Data Observability: Build vs Buy

Introduction

When bad data sneaks into your business, the costs can be brutal. Gartner puts the average price of poor data quality at $12.9 million per year per company for enterprises. That figure doesn't even include the hours wasted by engineers chasing issues upstream or capture the lost trust from business users.

This is why data observability has become a must-have in the modern stack. At its core, data observability means keeping an eye on the health of your pipelines and datasets so you can spot problems early. Think of it as the equivalent of monitoring in software engineering, you don’t just deploy an app and hope for the best, you monitor its uptime, errors, and performance.

The big question for data teams isn’t whether they need observability, it’s how to get there. Do you build it yourself using open-source tools and custom scripts, or do you buy a dedicated platform? This article breaks down both paths, the benefits and pitfalls of DIY, what commercial platforms deliver, and how to make the call for your team.

The DIY Route: Open Source Data Observability and In-House Builds

Many teams start by building their own data observability setup. The logic is simple: why pay for a platform when you can stitch together free tools and your own checks? Open source offers flexibility, avoids license costs, and gives engineers full control. You’re also not tied to any contracts or tools, you can replace and re-build parts of the setup when needed.

Here are some of the main building blocks teams use:

• Great Expectations (GX): Lets you define “expectations” (rules) for your data, like “no nulls in the primary key” or “values in this column must be positive.” It’s widely adopted and integrates with Python, SQL, and data warehouses.
  
• Soda SQL / Soda Core: Uses a YAML-based language to define checks, runs them against your sources, and alerts you when something breaks. Popular for embedding data quality into pipelines.
  
• dbt Tests and Elementary: dbt lets you add tests directly into your models (e.g., no duplicates, valid foreign keys). Elementary builds on top of dbt to give monitoring and reporting on test outcomes.
  
• Apache Griffin, AWS Deequ, MobyDQ: Designed for big data and streaming contexts, these help with completeness and validity checks at scale.
  
• Custom scripts: Many teams simply write SQL queries or Python scripts to count rows, check distributions, or validate schemas, and schedule them in Airflow or similar orchestrators.
  

Why Teams Go DIY

• Cost: No license fees. Tools like GX or dbt tests are free to use.
  
• Customization: You can tailor checks exactly to your business rules.
  
• Control: All data stays in your environment, which can help with compliance.
  
• Integration: DIY checks can be embedded right into existing pipelines.
  

For small teams or those just starting out, this approach can work well. You can cover basic sanity checks quickly and avoid big SaaS bills early on.

The Hidden Costs of DIY

The initial savings of going open source often disappear as soon as you scale. The hours spent maintaining tests, fixing integrations, and tuning thresholds become an ongoing expense that’s often larger than the license fee you were trying to avoid.

Here’s where DIY gets tough:

• Engineering load: Writing and updating tests, fixing broken checks after schema changes, and wiring together multiple tools adds up. Even if you avoid license fees, you’re spending valuable engineering hours that could be used to improve products or analytics.
  
• Known issues only: Tools like GX are great for testing what you think might go wrong. But most incidents come from surprises: an unexpected schema change, a distribution shift, a missed join. Rule-based tests won’t catch these “unknown unknowns.”
  
• Scaling pain: Writing 10 checks is fine. Managing hundreds or thousands quickly becomes a burden. Teams often end up spending more time fixing broken tests or tuning thresholds than solving actual business problems.
  
• Integration gaps: Most open-source tools focus on one part of the stack. You may end up cobbling together multiple tools for warehouses, streams, and BI, and still lack a full view of data health.
  
• Advanced features: Machine learning-based anomaly detection, automated root cause analysis, or smart alerting (to reduce noise) are hard to build in-house. Without them, you risk either missing issues or drowning in false alarms.
  
• Support risk: If your DIY setup fails, your team is the support team. If the one engineer who built it leaves, you may be stuck.

The bottom line: open source can handle basics, but the effort to scale and maintain it is often underestimated.

Buying a Platform: What You Get

Dedicated data observability platforms exist to take this burden off your plate. They focus on breadth, automation, and intelligence. It goes without saying, but their sole focus is building the best data observability solution. They’ve dealt with different customers, have years of learning of what works and what doesn’t and are constantly building new products & features.

Here’s what buying typically gives you:

1. Full-stack monitoring: Out-of-the-box coverage for freshness, volume anomalies, schema changes, and distribution shifts across your transformation layer, warehouse, ETL, and BI tools.
   
2. Speed to value: Connect your sources, and you’re usually seeing insights within days, not months.
   
3. AI-driven detection: Machine learning models adapt to seasonality and trends, so you get fewer false positives. Some platforms even tie anomalies back to root causes using lineage.
   
4. Unified view: Monitor all your pipelines and datasets in one place. See how issues in upstream tables affect dashboards downstream.
   
5. Incident workflows: Built-in alerting, ticketing, and dashboards so teams can track and resolve issues faster.
   
6. Support and updates: A vendor team continuously improving the product, adding integrations, and providing SLAs if something breaks. Support from dedicated solutions engineers, who have gone through multiple data observability implementations.
7. AI Agents: A relatively recent addition to Data Observability, but AI Agents have taken a big role in removing manual work, automating testing & monitoring and identifying and fixing data issues.
   

The trade-off is cost. Subscriptions can run six figures for large teams. But for many, the ROI is clear: preventing one or two major data incidents can justify the spend, not to mention the engineering hours saved.

For a more comprehensive understanding of the ROI, check out our guide ROI of Data Observability.

Build vs Buy: How to Decide

There’s no one-size-fits-all answer, but here’s a framework to guide the call:

• How critical is data for you? If data is business-critical to your company, the tolerance for failure is low. Since the cost of broken data is high, the investment into data observability is small compared to what can happen if data is unreliable.
  

• What’s your budget and team size? If you have a lean team and tight budget, start with open source. The bigger your data team is, the more complex the data teams becomes. You’re quickly unable to know everything that is going into the data stack, which makes building the right testing strategy hard.
  
• How urgent is the need? DIY can take months. Vendors can deliver value quickly. If you’re already dealing with constant data fires, waiting a year isn’t realistic.
  
• How complex is your stack? A handful of stable pipelines? DIY might suffice. A large, fast-growing, multi-system environment? Vendor platforms shine here.
  
• Do you need advanced features? If anomaly detection, lineage, or SLA reporting are must-haves, it’s hard to replicate those in-house.
  

A Hybrid Approach: Combine dbt tests with Advanced Monitoring

Many teams land somewhere in between. They keep using dbt tests or Great Expectations for known checks tied to business rules, while layering on a commercial tool for unknown anomalies, root cause analysis, and incident resolution.

This approach balances cost and coverage: you keep control where you want it, and offload the heavy lifting where it makes sense.

Conclusion

Data observability isn’t optional anymore. The only real choice is how you implement it.

• Build if your environment is small, your budget is tight, or you want full control. Just be realistic about the ongoing effort.
  
• Buy if your data landscape is complex, your incidents are costly, or your team can’t spare months to build and maintain infrastructure.
  
• Blend if you want the best of both, open source for custom rules, a platform for scale and automation. Look for platforms that can ingest your own tests and pair them with monitors to give you one overview into the health of your data.
  

At the end of the day, what matters is trust. Trust that your dashboards are right, that your ML models aren’t training on the wrong data, and that your business leaders can act on data without hesitation. Whether you build, buy, or do a mix, make observability part of your culture.

Fixing broken data is painful, but fixing broken trust is harder.

FAQ:

When should you buy a data observability platform instead of building?

Buying makes sense when data reliability is business-critical. Building in-house takes months of engineering time and requires ongoing maintenance. Vendor platforms deliver faster time-to-value, advanced features like anomaly detection and lineage, and shift support risk away from your team. Building is only worth it if observability itself is your core IP or your stack is so unique that no vendor can support it.

What are the best open source tools for data observability?

The most common open-source building blocks include:

• Great Expectations (GX): Define validation rules for datasets.
  
• Soda Core / Soda SQL: YAML-based checks with alerting.
  
• dbt tests + Elementary: Native dbt testing with monitoring and reporting.
  
• Apache Griffin, AWS Deequ, MobyDQ: Useful for big data and streaming checks.
  
• Custom SQL/Python scripts: For simple row counts, distributions, and schema checks.

These cover the basics, but scaling brings hidden costs. Managing hundreds of checks, wiring integrations, and fixing breakage often consumes more time than teams expect.

How can you calculate the ROI of data observability?

Think about ROI in terms of avoided costs and saved time:

• Incident prevention: How many firefighting hours are avoided each quarter?
  
• Protected revenue: What would one bad pipeline or decision have cost? (Research shows poor data quality impacts up to a third of revenue.)
  
• Team efficiency: Engineers freed from maintaining tests can focus on analytics and product work.
  
• Trust: Reduced “data downtime” builds confidence across the business.
  

If a platform costs $100K a year but prevents even one $1M incident, the ROI case is already clear.

dbt tests vs dedicated data observability

• dbt tests are best for known issues: null checks, duplicates, referential integrity, and business rules tied to transformations.
  
• Observability platforms are built for unknown issues: anomalies, schema drift, freshness problems, or distribution shifts you didn’t anticipate. They also add lineage, incident workflows, and root cause analysis.
  

Most teams run both: dbt tests for the “known knowns,” and observability tools for the “unknown unknowns” at scale.

What’s the difference between known unknowns and unknown unknowns?

• Known unknowns are problems you can anticipate and test for, like a column that must never be null. Tools such as dbt or Great Expectations handle these well.
  
• Unknown unknowns are issues you don’t expect, such as a schema change upstream, a sudden drop in volume, or a silent distribution shift. Rule-based tests rarely catch them.
  

This is where observability platforms shine, surfacing the unexpected before it erodes trust in your data.