用Snowflake数据构建车队预测性维护分析仪表盘

Fleet telemetry systems generate thousands of operational events every day. Pressure readings, vibration signals, equipment usage, and maintenance records can provide valuable insight into system health-but only if the data can be queried and analyzed effectively.

In this analysis, we’ll use Snowflake data and SQL queries to build a predictive maintenance dashboard for vehicle telemetry monitoring. We’ll start with raw telemetry records, create analytical datasets using SQL, and transform those datasets into interactive dashboard components for operational analysis.

By the end of this guide, you’ll learn how to:

• Connect a dashboard to Snowflake data
• Query telemetry data using SQL
• Build KPI metrics from aggregated results
• Create analytical visualizations for operational monitoring
• Construct a predictive maintenance dashboard from Snowflake data

Overview

In this article, we’ll build a predictive maintenance dashboard using vehicle telemetry data stored in Snowflake.

The workflow follows a simple pattern:

The workflow consists of four stages:

1. Telemetry records are stored in Snowflake.
2. SQL queries aggregate and analyze operational data.
3. Query results become reusable datasets.
4. Dashboard components visualize the resulting metrics.

This approach separates data storage, analytical logic, and visualization while keeping SQL at the center of the workflow.

The final dashboard includes:

• Fleet health KPIs
• Risk distribution analysis
• Vehicle usage risk segmentation
• Correlation analysis
• Time-series monitoring
• Fleet health scoring

The Problem

Modern fleets continuously generate telemetry data from operational systems.

Engineers and operators often need answers to questions such as:

• Which vehicles are operating under elevated risk conditions?
• Does heavy equipment usage increase failure risk?
• Are vibration and pressure signals correlated with operational issues?
• How does fleet risk change over time?
• Which metrics should be monitored to identify potential failures early?

While telemetry systems generate large volumes of data, extracting meaningful insights requires aggregation, analysis, and visualization.

In this article, we’ll use Snowflake as the analytical foundation for answering these questions.

Dataset Overview

For this tutorial, we’ll use a vehicle telemetry dataset containing 25,000 records.

Each row represents a telemetry observation from a fleet vehicle.

The dataset contains the following fields:

This structure resembles telemetry datasets commonly used in predictive maintenance and operational monitoring systems.

Step 1: Connect to Snowflake

The first step is establishing a connection to Snowflake.

Provide the following information:

• Snowflake Account
• Warehouse
• Database
• Schema

Once the connection is established, SQL queries can be executed directly against the telemetry dataset.

After connecting, we can begin building analytical datasets.

Step 2: Create Fleet Health KPIs

A common starting point for operational dashboards is a set of KPI metrics that summarize overall system conditions.

The following query calculates monthly operational metrics.

SELECT 
 TO_CHAR(event_time, 'YYYY-MM') AS month, 
 COUNT(*) AS total_records, 
 AVG(failure_risk) AS avg_failure_risk, 
 AVG(pressure_score) AS avg_pressure, 
 AVG(vibration_score) AS avg_vibration_score 
FROM aps_vehicle_data 
GROUP BY month 
ORDER BY month;

This query produces:

• Total Records
• Average Failure Risk
• Average Pressure Score
• Average Vibration Score

These metrics provide a high-level overview of fleet conditions and can be visualized as KPI cards.

At this stage, we’ve transformed thousands of telemetry records into a concise operational summary.

Step 3: Analyze Vehicle Usage and Operational Risk

Telemetry data becomes more useful when we move beyond summary statistics and begin analyzing operational behavior.

The following query groups vehicles by usage intensity and categorizes records according to risk level.

SELECT 
CASE 
WHEN usage_hours < 2500 THEN 'Low Usage' 
WHEN usage_hours < 6000 THEN 'Medium Usage' 
ELSE 'High Usage' 
END AS usage_group, 

CASE 
WHEN failure_risk < 30 THEN 'Low Risk' 
WHEN failure_risk < 60 THEN 'Medium Risk' 
ELSE 'High Risk' 
END AS risk_level, 

COUNT(*) AS vehicle_count 
FROM aps_vehicle_data 
GROUP BY usage_group, risk_level 
ORDER BY usage_group;

Visualizing this result as a stacked bar chart allows us to compare risk distributions across operational categories.

This type of segmentation helps identify whether heavily utilized equipment tends to exhibit elevated risk conditions.

Step 4: Explore Risk Distribution

Understanding how operational risk is distributed across a fleet can reveal whether issues are isolated or widespread.

The following query groups records into risk buckets.

SELECT 
 FLOOR(failure_risk / 10) * 10 AS risk_bucket, 
 COUNT(*) AS vehicle_count 
FROM aps_vehicle_data 
GROUP BY risk_bucket 
ORDER BY risk_bucket;

Visualizing these results as an area chart provides an overview of fleet-wide risk distribution.

Rather than inspecting individual records, engineers can quickly understand the overall risk profile of the fleet.

Step 5: Measure Risk Composition

Risk categories provide another useful perspective.

The following query classifies records into low, medium, and high-risk groups.

SELECT 
 CASE 
 WHEN failure_risk < 30 THEN 'Low Risk' 
 WHEN failure_risk < 60 THEN 'Medium Risk' 
 ELSE 'High Risk' 
 END AS risk_level, 
 COUNT(*) AS total_vehicles 
FROM aps_vehicle_data 
GROUP BY risk_level 
ORDER BY total_vehicles DESC;

A pie chart can then be used to visualize the composition of the fleet.

This makes it easy to understand how operational risk is distributed across the population of monitored assets.

Step 6: Investigate Usage and Failure Risk

One of the most common predictive maintenance questions is whether equipment usage contributes to increased failure risk.

The following query supports that analysis.

SELECT 
 usage_hours, 
 AVG(failure_risk) AS avg_failure_risk 
FROM aps_vehicle_data 
GROUP BY usage_hours 
ORDER BY usage_hours;

A scatter or point chart can reveal relationships between utilization and operational risk.

Patterns identified here may help determine whether heavily utilized equipment requires additional monitoring or maintenance.

Step 7: Examine Temperature Effects

Temperature is another operational factor that can influence equipment performance.

The following query aggregates risk by temperature.

SELECT 
 temperature, 
 AVG(failure_risk) AS avg_failure_risk 
FROM aps_vehicle_data 
GROUP BY temperature 
ORDER BY temperature;

Visualizing the results as a line chart helps identify potential relationships between operating temperature and failure risk.

Environmental conditions often provide important context when interpreting operational behavior.

Step 8: Monitor Risk Over Time

Time-series analysis is a critical component of operational monitoring.

The following query calculates average risk by date.

SELECT 
 DATE(event_time) AS date, 
 AVG(failure_risk) AS avg_risk 
FROM aps_vehicle_data 
GROUP BY DATE(event_time) 
ORDER BY date;

Visualizing the results as an area chart provides a chronological view of fleet health.

This view helps identify emerging trends and changing operational conditions.

Step 9: Calculate a Fleet Health Score

Operational dashboards often benefit from a single summary metric.

The following query converts average failure risk into a fleet health score.

SELECT 
 ROUND(100 - AVG(failure_risk), 2) AS fleet_health_score 
FROM aps_vehicle_data;

The result can be visualized using a gauge chart.

While detailed telemetry remains important, summary indicators can help operators quickly assess overall conditions.

Final Dashboard

After combining the KPI metrics and analytical views, we obtain a complete predictive maintenance dashboard.

The dashboard includes:

• Fleet health KPIs
• Fleet health score
• Risk distribution analysis
• Risk composition analysis
• Usage-based risk segmentation
• Correlation views
• Time-series monitoring

All visualizations are driven by SQL query outputs generated from Snowflake data.

Key Insights

Several useful observations can be derived from this workflow.

• Usage Intensity Influences Risk — Vehicles in higher usage categories tend to contribute a larger proportion of medium and high-risk observations.
• Risk Is Not Uniform — Risk distribution analysis reveals that operational risk varies significantly across the fleet population.
• Multiple Perspectives Improve Monitoring — KPI metrics, distributions, correlations, and time-series views each reveal different aspects of system behavior.
• SQL Serves as the Analytical Layer — The same Snowflake dataset can support multiple dashboard views simply by changing aggregation logic and query structure.

Conclusion

• In this tutorial, we built a predictive maintenance dashboard using Snowflake data and SQL-based analytics.
• Starting with raw telemetry records, we created reusable query outputs that powered KPI metrics, risk analysis views, operational monitoring charts, and a fleet health indicator.
• The same approach can be applied to a wide range of telemetry-driven applications, including industrial monitoring, IoT systems, logistics operations, and equipment maintenance workflows.
• By keeping SQL at the center of the analytical process, developers can transform structured Snowflake data into operational insights that support monitoring and decision-making.

Snowflake-Powered Fleet Predictive Maintenance Analytics Dashboard was originally published in Snowflake Builders Blog: Data Engineers, App Developers, AI, & Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.