Performance Monitoring & Analytics Dashboard For A Leading Auto Manufacturer

Case Study


The Client

The client is a multinational automotive manufacturing corporation, headquartered in Mumbai. The company is the leading vehicle manufacturer in India as well as the largest manufacturer of tractors in the world by volume. Their tractor models are known for being powerful, having a high lift-capacity, dual-acting power steering, and more.

Project Objective – Performance Monitoring & Analytics Dashboard

Business value: The client was looking to improve the performance monitoring and analytics dashboard for their existing data platform. The data platform helps farmers and the manufacturer have an overview of their tractors’ performance and their operation parameters. 

The client has a terminal level system where they can monitor the status of their machines. We worked to improve their platform with a data analytics and reporting layer of the machines. The data platform was to provide the farmers with a user-friendly interface that would allow them to leverage dashboards and save time, and improve  efficiency in searching for information from multiple tools.


Business Solution

The client’s data platform for tractor performance analytics is built on GCP. They were looking to add four additional features to it such as, SMS and PUSH notifications in regional languages, sudden drop in fuel and refill alert, as well as new parameter additions such as fuel consumption, average GPS speed, engine RPM, ground speed, wheel slippage, and tractor usage report. Additionally, we have also helped them with BigQuery optimization to improve the quality of their application, integrating cost efficiency, and scaling for better performance.



There are four layers used in the data platform architecture:

  • Ingestion layer accepts data packets from the tractors and these packets are enriched by the meta data.
  • Data pipelines layer comprises of Pub/Sub architecture for the flow of data packets using Cloud Pub/Sub for the processing of data packets using Google Cloud Dataflow 
  • Database Layer comprises a data lake and a data warehouse implemented using BigQuery and data marts implemented using Cloud Big Table. 
  • Frontend layer comprises front-end applications and backend services implemented using Tomcat server on Compute Engine and Google Kubernetes Engine respectively.

Technology Stack

Google Kubernetes engine
Cloud Memorystore
Cloud Pub/Sub
Cloud Dataflow
Cloud SQL

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