Run Data Like Production Systems
Run production-grade data workflows with continuous pipelines, automated recovery, and upstream quality enforcement
Why Data Pipelines Don't Scale Like Software
Software teams have CI/CD, observability, and automated recovery. Data pipelines still rely on scripts, cron jobs, and tribal knowledge.
Technical Debt Accumulates
Unmanageable Complexity
Pipelines accumulate scripts, patches, and exceptions. Every fix adds more complexity. Nobody understands the full system anymore.
Failures Need Humans
No Automated Recovery
When pipelines break, someone has to wake up. Manual restarts, backfills, and debugging consume engineering time that should go to building.
Knowledge Walks Out
Single Points of Failure
Reliability depends on tribal knowledge. When key engineers leave, so does institutional memory. New team members take months to ramp up.
Treat Data Pipelines Like Production Services
Apply software engineering practices—automation, observability, reliability, and governance—to every data workflow
How Expanso Operationalizes Data
Production-grade reliability built into every pipeline
Standardized execution everywhere
One Model, Every Environment
One operational model across cloud, on-prem, and edge. Pipelines behave consistently regardless of where they run.
Reliability built into execution
Automatic Recovery
Ordering, buffering, retry, and recovery are enforced automatically. Pipelines self-heal without manual intervention.
Quality enforced upstream
Shift Left for Data
Validation and policy checks run before data moves, not after ingestion. Bad data never reaches downstream systems.
Full pipeline observability
See Everything
Real-time visibility into pipeline state, execution timing, failures, and downstream impact. Know exactly what's running and where.
Policy-driven governance
Compliance as Code
PII, GDPR, HIPAA, and security controls are enforced during execution—not bolted on afterward or handled manually.
Lineage and auditability
Track Everything
Complete data lineage from source to destination. Know where data came from, what happened to it, and where it went.
Outcomes From Your DataOps Platform
Real impact across pipeline reliability and team productivity
Faster pipeline recovery with automated detection and remediation
Reduction in manual pipeline maintenance and rework
Reliable, validated data delivered to analytics and AI systems
Increase in productivity across data engineering and analytics teams
Real-World Impact
See how leading organizations operationalize their data pipelines at scale
DataOps for Real-Time Pipelines
A major North American sports league ran dozens of brittle data pipelines per stadium. Failures caused live graphics delays and manual fixes. Expanso standardized and automated pipeline operations locally.
DataOps at Fleet Scale
A European OEM operated millions of vehicle telemetry pipelines with frequent failures and backlogs. Expanso automated pipeline reliability and quality enforcement at the edge.
Operationalizing Observability Pipelines
A top-25 US regional bank spent weeks fixing broken log pipelines feeding Splunk. Expanso enforced quality and reliability upstream, transforming reactive firefighting into proactive operations.
DataOps for Distributed Processing
A forestry company ran fragile batch pipelines for drone imagery across field offices. Multi-day delays and frequent failures. Expanso automated and standardized pipeline execution locally.
Why Expanso for DataOps
Deploy anywhere
Operate pipelines across SaaS, on-prem, edge, and hybrid environments
Broad integrations
Works with existing data platforms without lock-in or rewrites
Policy-driven operations
Rules replace scripts. Reliability and governance scale without complexity
Built to scale
Manage hundreds to thousands of pipelines without growing team size
Frequently Asked Questions
What is a DataOps platform?
A DataOps platform applies software operational principles—automation, observability, and reliability—to data pipelines, ensuring consistent, high-quality data delivery.
How is DataOps different from ETL tools?
ETL tools move data. DataOps ensures pipelines are reliable, observable, governed, and recover automatically when things break.
Does Expanso replace orchestration tools?
Expanso complements orchestration by enforcing quality, governance, and operational reliability at the source. It works alongside existing tools like Airflow, Dagster, or Prefect.
Can Expanso run in hybrid or edge environments?
Yes. Expanso runs in cloud, on-prem, edge, and hybrid setups with consistent policy enforcement and operational behavior.
How does Expanso improve data quality?
Quality checks run upstream before data moves, preventing bad data from reaching analytics or AI systems. Issues are caught at the source, not discovered downstream.
Run data like production
Your data pipelines already exist. DataOps determines whether they scale or fail.