Learn How You Can Get Real Time Insights From Your Mobility Data using Condense

Connected mobility often feels like a promise that’s always just over the horizon buzzwords like telematics, predictive analytics, and digital twins dominate the conversation. But in this webinar, the promise turned into proof. 

Over the course of an hour, we watched a live build of a production-grade streaming pipeline a working system ingesting telematics data, applying real-time transformations, and delivering actionable alerts to enterprise channels. Not a canned demo, not a simulation video. A pipeline you could deploy tomorrow to manage fleets at national scale. 

Why Streaming is Non-Negotiable for Vehicle Data 

Kishore from Bosch MPS set the stage by reframing the modern vehicle as a “data center on wheels.” 

Each vehicle continuously emits telemetry: 

• Location & GPS for navigation and logistics. 

• CAN bus frames carrying signals like RPM, gear, throttle, braking. 

• Sensor data like tire pressure, fuel level, and engine temperature. 

• Behavioral signals like overspeeding, harsh braking, and idling. 

On average, a connected truck sends data every 6–10 seconds. With advancing networks (4G/5G), intervals are shrinking toward 1–2 seconds. For fleets of 100,000+ vehicles, that’s hundreds of thousands of events per second. 

Traditional batch systems fail here: 

• ETL jobs introduce delays. 

• Databases struggle with write-heavy telemetry. 

• By the time insights arrive, the window to act prevent an accident, warn a driver, reroute a fleet has closed. 

The answer is streaming-first architectures, where every event is processed the moment it arrives. This is what makes predictive maintenance possible, what allows real-time driver scoring, and what empowers OEMs to design components based on live, global telemetry feedback. 

Condense: Kafka, but Verticalized for Mobility 

This is the space where Condense, Zeliot’s flagship platform, was engineered to operate. 

At its backbone is Apache Kafka, chosen for its durability, distributed partitioning, and horizontal scalability. Kafka guarantees ordering within partitions and ensures no event is lost, even under heavy throughput. But vanilla Kafka requires significant expertise managing brokers, configuring retention, ensuring consumer groups scale, wiring transformations. 

Condense builds on Kafka with a mobility-native abstraction layer: 

BYOC (Bring Your Own Cloud) 

• Condense is deployed directly into the customer’s AWS, Azure, or GCP tenant. 

• No data leaves the enterprise’s cloud boundary ensuring data sovereignty and compliance with privacy regulations. 

• Enterprises can leverage their own reserved cloud credits and existing security frameworks. 

Prebuilt Mobility Connectors 

• CAN, OBD-II, GPS ingestion built-in. 

• Out-of-the-box support for major telematics providers like Teltonika, iTriangle, and Volvo Trucks. 

• Future-proof for V2X, dashcams, and lidar feeds, reducing custom integration overhead. 

Transformation Layer 

• No-code rules: domain users can define business logic without writing a single line of code (e.g., flag overspeeding above 100 km/h). 

• GitOps-native custom transforms: developers can push logic directly from GitHub/GitLab repos. 

• Stateful processing with KSQL: enabling trip formation, SLA monitoring, driver scoring, and anomaly detection at scale. 

Elastic Scaling with Cost Control 

• Kafka brokers and Condense transforms scale horizontally as load increases. 

• During fleet idle periods (e.g., night hours), Condense scales workloads down, cutting cloud spend without impacting availability. 

• Fine-grained resource control: CPU/RAM limits per connector or transform, with auto-scaling toggles. 

This verticalization abstracts the boilerplate: 

• No Kafka ops teams. 

• No bespoke observability stack setup. 

• No microservice sprawl. 

• Just focus on the domain logic that creates business value. 

The Live Build: Pipeline in Action 

The most powerful moment came when Sudeep from Zeliot opened a blank workspace and started building, live: 

Ingestion 

• A simulated telematics stream generated JSON events with fields like vehicle_id, location, speed, RPM, fuel_level, and tire_pressure. 

• Data was ingested into a Kafka topic: raw_data. 

Transformation 

• A no-code rule was defined: 

1. If speed > 100 km/h, flag as overspeed. 

2. Write flagged events to alert_data. 

3. Continue clean telemetry downstream. 

• Rule deployed instantly no code, no restarts. 

Custom Output 

• Using Condense’s GitOps IDE, a custom Teams connector was deployed. 

• It listened to alert_data and pushed notifications directly to a Microsoft Teams channel. 

• Within seconds, overspeed alerts appeared in chat real-time driver monitoring, proven live. 

Scalability Demonstrated 

• Kafka partitioning, replication, and retention handled transparently. 

• Same design could scale from 1 vehicle → 100,000+ vehicles without re-architecture. 

In under 60 minutes, a fully operational, production-ready pipeline was online. Something that typically takes months of engineering was condensed into an hour. 

From Streaming to Intelligence: BytEdge AI 

Once the streams were flowing, BytEdge showcased how raw events become predictive intelligence. Their agentic AI layer added: 

Driver Behavior Analysis 

1. Linking overspeeding and RPM patterns to aggressive driving styles. 

2. Estimating impact on fuel efficiency, emissions, and wear. 

Predictive Maintenance 

1. Calculating Remaining Useful Life (RUL) for tires, clutches, and brakes. 

2. Proactively suggesting maintenance schedules to minimize downtime. 

Conversational AI Interfaces 

1. Operators could query: “Show me vehicles at risk of clutch failure in the next 2 weeks.” 

2. AI responded with anomalies, predictions, and recommended actions. 

Digital Twin Design Optimization 

1. OEMs used real-world data to simulate component stress in virtual twin environments. 

2. Design cycles for brakes and clutches cut from 18 months to ~9 months. 

Together, Condense + BytEdge delivered an end-to-end stack: 

1. Real-time ingestion. 

2. Live rule-based transformation. 

3. Predictive, AI-driven intelligence. 

4. All within the customer’s own cloud. 

Key Technical Takeaways 

• Kafka-native at the core: guaranteeing durability, ordering, and high throughput. 

• Mobility connectors remove integration pain: no reinventing the wheel for CAN, OBD-II, or GPS. 

• BYOC = true data control: no vendor access, no compliance gray zones. 

• Elastic scaling = financial efficiency: cloud costs aligned with actual fleet usage. 

• AI layer transforms telemetry into foresight: predictive maintenance, driver behavior insights, and accelerated OEM R&D. 

Why This Matters 

The session proved one thing clearly: the future of connected mobility isn’t about dashboards it’s about real-time, actionable pipelines. 

• Fleets reduce breakdowns, downtime, and accidents. 

• OEMs gain faster design cycles and better feedback loops. 

• Enterprises unlock predictive insights without massive engineering overhead. 

This is the new baseline: production-ready, Kafka-native, BYOC streaming pipelines that scale from day one. 

Watch the On-Demand Webinar 

This wasn’t a theoretical session. It was a live demonstration of building connected mobility infrastructure end-to-end, in real time. 

Watch the complete recording here