How the Navixy DataHub Bronze layer powers custom analytics

Most development teams waste valuable resources on telematics data integration challenges, battling API rate limits, building fragile export scripts, and struggling with compliance requirements. Navixy DataHub's Bronze layer eliminates these bottlenecks. Read to explore capabilities that the telematics analytics platform enables for your business.

Key takeaways

• Access raw telematics data with direct SQL, eliminating API bottlenecks and boosting speed of enterprise analytics.
• Design and run custom fleet analytics tailored to unique operational goals and performance metrics.
• Enable live sensor monitoring, anomaly detection, predictive maintenance, and advanced geospatial insights.
• Transform telematics into a scalable engine for enterprise-wide predictive analytics and smarter decisions.

Enterprise-grade fleet analytics without roadblocks

Navixy DataHub provides developers, who work with enterprise-level fleets, direct SQL access to real-time telematics streams, historical datasets, and flexible data schemas design for building custom fleet analytics applications.

Contact Sales to discover the main benefits of a new approach to fleet analytics available with Navixy DataHub.

The Navixy DataHub platform eliminates API rate limits and data access constraints, enabling it to build everything from live operational dashboards to complex multi-dimensional analytics engines. With PostgreSQL compatibility and native integration support for popular BI tools, ML frameworks, and data pipelines, developers can leverage existing skills and toolchains without vendor lock-in.

The underlying Private Telematics Lakehouse (PTL) architecture exposes four key data processing patterns through standard SQL interfaces:

• streaming data ingestion for real-time applications,
• time-series analysis for historical reporting,
• feature engineering pipelines for ML model training,
• cross-table joins for contextual business intelligence.

This architecture gives developers the flexibility to implement custom business logic directly in SQL or extract data for processing in Python, R, or other analytical environments and BI-tools.

Fleet analytics scenarios enabled by the Bronze layer

The Bronze layer exposes raw, structured datasets that developers can query directly for applications such as:

• Real-time monitoring sensor readings

DataHub supports real-time ingestion of telematics data, allowing live dashboards and operational panels to display sensor values such as GPS, speed, RPM, temperature, or fuel levels with minimal delay. This streaming capability provides dispatchers and managers with immediate visibility into fleet statuses, enabling faster troubleshooting, operational awareness, and informed decision-making.

• Anomaly detection

Identify irregularities such as fuel theft, sensor malfunctions, or unauthorized vehicle use through real-time detection frameworks. The DataHub allows to launch statistical approaches, enabling high-confidence alerts based on deviations from historical norms or defined behavioral thresholds.

• Predictive maintenance

Anticipate component failures by analyzing patterns in temperature, RPMs, fault codes, and historical breakdowns. By training machine learning models on this data, businesses can shift from reactive repairs to proactive scheduling — reducing emergency repairs, improving vehicle uptime, and optimizing service intervals.

• Geospatial intelligence

Analyze movement data across time and space to uncover geofences, optimize depot or warehouse locations, and evaluate geographic service coverage. Asset positions can be compared with locations in near real-time, while telematics data adds contextual insights to support better decision-making.

Custom real-time fleet dashboards: precision where every minute counts

The Bronze layer of the Navixy DataHub marks the starting point of a deeper analytical journey capturing raw, unfiltered data in a structured, queryable environment. It is indispensable for building meaningful dashboards or applications and addressing immediate operational challenges through real-time monitoring and historical reporting.

You can retrieve and visualize raw data as-is, or apply logic to create new metrics, KPIs, and custom statuses. Let’s have a look at practical examples.

Use case: real-time sensor monitoring

In a manufacturing plant, operators built a Grafana dashboard to monitor live sensor data — filtering by asset, sensor type, or individual sensors. This helped detect anomalies early, prevent downtime, and improve maintenance response.

The dashboard used SQL queries on raw data, like the following:

select
	o.object_label,
	lcs.sensor_type,
	lcs.device_time,
   lcs.value as last_measurement_value
FROM business_data.latest_calibrated_sensors lcs
LEFT JOIN raw_business_data.objects o ON o.device_id = lcs.device_id
WHERE 1=1
and lcs.sensor_id is not null
ORDER BY lcs.device_time DESC

No complex setup was required — just direct access to inputs and a visualization tool connected to the DataHub.

Use case: fleet status dashboard to custom logic for real

In logistics fleet managers needed dashboards that go beyond standard event categories. Using the Bronze layer of DataHub, they built a “movement status” dashboard based on custom logic — such as minimum speed thresholds and idle time. Here’s a simplified version of the query used:

WITH tracking_data_core AS (
  SELECT * FROM raw_telematics_data.tracking_data_core tdc
  WHERE tdc.device_time >= NOW() - INTERVAL '15 minutes'
    AND tdc.event_id IN 
(2, 802, 803, 804, 811)
  ORDER BY tdc.device_time, tdc.device_id DESC
)
SELECT DISTINCT ON (tdc.device_id)
  tdc.device_id,
  tdc.event_id,  tdc.platform_time,
  tdc.speed / 100 AS speed,
  tdc.latitude / 1e7 AS latitude,
  tdc.longitude / 1e7 AS longitude,
  tdc.altitude / 1e7 AS altitude,
  tdc.device_time,
  CASE 
    WHEN tdc.speed / 100 > 2 
      AND EXTRACT(EPOCH FROM (NOW() - tdc.device_time)) / 60 < 3 
      THEN 'moving'
    WHEN EXTRACT(EPOCH FROM (NOW() - tdc.device_time)) / 60 < 3 
      THEN 'stopped'
    ELSE 'parked'
  END AS moving_status,
  CASE 
    WHEN EXTRACT(EPOCH FROM (NOW() - tdc.device_time)) / 60 <= 1 THEN 'active'
    WHEN EXTRACT(EPOCH FROM (NOW() - tdc.device_time)) / 60 <= 3 THEN 'idle'
    ELSE 'offline'
  END AS connection_status,
  to_char(tdc.device_time, 'YYYY-MM-DD HH24:MI:SS') as last_connect_formatted
FROM tracking_data_core AS tdc
ORDER BY tdc.device_id, tdc.device_time DESC

The strength of this model is its adaptability. For instance, aside from standard statuses, users could define its own custom status, for example status “dwell” — triggered only when a vehicle is within a geofenced warehouse and its door sensor is active. The logic is fully configurable via SQL, giving operations teams direct control over how their fleet is monitored and reported. Or you can use Python to build an application with your own dashboard or report as shown on the screen below.

This approach gives managers real-time oversight and the freedom to evolve logic without vendor intervention or reliance on pre-defined templates.

Leverage DataHub as the gateway to advanced fleet analytics

The DataHub Bronze layer marks a strong starting point in gaining a true understanding of your operations. Once this foundation is established, businesses can move beyond rule-based detection to develop more advanced models.

At this level you can:

• Apply machine learning to identify suspicious trips.
• Build predictive scoring systems to anticipate risky driver behavior.
• Integrate eco-driving insights with maintenance and compliance data to maximize fleet efficiency.

By placing control over both data and logic directly in the hands of decision-makers, Navixy DataHub transforms telematics from static dashboards into a powerful engine of intelligence and automation.

Contact Sales to learn how leverage of the Navixy DataHub Bronze layer can support your analytics and reporting needs.