Case Study

Streamlined Data Management for Ultraviolette Reduces Costs by 70% with CloudThat

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AWS IAM, Amazon CloudWatch, AWS Support, Amazon Redshift, Amazon QuickSight, Amazon MSK, Amazon DynamoDB, AWS Lambda


Boosting Storage Efficiency, Security, and Query Performance with Scalable Solutions

About the Client

Ultraviolette Automotive Pvt. Ltd. was founded in 2016 by Narayan Subramaniam and Niraj Rajmohan, pioneers sustainable mobility solutions through innovative design and energy-efficient technology, developing India’s first high-performance electric vehicles and energy infrastructure.



Enhanced Storage Efficiency

Only Authorized Personnel

Improved Data Security


Optimized Query Performance and Cost Reduction

The Challenge

Analyzing vast real-time data from Ultraviolette bikes presents challenges due to high volume and absence of a data lake solution. The absence of a data lake complicates extracting actionable insights. Challenges include handling complex queries across 350+ fields, fault count analysis on 150+ fields, and integrating user and vehicle data.


  • Bike data is collected via AWS IoT Core and routed to Amazon MSK. 
  • AWS Lambda converts binary data to JSON and distributes it across Kafka topics. 
  • Logstash preprocesses JSON data, adding calculated fields as needed. 
  • Processed data is stored in Amazon OpenSearch and Amazon S3 for real-time and batch analysis respectively. 
  • Data in Amazon S3 is compressed and partitioned by date. 
  • AWS Glue crawls data and creates tables for easy access. 
  • Amazon Redshift utilizes Spectrum tables for querying and combines data from Amazon DynamoDB. 
  • Aggregated data is stored in Parquet format in Amazon S3. 
  • Amazon QuickSight creates dashboards from Redshift data. 

The Results

Optimizing storage efficiency by 60-70%, enhancing security, boosting query performance, executing complex queries efficiently, integrating diverse datasets, and providing scalable solutions for processing terabytes of data.

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