Why We Built Sparvi: A Better Way to Monitor Data Quality

The 3 AM Wake-Up Call

It was 3 AM when the Slack message came through: "Revenue dashboard is showing wrong numbers. CEO is asking questions."

This wasn't the first time. As data engineers, we'd spent years building pipelines, writing transformations, and creating dashboards. And we'd spent just as many hours debugging why data broke, when it broke, and what downstream systems were affected.

The worst part? We usually found out days or weeks after the damage was done. A schema changed upstream. An ETL job failed silently. A data quality check that should have caught bad data... didn't exist yet.

The Existing "Solutions"

We tried every approach to prevent these issues:

DIY Monitoring

We wrote custom scripts. Row count checks. Null checks. Email alerts. Cron jobs running validation queries. It worked... until it didn't:

• Scripts broke when schemas changed
• Email alerts got ignored in crowded inboxes
• Only data engineers could understand the alerts
• Maintaining hundreds of custom scripts became a full-time job

Enterprise Tools

Then we looked at enterprise data observability platforms. They promised to solve everything:

• Automatic anomaly detection
• Schema monitoring
• Data lineage
• Integration with everything

But then we saw the pricing: $50,000-100,000+ per year. For a team of 8 people, that was more than our entire data infrastructure budget.

Even if we could justify the cost, the setup would take months. We needed something that worked now, not next quarter.

Open Source Tools

Great Expectations, dbt tests, and other open-source tools helped. But:

• They required significant engineering effort to set up and maintain
• No built-in alerting or collaboration features
• Non-engineers couldn't interact with them
• Each tool solved one piece of the puzzle, not the whole problem

The Real Problem

After comparing notes with dozens of other data teams, we realized the real problem wasn't just monitoring. It was the entire workflow around data quality.

Detection was too slow. By the time we found issues, they'd already impacted dashboards, reports, and business decisions. Days or weeks of bad data had already gone out the door.

Collaboration was broken. When issues happened, the entire team needed to know - not just data engineers. But existing tools were built for engineers only. Product managers couldn't understand the alerts. Analysts couldn't figure out which reports were affected. Stakeholders were left in the dark.

And resolution took forever. Once we detected an issue, we'd post in Slack, create a Jira ticket, document it in Confluence, update multiple stakeholders, then verify the fix. All of this happened outside the monitoring tool, which meant constant context switching and information getting lost.

There had to be a better way.

Building Sparvi

We decided to build the tool we wished existed. A data observability platform that:

Catches Issues Early

• Automatic anomaly detection that works out of the box
• Schema change monitoring with zero configuration
• Custom validation rules for business-specific checks
• Comprehensive data profiling to understand your data

Enables Team Collaboration

• @mention teammates directly in issue discussions
• Add business context that non-engineers can understand
• Track who's working on what
• Notify the right people automatically

Speeds Up Resolution

• Everything in one place - no switching between tools
• Clear ownership and accountability
• Historical context when similar issues happen again
• Automated workflows that reduce manual work

Doesn't Break the Bank

• Pricing designed for teams of 3-15, not 100+
• Transparent costs with no surprise bills
• Setup in hours, not months
• No massive implementation fees

What We've Learned

Building Sparvi has taught us a lot about what teams actually need. The most important lesson? Data quality isn't just an engineering problem. The most successful teams we've talked to treat it as everyone's problem. Product managers care about accurate metrics. Analysts care about reliable reports. Executives care about trustworthy dashboards. Tools that only speak to engineers miss half the battle.

We also learned that simple beats perfect. We could've built 100 features and taken years to launch. Instead, we focused on doing five things really well: detecting anomalies, monitoring schemas, validating data quality, profiling data comprehensively, and enabling team collaboration. Turns out that's what most teams actually need.

And speed matters more than features. A tool that takes three months to set up won't help you catch issues tomorrow. We designed Sparvi to be useful in hours, not months. Because when data breaks at 3 AM, you need monitoring running today, not next quarter.

What's Next

We're just getting started. Our vision is to make data quality monitoring accessible to every data team, regardless of size or budget.

We're working with design partners to refine the platform, add new features, and make sure we're solving real problems. If you're a data team of 3-15 people struggling with data quality, we'd love to talk.

Why This Matters

Data breaks. It's not a question of if, it's when. And when it breaks, the damage spreads fast - bad decisions, wrong numbers in customer dashboards, miscounted revenue. The longer it takes to catch issues, the more expensive they become.

But here's the frustrating part: most tools for preventing these issues are either too expensive ($50K+/year), too complex (months of setup), or too limited (engineers only). Small teams get stuck choosing between unaffordable enterprise tools and duct-taped DIY solutions.

That's the gap we're filling with Sparvi. We're building this for teams like ours - small data teams at growing companies who need enterprise-grade monitoring without enterprise complexity or pricing. Teams that need to move fast, collaborate across functions, and catch issues before they turn into incidents.

No more 3 AM wake-up calls about broken dashboards. No more discovering data issues weeks after they happen. No more tools that only engineers can use. Just straightforward data observability that helps your whole team catch issues early and fix them fast.