Data quality for startups
Scale data trust with your fast-growing startup via automated, end-to-end data observability.
Trusted by the data teams at
Learn how Prefect increased data quality coverage by 70 percent with Monte Carlo.
hours per week in engineering resource savings with Monte Carlo.
Faster implementation compared to building an in-house tool.
- Resource cost of root causing data quality issues and broken pipelines.
- Wasted hours to days spent debugging data pipelines.
- Lack of visibility into new data stack.
- Out-of-the-box data monitoring and lineage – no implementation required.
- Seamless alerting and incident triaging to scale the impact of a lean team.
- Immediate time-to-value with automated data quality checks.
“The incident alerting directly into Slack and giving the ability to triage straight from there was a game changer. It’s so great that if I add new columns to some of the ingestion tables, [our data analyst] gets alerts from Monte Carlo. It’s like, ‘Look, here’s this schema change!'”
Dylan Hughes Data Engineering Manager
Say good bye to broken dashboards.
Achieve stakeholder trust.
Be the first to know when data breaks so you can fix before your downstream consumers are affected.
Launch more reliable products.
Build and release customer-facing products knowing that the data powering these services are fresh, accurate, and reliable.
Save time and resources
Reduce the amount of time and resources you spend firefighting data quality issues with automated, end-to-end coverage.
“Monte Carlo gives us the power to know what’s going on with our data at any given point in time so we can ask the right questions when data downtime strikes, for instance ‘we think something’s wrong here, did you change anything, or is this expected?”
Pablo Recio Data Engineer
Out-of-the-box coverage across all your data tables, opt-in monitors for key assets, and monitors-as-code.
Don’t just sound the alarm when data incidents occur. Empower your data teams to resolve incidents in minutes.