Build Models You Can Trust
Your dbt models are only as good as the data flowing through them. Sparvi helps you monitor upstream sources, validate transformations, and ensure your models deliver reliable metrics—every time.
The Analytics Engineer's Dilemma
You own the transformation layer—but not the data flowing into it.
"The source data changed"
Upstream systems modify schemas or data formats. Your models run successfully—but produce garbage. dbt tests pass, but the numbers are wrong.
"Why is this metric off?"
A stakeholder questions your numbers. Was it a model bug? Source data issue? ETL failure? You spend hours tracing through the lineage.
"dbt tests don't catch everything"
Unique and not_null tests help, but they can't detect when valid-looking data is actually wrong. Row counts can be fine while values are corrupted.
"Data arrived late"
Your model ran on schedule, but the source data wasn't fresh. Now yesterday's dashboard shows partial data and stakeholders are confused.
"Who uses this model?"
You want to refactor, but you're not sure what downstream dashboards or models depend on it. Making changes feels risky.
"It worked in development"
Models pass CI, but production data behaves differently. Edge cases appear that your test data never covered.
Complete Your dbt Stack
dbt tests validate at build time. Sparvi monitors continuously—catching what tests miss.
Source Monitoring
Know when upstream data changes before it breaks your models. Monitor freshness, volume, and schema changes on source tables.
- • Source freshness alerts
- • Schema change detection
- • Volume anomaly tracking
- • Null rate monitoring
Transformation Validation
Validate your models beyond dbt tests. Add business rules, cross-model checks, and anomaly detection to your transformed data.
- • Custom SQL validations
- • Cross-model consistency checks
- • Distribution monitoring
- • Metric anomaly detection
Lineage & Impact
Understand data flow end-to-end. When issues occur, instantly see affected downstream models and dashboards.
- • Source-to-dashboard lineage
- • Impact analysis for changes
- • Model dependency mapping
- • Stakeholder notification
Designed for dbt Workflows
Sparvi doesn't replace dbt tests—it complements them. Use both for comprehensive coverage.
dbt + Sparvi Coverage
Built for Analytics Engineers
SQL-Native Rules
Write validation rules in SQL—the same language as your dbt models. No new syntax to learn.
Freshness Monitoring
Know when source data is stale before running models. Avoid transforming outdated data.
Metric Validation
Track key metrics over time. Get alerted when revenue, user counts, or other KPIs look unexpected.
Schema Evolution
See when source tables change. Plan model updates before they break.
Cross-Model Checks
Validate consistency between related models. Ensure fact and dimension tables stay in sync.
Stakeholder Alerts
Route alerts to the right teams. Data issues go to you; business impact alerts go to stakeholders.
Ship Models with Confidence
Stop worrying about data quality. Focus on building models that drive business value.
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