An intelligence-driven data science and behaviour change company which turns motion data into contextual insights and using behavioural change techniques to personalize engagement for safer and sustainable mobility and wellbeing experiences.
Challenge
The company experienced bottlenecks in their existing data architecture and was in need of scaling and optimising its platform to collect and analyse data from mobile devices.
Solution
The company had built an AWS-based cloud platform to process data from their mobile application SDK by collecting smartphone sensor data. This platform was designed according to a Lambda architecture with both a speed layer for real time processing and a batch layer for daily processing of sensor data. While the platform leverages cloud computing and modern technologies, a number of performance and scalability bottlenecks still remained and made that only a limited number of users where supported.
Klarrio analysed the cloud architecture and conducted load tests to identify the performance bottlenecks. Over a short period of time, the most important bottlenecks were resolved and scalability improved with a factor of 50 at the same AWS cloud infrastructure cost.
The Technology behind
- Scala
- Apache Spark
- Kafka
- Elasticsearch
”“Things work out best for those who make the best of how things work out.”
— John Wooden
Results
Scale | Improved messaging layer from 4K to 200K supported persistent connections.
Cost | AWS cost remained the same despite the increase in scalability.
Speed | deployed within 3 months.
200
From 4K to 200K persistent connections
50
more concurrent connections
0
difference in cloud infrastructure costs
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