How We Built Our Own Data Observability Layer (And Why You Might Want To)

In today’s digital landscape, data accuracy and real-time insights are the foundation of successful business operations. Yet, many organizations still face silent failures in their data pipelines, resulting in faulty dashboards, misinformed decisions, and reduced trust in reporting systems. This is where data observability steps in as a crucial strategy to ensure pipeline health, data integrity, and system transparency.

Why Data Observability Matters

For one organization, 40% of their data issues went undetected until stakeholders raised concerns. Delays in spotting and resolving pipeline failures not only hampered operations but also resulted in unreliable reports. Recognizing these inefficiencies, the team decided to build a custom data observability layer tailored to their specific infrastructure and business needs.

Unlike off-the-shelf solutions, which were either costly or incompatible with their stack (Airflow, dbt, Snowflake), the in-house system provided flexible and cost-effective monitoring aligned with internal workflows. By implementing real-time alerts and proactive monitoring, the team transitioned from reactive problem-solving to preventive system improvements.

From Identifying the Problem to Creating a Solution

The journey began with identifying gaps in existing monitoring methods. Reports showed missing data, inaccurate values, or failed updates that remained unnoticed. To counter this, the team first mapped how different departments used data. For instance, dashboards used by executives required absolute precision, whereas internal testing tools could tolerate minor inconsistencies.

Once these usage patterns were defined, the team developed a foundational observability system that monitored key metrics like data freshness, row counts, and schema changes. Upon implementation, teams received immediate alerts via Slack when jobs failed, reducing diagnosis time by 65% and significantly improving data reliability.

Enhancing Observability Through Customization

The organization took observability a step further by adding custom monitors. For instance, the executive dashboard expected fresh rows by the start of each business day. If those records were missing, alerts were instantly sent to the appropriate team. This helped catch problems early—before data ever reached end users.

Datasets were also labeled based on priority. High-value datasets received stricter monitoring, while less critical data had relaxed rules. This ensured optimal resource allocation and improved data pipeline efficiency.

Efficient Ownership and Proactive Strategy

To drive meaningful action, observability must lead to accountability. Ownership of each dataset was clearly defined, with specific teams receiving alerts and protocols to follow. A standard incident response framework was introduced to streamline issue resolution and promote team collaboration.

As the system matured, the organization shifted toward trend-based monitoring. Repeated patterns—like underutilized databases or declining table performance—were flagged early. This proactive stance allowed teams to make long-term improvements and prevent issues before they could disrupt reporting.

Conclusion: Why In-House Observability Works

Developing a custom data observability tool required effort but delivered long-lasting benefits. Tailored to the company’s unique systems and workflows, it ensured precise monitoring, faster issue detection, and increased data trust. Organizations facing similar challenges can start small—with basic automation and alerts—and gradually build an observability system that transforms their entire data strategy.