Data Processing Optimisation for True Digital

The Vodworks team helped enhance the data processing efficiency for True Digital’s project at the onset of the COVID-19 pandemic to predict the movement and spread of the virus using the location data of its over 30 million customers.

  • 5 Trillions of datapoints optimised
  • Reduced infrastructure cost by 50%
People wearing masks

Brief

True Digital, a subsidiary of one of Thailand's largest telecommunications providers, initiated a project at the onset of the COVID-19 pandemic to predict the movement and spread of the virus using the location data of its over 30 million customers.

Initially, processing this data took over 15 hours, hindering the ability to provide real-time insights. Vodworks was onboarded to optimise data processing, reducing the time to less than 10 minutes and enabling near real-time tracking of movement for approximately one-third of the Thai population.

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Approach

Vodworks' role in the project was to enhance the data processing efficiency. True Digital had already established a system utilising GCP and MemCache for processing and tracking customer location data. However, the processing time exceeded 15 hours, hindering the timely availability of data crucial for tracking the spread of COVID-19.

The project team comprised Vodworks' data engineers and solution architects. They collaborated to design and implement the optimised data architecture necessary for expediting the processing of vast amounts of location data generated by True's customer base.

Vodworks optimised the data processing by reconfiguring the architecture, leveraging BigQuery, GCS, and Kafka to significantly reduce processing time to less than 10 minutes.

Tech stack

Google-Cloud-platform-icon
Google-cloud-storage-icon
BigQuery-icon
kafka-icon
MemCached-icon

Project management

The project team comprised Vodworks' data engineers and solution architects. They collaborated to design and implement the optimised data architecture necessary for expediting the processing of vast amounts of location data generated by True's customer base.

The primary challenge faced during the project was the sheer volume of data that needed to be processed. With over 30 million customers continuously transmitting location data to cell towers every few seconds, the processing workload was immense. Optimising the processing of this massive dataset while ensuring accuracy and reliability posed a significant technical hurdle for the team.

Outcome

  • Reduced location data processing time from 15 hrs to under 10 mins

  • Optimized hiring process to 2 weeks 

  • Reduced infrastructure cost by 50%

  • 5 Trillions of datapoints optimised

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