Why Choose Kafka While Building Mobility Solutions?

Mobility is No Longer a Batch Problem

Modern mobility systems are real-time by nature. Vehicles emit telemetry every second, routes evolve with traffic conditions, driver behavior fluctuates moment to moment, and passengers or logistics systems demand immediate feedback. These systems are always-on, stateful, and volume-intensive. 

As mobility moves from reactive dashboards to predictive, event-driven intelligence, infrastructure must evolve to match this pace. That’s why Apache Kafka has become the backbone of connected mobility solutions. 

Kafka: The Backbone for Real-Time Mobility Infrastructure 

Kafka is a distributed log built for high-throughput, fault-tolerant, real-time event streaming. In the context of mobility, it enables: 

• Continuous ingestion of telemetry from CAN, OBD-II, GPS, ADAS, and mobile apps 

• Replayable logs for debugging, model re-training, or compliance 

• Partitioned scalability across geographies, vehicle types, or customers 

• Durable storage of millions of events per second across thousands of streams 

• Loose coupling between producers (vehicles, sensors, drivers) and consumers (analytics, dashboards, alerting systems)

Kafka is ideal when data comes from hundreds of thousands of vehicles, flowing in millions of small messages, often from heterogeneous sources, and must be processed with predictable latency. 

But Kafka Alone Doesn’t Solve the Whole Problem 

In real-world fleet, logistics, and automotive platforms, Kafka solves the transport problem. But teams still face critical production challenges: 

Provisioning and Scaling Kafka Clusters 

Infrastructure bottlenecks appear quickly with telemetry volume scaling linearly across vehicles. 

Schema Management and Multi-source Normalization 

Ingesting heterogeneous payloads from mixed vehicle OEMs, telematics providers, and third-party systems. 

Stateful Processing and Alert Logic 

Kafka does not offer inbuilt tools for joining, windowing, alerting, or tracking vehicle state over time. 

End-to-End Observability 

From ingestion to action, tracing per-message processing across the stack is non-trivial. 

Infrastructure Complexity 

Kafka typically requires Zookeeper, brokers, producers, consumers, metrics pipelines, and multiple microservices for transformation and delivery. 

These challenges compound when dealing with hundreds of millions of data points per day, sourced from dozens of telematics protocols, and when regulatory constraints demand data sovereignty and per-tenant isolation. 

Condense: A Kafka-Powered Platform Veriticalized for Real-Time Mobility 

Condense is a fully managed, streaming-native platform verticalized on top of Kafka — abstracting its complexity and delivering production ready real-time infrastructure tailored for mobility ecosystems. 

Rather than offering Kafka as a service, Condense provides: 

• Fully Managed Kafka Infrastructure with autoscaling, 99.95% uptime, and no manual tuning 

• BYOC (Bring Your Own Cloud) deployment for full data sovereignty and compliance 

• Verticalized Connectors for ingesting CAN, GPS, ADAS, and OBD-II from diverse sources 

• Prebuilt Transforms for geofencing, behavior scoring, and fleet alerts 

• In-Stream State Tracking for vehicle context, driver profiles, and trip correlation 

• Built-in IDE for coding and deploying complex logic in Python, JavaScript, Go, or Java 

• No-Code Logic Blocks for operations teams to manage business workflows 

• Stream Observability with end-to-end logs, alerting, retries, DLQs, and replay support 

With Condense, developers focus on use case realization, not infrastructure management. Kafka is leveraged as the high-throughput engine — Condense orchestrates everything else required to run large-scale, mission-critical mobility applications. 

Success Story #1: Real-Time AI Fleet Platform Processing 328 Million++  Events/Month 

One emerging fleet-tech platform built a fully AI-powered mobility intelligence stack — handling driver behavior scoring, fuel optimization, route monitoring, and geospatial automation.

Key Challenges: 

• Needed to ingest and process hundreds of millions of vehicle data packets per month 

• Required real-time AI model execution on in-motion data 

• Demanded full cloud cost optimization and no infrastructure drag 

How Condense Helped: 

• Kafka-based ingestion handled telemetry from tens of thousands of vehicles, all streaming simultaneously 

• BYOC deployment allowed hosting within the customer’s cloud, ensuring 100% data control 

• Prebuilt geofence transforms and parsing utilities accelerated development by months 

• Built-in IDE enabled engineers to author AI-based fuel consumption models natively, reducing external dependencies 

• Auto-scaling handled surge loads without operator intervention 

• Real-time event pipelines powered instant notifications, behavioral flags, and compliance tracking 

Outcome

• Over 328 million events processed monthly 

• Thousands of fleets onboarded 

• Zero infrastructure bottlenecks, zero downtime 

• Cloud spend optimized through intelligent streaming resource allocation  

Success Story #2: Enterprise-Grade OEM Ingesting Over 129 TB of Data Per Month 

A major commercial vehicle OEM with over 190,000 connected trucks and buses uses Condense to power one of India’s largest mobility data ecosystems. 

The Scale: 

• 129+ TB of data/month inbound (plus outbound streams) 

• Millions of events per hour from connected vehicles 

• Real-time applications across: 

  • Driver performance analytics 

  • Predictive maintenance 

  • Route optimization 

  • Uptime command centers 

    
    

What Condense Delivered

• Kafka-powered ingestion layer, fully managed with sub-second delivery SLAs 

• Telemetry harmonization from diverse ECUs, vendors, and telematics units 

• Edge-to-cloud data flows orchestrated with Condense’s schema-bound connectors 

• Prebuilt transforms for geospatial enrichment, trip scoring, and fault code parsing 

• Built-in observability ensured compliance-grade traceability 

• Full BYOC deployment met all internal infosec and governance requirements 

Outcome: 

• Seamless ingestion and processing of petabyte-scale mobility data 

• Real-time insights available to operations, analytics, and command centers 

• Five-year strategic roadmap anchored on Condense for predictive intelligence, electric vehicle support, and next-gen fleet services 

Kafka is the Transport. Condense is the Real-Time Operating Layer. 

When building mobility systems at national or global scale, the conversation must move beyond “Can we ingest the data?” to: 

• Can we track state and make decisions in motion? 

• Can we scale to millions of messages per second, across geographies? 

• Can we do this without building 20 backend microservices? 

• Can we guarantee uptime, traceability, and compliance? 

Kafka alone is not enough to answer those questions — it’s a transport layer. You still need a real-time control system to define behavior, track state, apply rules, and deliver outcomes. 

That’s the role of Condense. 

Kafka is Critical — Condense Makes It Production-Ready for Mobility 

Kafka remains a foundational technology for mobility platforms — enabling ingestion of diverse, high-volume, low-latency streams. 

But production-grade use cases in mobility require far more than a broker. They require: 

• Vertical intelligence for vehicle data types 

• Full stack observability 

• Built-in transforms and logic orchestration 

• Developer tooling and operational safety 

• Scalable, BYOC-native infrastructure 

Condense delivers all of this, powered by Kafka — with the simplicity, scalability, and precision that real-time mobility demands. 

Whether you're building a new fleet intelligence platform or modernizing a commercial vehicle ecosystem, Condense offers a proven foundation to scale confidently, act instantly, and innovate continuously — with no backend complexity.