Introduction

Paradime is an AI-powered workspace for analytics teams that consolidates the entire analytics workflow into one platform. With proven ROI of 50-83% productivity gains and 10x faster speeds, Paradime eliminates tool sprawl by integrating everything from development to production orchestration. DinoAI co-pilot writes SQL, generates documentation, and refactors models, while Paradime Bolt provides production-grade orchestration with 100% uptime. Companies like Tide, Customer.io, and Emma achieve 25-50% faster development cycles with complete visibility through column-level lineage and real-time monitoring.

Data quality is the foundation of trustworthy analytics. Without proper monitoring of your dbt tests, data quality issues can silently propagate through your pipelines, eroding stakeholder confidence and leading to costly business decisions based on faulty data. Paradime's Radar Test Dashboard provides analytics teams with comprehensive visibility into test health, enabling proactive identification and resolution of data quality issues before they impact downstream consumers.

What is Paradime Radar Test Dashboard?

Overview of the Tests Dashboard

The Tests Dashboard is part of Paradime's Radar suite, designed specifically for dbt monitoring. It provides a comprehensive view of test results across your entire dbt project, consolidating test outcomes into actionable insights. The dashboard is organized into two main sections—Overview and Detailed—each serving distinct monitoring needs.

This centralized approach enables teams to track test outcomes efficiently and maintain data quality standards without switching between multiple tools or interfaces. By bringing all test-related metrics into one place, teams can quickly identify trends, troubleshoot failures, and ensure their data transformations meet quality expectations.

Key Benefits of Using the Test Dashboard

The Test Dashboard delivers real-time visibility into test health and status, allowing teams to catch issues as they emerge rather than discovering them after data consumers are already affected. This proactive approach to data quality management significantly reduces the time spent on reactive troubleshooting.

Historical tracking capabilities enable teams to identify patterns in test performance over time, correlating failures with code deployments or upstream data source changes. Impact assessment features help prioritize which failures require immediate attention based on the number of rows affected and downstream dependencies. This streamlined approach to troubleshooting accelerates resolution times and improves overall data pipeline reliability.

Getting Started with the Test Dashboard

Before accessing the Radar Test Dashboard, you'll need to complete the dbt monitoring setup in Radar's Getting Started guide. This includes configuring test execution in your dbt project and ensuring proper integration with Paradime's monitoring infrastructure.

Once setup is complete, navigate to the Radar suite within Paradime and locate the Tests Dashboard section. The interface is intuitively organized with clear sections for overview metrics and detailed analytics, making it easy to find the information you need regardless of whether you're conducting a quick health check or deep-dive investigation.

Overview Section: High-Level Test Monitoring

Test Execution and Daily Monitoring

The Overview section provides a high-level summary of all test results, displaying the total number of tests categorized by status: passed, warn, errored, and failed. This at-a-glance view enables quick assessment of your overall data quality posture.

Daily test execution patterns help identify trends and recurring issues over time. Use the "Select date range" dropdown to focus your analysis on specific time periods, whether you're investigating a recent incident or tracking long-term quality improvements. Monitoring these trends helps teams understand if data quality is improving, degrading, or remaining stable.

Analyzing Test Failures

The Overview section provides detailed information about non-passing tests, including the associated models and impacted row counts. This context is crucial for prioritizing remediation efforts—a test failing on 10,000 rows demands more urgent attention than one affecting a handful of records.

By reviewing failure details in context with model dependencies, teams can quickly assess whether issues are isolated or potentially cascading through downstream transformations. This insight enables more efficient troubleshooting and helps prevent small issues from becoming major incidents.

Identifying Top Models with Test Issues

One of the most valuable features of the Overview section is the ability to identify dbt models with the highest number of problematic tests. This model-level perspective helps focus remediation efforts on the areas of your project that need the most attention.

Models appearing consistently at the top of this list may indicate underlying data quality issues with source systems, overly complex transformations that need refactoring, or insufficient test coverage that's failing to catch issues earlier in the pipeline. Tracking model-level test health metrics enables teams to take a strategic approach to improving data quality rather than reacting to individual test failures in isolation.

Detailed Section: Deep-Dive Test Analytics

Model-Specific Test Results

The Detailed section allows you to filter by individual dbt models for focused analysis. Using the "Choose a model" dropdown, you can view the complete test coverage for any specific model, along with its pass rate and test distribution.

This model-specific view is invaluable when investigating issues related to particular datasets or responding to stakeholder concerns about specific data products. Understanding the test composition for each model helps assess whether coverage is adequate and whether the right types of tests are being applied.

Historical Test Performance Tracking

Visualizing test results evolution over time is one of the most powerful features of the Detailed section. Historical trends help identify patterns in test failures and successes, making it possible to correlate changes with specific code updates or data source modifications.

For example, if a test that historically passed 100% of the time suddenly starts failing after a deployment, the historical view immediately highlights this regression. Similarly, tracking improvement in data quality over time provides measurable evidence of the effectiveness of data quality initiatives, which can be valuable for reporting to stakeholders and leadership.

Column-Level Test Analysis

Granular insights at the column level enable teams to identify specific data fields requiring attention. When tests fail repeatedly on the same columns, it signals systemic issues that need addressing at the source or in transformation logic.

This column-level visibility is particularly useful for large dimensional tables where different columns may have varying quality characteristics. By pinpointing exactly which columns are problematic, teams can implement targeted fixes rather than having to investigate entire models.

Impact Analysis and Assessment

Monitoring the number of rows affected by test failures provides crucial context for prioritization decisions. A unique key test failing on 5% of rows has very different implications than one failing on 95% of rows.

Understanding downstream effects on data consumers helps assess business impact, enabling teams to communicate effectively with stakeholders about the severity of issues and expected resolution timelines. Prioritizing fixes based on impact magnitude ensures that limited engineering resources are directed toward the problems that matter most to the business.

Best Practices for dbt Testing

Implementing Comprehensive Test Coverage

Start by adding generic tests for important columns—uniqueness, non-nullness, and accepted values are foundational checks that catch many common data quality issues. Implement referential integrity checks between models to ensure relationships remain consistent as data flows through transformations.

For business-specific validation rules, custom tests enable you to encode domain knowledge directly into your testing framework. Shifting data quality checks left in the development cycle—testing during development rather than only in production—catches issues earlier when they're cheaper and easier to fix.

Test Configuration and Optimization

Configure appropriate severity levels for your tests. Use "warn" for tests that indicate potential issues but shouldn't block pipelines, and "error" for critical tests where failures should halt execution. The store_failures configuration captures detailed failure data, making it easier to investigate and resolve issues.

Unit testing for complex transformation logic validates that your business logic works correctly on known inputs before applying it to production data. Balance test coverage with execution performance—comprehensive testing is valuable, but tests that significantly slow your pipelines may need optimization or sampling strategies.

Establishing Testing Workflows

Integrate tests into CI/CD pipelines to catch issues before code reaches production. Run tests regularly on production schedules to monitor ongoing data quality, not just at deployment time.

Set up automated alerts for test failures so teams are notified immediately when issues arise. Document test expectations and rationale so that future team members understand the purpose of each test and can make informed decisions about maintenance and modifications.

Monitoring and Alerting Strategies

Setting Up Effective Monitoring

Configure date range filters to focus on relevant time periods for your analysis. Monitor daily trends to catch issues early—a test that starts failing every morning at 3 AM suggests an upstream data delivery problem.

Track test execution patterns and anomalies using the Overview section for quick health checks. Regular monitoring cadences, such as reviewing the dashboard during daily standups or weekly data quality reviews, help teams stay ahead of issues.

Alert Configuration Best Practices

Integrate Paradime with tools like PagerDuty, DataDog, or Slack to ensure the right people are notified when tests fail. Set appropriate alert thresholds—alerting on every warning may create noise, while only alerting on critical failures might miss important issues.

Assign clear ownership for test failures so alerts reach people who can take action. Establish escalation procedures for critical issues to ensure that high-priority problems are addressed even if the primary owner is unavailable.

Responding to Test Failures

When investigating test failures, use the detailed dashboard insights to understand root causes. Examine historical trends for context—is this a new issue or a recurring problem? Assess impact before determining priority, focusing first on failures affecting the most rows or the most critical downstream consumers.

Document resolutions for future reference. When the same or similar issues arise again, having documentation of previous investigations and fixes dramatically accelerates resolution.

Troubleshooting Common Test Issues

Use historical performance views to spot recurring issues that may indicate systemic problems rather than one-time anomalies. Analyze column-level data to pinpoint problem areas within larger models, and review model dependencies to trace failures back to their upstream causes.

Correlating failures with deployment or data changes helps determine whether issues stem from code changes, data quality degradation in source systems, or environmental factors. Start remediation efforts with high-impact, high-frequency failures—these deliver the most value for the time invested.

Use impacted row counts to gauge severity, and validate fixes by comparing test results before and after changes. Monitor resolution effectiveness over time to ensure fixes truly addressed the underlying issues rather than just masking symptoms.

Advanced Use Cases

Correlating Test Results with Development Cycles

Track how test results change after deployments to identify regressions introduced by code changes. This correlation helps teams understand the impact of their development practices on data quality and adjust workflows accordingly.

Use test metrics to validate improvement initiatives—if you're working to improve data quality in a particular area, historical test data provides objective evidence of progress. Sprint retrospectives can leverage test trends to inform discussions about technical debt and quality improvements.

Data Quality Reporting for Stakeholders

Generate executive summaries from dashboard insights to communicate data reliability metrics to business users. Tracking data quality KPIs over time demonstrates the value of analytics engineering efforts and builds confidence in data products.

Showing improvement in data trustworthiness through quantitative metrics helps secure buy-in for continued investment in data quality initiatives and infrastructure.

Integrating with Broader Data Observability

Combine test monitoring with source freshness checks to get a complete picture of data pipeline health. Monitor model execution alongside test results to understand whether performance issues are correlated with quality problems.

Tracking end-to-end data pipeline health across tests, freshness, and execution provides comprehensive visibility across the entire data stack, enabling truly proactive data operations.

Conclusion

Paradime's Radar Test Dashboard provides comprehensive visibility into dbt test health, enabling analytics teams to maintain high data quality standards with minimal manual effort. The combination of overview and detailed sections supports both quick health checks and in-depth investigations, making it suitable for daily monitoring and deep troubleshooting alike.

Proactive monitoring prevents data quality issues from reaching consumers, protecting stakeholder trust and ensuring analytics remain reliable. Historical tracking capabilities help identify trends and measure improvements, providing objective evidence of data quality program effectiveness.

To get started, complete the dbt monitoring setup in Radar and explore the other dashboards for models, schedules, and sources to gain complete visibility into your data operations. Implement alerting for critical test failures and establish a regular review cadence for test dashboard insights. With these practices in place, your team will be well-equipped to maintain exceptional data quality and deliver trustworthy analytics to your organization.