Inside IoT Query:PTL, layers & governance
A PostgreSQL-compatible lakehouse for telematics data analytics — layered schemas, strong client isolation, and regional hosting, built for architects, security teams, and senior data engineers.
Data architecture at a glance
IoT Query is built around the Private Telematics Lakehouse (PTL) — a multi-schema, layered data warehouse purpose-built for telematics data analytics, combining the scalability of a data lake with the performance of a structured warehouse.
Raw, transformation, business metrics – technical view
IoT Query employs a medallion architecture with three progressive layers — each optimized for different analytical needs and user personas.
Raw data layer – full-fidelity foundation
The foundational layer containing all raw, minimally processed telematics and business data. Original detail and schema are preserved with uniform naming conventions.
What it enables
- •Trace issues and detect anomalies down to the original sensor reading
- •Run advanced analytics on full-fidelity, time-series data
- •Power exploratory data science and custom feature engineering
Example: Correlate engine RPM, throttle position, and fuel level readings at each timestamp to detect anomalies invisible in aggregated reports.
Transformation layer – cleansed & analytics-ready
Validated, cleaned, and enriched data merged with business context. Structured into query-friendly tables with computed derived fields and standardized units.
What it enables
- •Work with validated, outlier-filtered, and time-regularized data
- •Analyze trip segments, idle events, and geofence visits without manual joins
- •Explore driver behavior and early-stage predictive insights on pre-joined IoT and business data
Example: Query 'idle time within geofences by driver and time of day' without manually joining multiple raw tables.
Business metrics layer – business-ready KPIs
Pre-aggregated, highly curated data marts tailored to business-level metrics. Ready for immediate use in dashboards and decision-making.
What it enables
- •Pull daily or weekly KPIs by region, vehicle, or driver — no complex SQL required
- •Power executive dashboards and operational reports with plug-and-play datasets
- •Train ML models on feature-rich, business-ready datasets
Example: A table of weekly fleet utilization and maintenance status metrics, normalized across currencies and time zones for global comparisons.
Want help mapping your fleet data onto these layers?
Schedule a consultation about personalized fleet analyticsPerformance, retention & scalability
Built for fleet telematics analytics at scale — high-frequency data, billions of records, fast query performance, and flexible retention strategies.
Virtually unlimited storage
Retain years of detailed tracking data. Configurable retention policies — 6 months, 1 year, 5 years, or more — based on your needs.
Fast interactive queries
Complex joins and aggregations execute efficiently thanks to indexing, partitioning, and query optimization under the hood.
Flexible retention policies
Choose retention windows that balance cost and compliance. Archive older data to cold storage while keeping recent data hot.
Connect the tools your team already uses
Standard PostgreSQL compatibility means your team is productive on day one, with no new security review, no vendor lock-in, and no extra infrastructure to provision or maintain. Any tool with a PostgreSQL driver connects directly — protecting the BI, dev, and data science investments you've already made.
BI & visualization
Turn fleet data into dashboards
Commercial
- Microsoft Power BI
- Tableau
- Qlik Sense
- Looker / Google Looker Studio
- MicroStrategy
Open source
- Apache Superset
- Grafana
- Metabase
- Redash
- Evidence
"Connect directly — no data export or intermediate transformation needed."
Data engineering
Build and orchestrate pipelines
Transformation
- dbt (data build tool)
- SQLMesh
- Lightdash
Orchestration
- Apache Airflow
- Prefect
- Dagster
- dbt Cloud
"Model, transform, and schedule fleet data pipelines on top of your lakehouse."
Developer & query tools
Explore and debug your fleet data
Desktop clients
- DBeaver
- pgAdmin
- DataGrip (JetBrains)
- TablePlus
- Postico
Web / cloud
- Retool
- Beekeeper Studio
- Metabase (cloud)
"Connect any PostgreSQL client for ad-hoc exploration and query development."
Python & data science
Programmatic fleet analytics
Libraries
- pandas + SQLAlchemy
- Polars
- psycopg2 / asyncpg
- JDBC / ODBC drivers
Notebooks & apps
- Jupyter Notebooks
- Streamlit apps
- AWS SageMaker Studio
- Azure ML Studio
"Full SQL access from your Python data science and ML workflows."
Business & productivity
Fleet insights in familiar tools
Spreadsheets
- Microsoft Excel (via ODBC)
- Google Sheets (via connector)
- LibreOffice Calc
Low-code apps
- Retool
- AppSmith
- Budibase
- NocoDB
"Bring telematics data directly into spreadsheets and low-code apps your team already uses."
AI & machine learning
Power the next fleet intelligence layer
ML platforms
- AWS SageMaker
- Azure Machine Learning
- Google Vertex AI
- Databricks
AI frameworks
- LangChain / LangGraph
- OpenAI API integrations
- Hugging Face
- LlamaIndex
"Feed clean, structured fleet data into AI and ML pipelines for predictive fleet analytics."
PostgreSQL-compatible · Standard SQL · Any tool, any team
Data residency & deployment options
Choose the deployment model that fits your scale, compliance, and control requirements — from shared multi-tenant cloud to dedicated private instances.
Shared multi-tenant cloud
Cost-effective, fast onboarding for mid-sized fleets and TSPs.
- Logical tenant separation with dedicated schemas
- Shared infrastructure managed by Navixy
- Suitable for fleets of 50-5,000 assets
Dedicated / private instances
For large enterprises, government fleets, or highly regulated sectors.
- Full database isolation with dedicated compute and storage
- Custom retention policies and performance tuning
- VPN/VPC peering and custom SLA options
Available regional zones
IoT Query can be deployed in multiple geographic zones to ensure data never leaves your required jurisdiction.
US-based infrastructure with SOC 2-aligned controls for security and availability.
Data stays in EU-based data centers, fully aligned with GDPR data residency requirements.
Deployment options that keep data within the region to meet local regulatory requirements.
Regional hosting for APAC fleets, reducing latency and keeping data close to your operations.
Security & compliance posture
Enterprise-grade security with tenant isolation, encryption, regional hosting, and compliance alignment to SOC 2, ISO 27001, and GDPR/CCPA standards.
Tenant isolation
Each client's data resides in a dedicated schema or database instance. No cross-tenant joins or data leakage — complete logical separation.
End-to-end encryption
TLS 1.3 in transit, AES-256 at rest. Region-specific key management with hardware security modules (HSMs) for enhanced protection.
Role-based access control
Fine-grained permissions via Navixy platform roles. Optional two-factor authentication (2FA) and IP whitelisting for sensitive environments.
Compliance alignment
Designed to meet SOC 2 Type II and ISO 27001 standards. GDPR and CCPA compliant with data residency options.
Continuous monitoring
24/7 security monitoring, regular audits, and incident response procedures. XSS and SQL injection prevention with strict input validation.
Regional data residency
Host your data in EU, North America, Middle East, or APAC to meet local data sovereignty and compliance requirements.
Certification status
Navixy's security program is aligned with SOC 2 and ISO 27001 best practices. Formal certifications are in progress and expected within 2025.
Discuss your architecture with Navixy experts
Evaluating IoT Query or planning an integration? Our team will walk you through the architecture, security, and deployment options for your case — no commitment required.