Turn vehicle telemetry into timely service insight. Atomic Fusion provides connected vehicle platform teams with a managed, Kafka-compatible streaming foundation that connects relevant events to the applications responsible for action.
For connected vehicle platform teams building or improving this capability, consider the following situation. A connected fleet reports warning indicators and operating conditions through intermittently available links. A uniform upload schedule can leave service teams waiting for relevant evidence while large backlogs compete with current events. The required context spans vehicle signals, diagnostic codes, trip summaries, device connectivity. When those updates arrive through disconnected or backlogged pipelines, the receiving application must work with incomplete information or wait for reconciliation before presenting a useful next step.
Producer applications or integration adapters publish vehicle signals, diagnostic codes, trip summaries, device connectivity into Atomic Fusion topics, preserving source identifiers, event timestamps, and relevant versions. Atomic accepts, retains, and delivers these records to independently operated consumers. Your processing applications validate signal schemas, associate readings with vehicle configuration, and prepare operational features for monitoring applications.
Those applications then update fleet health views and route abnormal patterns to service teams. Downstream databases hold the resulting operational or analytical views, while services carry out authorized actions. Existing Kafka producers and consumers can be retained when replacing a Kafka streaming layer; new implementations can use the same familiar event interfaces.
Keep driving control on its engineered path; gateways manage consent, sampling, and disconnected buffering.
Atomic delivers 20× higher throughput per partition through distributed storage. Most enterprise topics can fit within one partition from a throughput perspective, reducing partitions added solely for capacity. Consumer processing can still require several partitions. That gives this workflow more ingestion headroom while preserving application-driven choices about keys and parallelism.
Atomic scales up and down elastically with workloads, and customers pay for what they use. Teams do not need to provision the streaming service continuously for peak demand. This helps accommodate uneven event arrival while leaving application processing capacity and source buffering to be planned for their own workload limits.
Atomic separates Kafka request-serving brokers from independently scalable durable storage. Serving capacity can grow without first copying retained partition history onto new brokers. Teams can expand consumption and retained history according to their different needs, giving this workflow room to add analytical or operational consumers as its audience grows.
Atomic encrypts data in transit and at rest, and its distributed storage fleet holds neither plaintext stream contents nor encryption keys. This separation reduces exposure within storage operations while preserving event distribution. Application permissions, data minimization, and retention policies still determine who may use the information and for what purpose.