Developed Scalable and Cost-effective Solution on Google Cloud from Scratch
Case Study
The Client
Ampere Electric is a leading player in India’s electric two-wheeler market, founded in 2008. Committed to sustainable mobility solutions, Ampere designs and manufactures a range of comfortable, reliable, and cost-effective electric scooters for the discerning Indian rider. Their unwavering focus on innovation and customer-centricity has resulted in over 2 lakh units sold and a robust network exceeding 210 dealerships across 139 cities.
Business Context
In the rapidly evolving Indian EV market, traditional Bluetooth-based connectivity provided basic data on individual vehicles and lacked the scope and accessibility needed for customer engagement. Tech-savvy consumers, accustomed to seamless digital experiences in other sectors, demanded a more sophisticated approach, prompting Ampere to seek a better solution.
Challenge:
The primary challenge was to build a cost-efficient, scalable, and robust telematics platform from scratch. As the IoT devices evolved and new variants were introduced, Ampere Electric’s database structure must be tailored accordingly. Addressing these complexities required meticulous planning and adaptation of the platform’s architecture to guarantee optimal performance and interoperability.
Opportunity:
Considering the robust API platform offered by Google Cloud and its ability to process real-time data, Niveus suggested building the telematics platform on GCP. Utilizing GCP, Ampere can onboard new variants and devices via the APIs. Niveus suggested incorporating Google Cloud Platform (GCP) features like BigQuery, Dataflow, and Looker Studio to provide real-time data insights, interactive dashboards, and reports on vehicle performance, driver behavior, and route optimization.
Business Solution
Niveus provided Ampere Electric users with a seamless, secure, and user-friendly experience. We designed and developed a scalable IoT-based Telematic Platform available on Android and iOS devices that provides real-time insights for all the electric vehicles.
Ampere Electric was able to track the performance for all the metrics, identify potential issues, and take proactive steps by raising alerts to address them quickly. This allowed them to optimize their operations and increase their efficiency. This phased approach ensures cost-effectiveness while paving the way for data-driven decisions.
- The pilot phase comprised 80% IoT devices and 20% Bluetooth-connected mobile phones. These devices generated data from different components of the vehicle, such as the battery, motor, and engine/flags
- Developed a scalable, self-managed, and fault-tolerant Dataflow pipeline using Apache Beam
- VerneMQ was used for interacting with MQTT clients and pushing the data to Pub/Sub topics
- Dataflow was used to process data on a real-time basis before loading it into the Cloud BigQuery
- BigQuery was the target layer, with Looker Studio over its table to create dashboards for the monitoring team where they could track the vehicles in real time.
- Google Cloud Storage was used for long-term data storage and archival purposes
The platform generates trip summaries alongside daily summaries of Ampere vehicle activity. The dashboard displays vital vehicle performance metrics, including total vehicles running and at a halt, total trips, and motor/throttle error occurrences.
Key Value Delivered
- The pilot phase consists of 80% IoT devices and 20% Bluetooth connected mobile phones. These devices generated data from different components of the vehicle, such as the battery, motor, and engine/flags
- Verne was used for interacting with MQTT clients and push the data to Pub/Sub topics
- Dataflow was used to process data on a real-time basis before loading it into the Cloud BigQuery
- BigQuery was the target layer with Looker Studio over its table to create dashboards for the monitoring team
- Google Cloud Storage was used for long term data storage & archival purposes
Technology Stack
BigQuery
Dataflow
Cloud BigTable
Cloud Pub/Sub
Cloud Storage
Looker Studio
Apache Beam
Customer Feedback
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