For Data Engineers

Stop Firefighting. Start Monitoring.

You're tired of learning about data issues from angry stakeholders. Sparvi gives you proactive observability—catch problems before anyone else notices, fix them faster, and finally get ahead of data quality.

Sound Familiar?

Every data engineer knows these pain points too well.

"Why does the dashboard look wrong?"

The dreaded Slack message. You drop everything to investigate, only to find a pipeline failed two days ago and no one noticed.

"Who changed the schema?"

Upstream teams modify tables without telling you. Your dbt models break. You find out at 2am when everything fails.

"Where did this data come from?"

Tracing data issues back to their source takes hours. By the time you find the root cause, stakeholders are frustrated.

"Is the data fresh?"

You manually check last-update timestamps. Sometimes you forget. Then reports show stale data and everyone assumes it's accurate.

"The metrics don't match"

Different dashboards show different numbers. You spend hours reconciling when you could be building new features.

"We need better monitoring"

But enterprise tools cost $100K and take months to implement. DIY solutions require time you don't have.

How Sparvi Helps Data Engineers

Proactive Alerting

Get notified about data issues before anyone asks. Anomalies, freshness problems, and validation failures go straight to Slack.

  • • Volume drops and spikes
  • • Null rate changes
  • • Distribution shifts
  • • Freshness delays

Faster Root Cause Analysis

When issues occur, understand impact immediately. See which downstream tables are affected and trace problems to their source.

  • • Data lineage tracking
  • • Schema change history
  • • Profile comparisons over time
  • • AI-suggested root causes

Automated Validation

Define business rules once, enforce them automatically. SQL-based validation rules run on schedule without manual intervention.

  • • Custom SQL rules
  • • Referential integrity checks
  • • Business logic validation
  • • Scheduled execution

Fits Your Workflow

Sparvi integrates with the tools you already use. No workflow disruption—just better visibility.

Connects to Snowflake with secure key-pair auth (BigQuery & Redshift Q4 2025)
Slack alerts for anomalies, schema changes, and validation failures
Works alongside dbt—adds monitoring where dbt tests leave off
Issue management system for tracking and resolving problems
API access for integration with existing monitoring
# What you see in Slack
Anomaly Detected: orders
Row count dropped 85% from expected
Expected: ~10,000 | Actual: 1,523
Detected: 5 minutes ago
View DetailsAcknowledge
Catch issues in minutes, not days

A Day With Sparvi

Without Sparvi

9:00 AM
Start work, check dashboards manually
10:30 AM
Stakeholder reports wrong numbers
11:00 AM
Start investigating the issue
2:00 PM
Finally find root cause—pipeline failed 2 days ago
4:00 PM
Fix the issue, backfill data
5:30 PM
Day lost to firefighting

With Sparvi

6:00 AM
Sparvi detects pipeline failure, sends Slack alert
9:00 AM
See alert, click through to issue details
9:15 AM
Root cause identified via lineage and AI suggestions
10:00 AM
Issue fixed before stakeholders notice
10:30 AM
Back to building new features
5:30 PM
Productive day, stakeholders happy

Built for How You Work

SQL-Based Validation

Write validation rules in SQL you already know. No proprietary DSL to learn.

Schema Change Tracking

See exactly when columns were added, removed, or modified. Full history for every table.

Automated Profiling

Statistics collected automatically. No queries to write or maintain.

Configurable Sensitivity

Tune anomaly detection to your needs. More signal, less noise.

Issue Management

Track data quality issues like bugs. Assign, discuss, and resolve in one place.

Historical Trends

See how data quality changes over time. Spot degradation before it becomes critical.

Ready to Get Ahead of Data Issues?

Join data engineers who use Sparvi to catch problems before stakeholders ask.

Apply for Design Partner Program