline Maintenance Hell

Schema changes break pipelines. Data sources go offline. Quality issues cascade downstream. Your team spends more time firefighting than building new capabilities.

60-80% time lost to maintenance

Integration Sprawl

Every new data source means weeks of connector development, testing, and deployment. Teams build custom integrations that become legacy systems overnight.

Weeks per new integration

Data Quality Whack-a-Mole

Quality issues discovered in production. Manual data profiling. Reactive fixes that don't address root causes. Teams play catch-up instead of getting ahead.

Endless quality firefighting

Shift Data Control Upstream

Process, validate, and govern data at the source - before it hits your pipelines

Pre-Built Integrations

200+ production-ready connectors for databases, APIs, cloud services, and IoT platforms. Deploy in minutes, not weeks.

Deploy integrations in minutes

Source-Level Validation

Data quality checks at origination. Schema validation, type checking, and business rules enforced before data moves.

Catch issues at the source

Declarative Pipelines

Define transformations in YAML. Version control your pipelines. Deploy globally with one command.

Pipelines as code

Built-In Observability

Monitor pipeline health, data quality metrics, and throughput in real-time. Alerts before issues impact downstream.

Proactive issue detection

How It Works

1

Deploy Agents at Sources

Lightweight agents run at data sources - databases, applications, edge devices, cloud services.

Single binary, minimal resources
2

Define Processing in YAML

Declare transformations, validations, and routing rules. Version control everything.

GitOps-ready configuration
3

Process at Source

Data validated, transformed, and governed before it moves. Only clean data reaches your platforms.

Quality guaranteed upstream
4

Route to Any Destination

Send processed data to Snowflake, Databricks, Kafka, S3, or any destination. One pipeline, many outputs.

Multi-destination routing

Results Data Teams See

70%
Less pipeline maintenance
Teams shift from firefighting to feature development
Minutes
New integration deployment
Pre-built connectors replace weeks of development
10x
Faster issue resolution
Problems caught at source, not discovered in production
50%
Reduced data platform costs
Filter noise and bad data before it hits your warehouse

Common Use Cases

Observability Data Optimization

Challenge: Logs and metrics flooding Splunk and Datadog. Platform costs growing faster than value delivered.
Solution: Filter, aggregate, and route observability data at the source. Send only actionable data to expensive platforms.
Result: 60% reduction in observability platform spend

Multi-Cloud Data Integration

Challenge: Data scattered across AWS, Azure, GCP, and on-prem. Custom integrations for each cloud.
Solution: Unified data processing layer across all environments. Same pipeline definitions everywhere.
Result: Single pipeline for all clouds

Real-Time Analytics Enablement

Challenge: Batch pipelines can't support real-time use cases. Teams rebuild from scratch for streaming.
Solution: Stream processing at the source. Same YAML definitions work for batch and streaming.
Result: Real-time without rebuilding

Traditional Pipelines vs. Upstream Control

Traditional Approach

  • Build custom connectors (weeks)
  • Move all data to warehouse
  • Discover quality issues in production
  • Fix issues retroactively
  • Repeat for every source
Months to value

With Expanso

  • Deploy pre-built connectors (minutes)
  • Validate and transform at source
  • Send only clean data downstream
  • Proactive quality monitoring
  • Scale effortlessly
Days to value

Stop Building Pipelines. Start Shipping Value.

See how data engineering teams accelerate delivery with upstream data control.