AWS uVI Workload Migration
Redesigned KUGU's energy consumption calculation platform for parallelisation, scalability, and reduced production load.
Overview
A core functionality of KUGU Home's platform is the calculation of energy and warm water consumption for end customers. To support their expanding customer base, DataMax collaborated with the KUGU team to redesign this system from the ground up — focusing on scalability, parallelisation, and a clean separation of transactional and analytical workloads.
Challenge
The existing setup processed customer data sequentially, creating a bottleneck as the customer base grew. The challenge was to break down the entire consumption calculation procedure into distinct, parallelisable steps that could run simultaneously for different customers — while ensuring the batch workload had zero negative impact on customer-facing services.
Approach
We deconstructed the process into clearly defined, independent stages and introduced parallel execution across all customers. We also made a strategic distinction between transactional and analytical workloads to optimise efficiency throughout.
Solution
We implemented a data lakehouse architecture capable of processing up to 10 million records per day, using Athena, Spark, Iceberg, and DynamoDB to parallelise consumption calculations per customer. All DevOps activities were automated for smooth, reliable deployments. We introduced automated and load testing, adopted Infrastructure-as-Code for transparent and replicable infrastructure, added comprehensive monitoring and logging, and partially automated manual plausibility checks to reduce human error.
Results
"DataMax demonstrated remarkable expertise in reengineering and monitoring our data pipeline, resulting in a significant improvement in performance and reliability. Their proactive approach, deep understanding of our needs, and exceptional collaboration made them a highly recommended partner for any organisation seeking help in data engineering in cloud. By working with DataMax we were able to blend our engineering together to create a seamless and high impactful piece of work. We hope to work with them more in the future."
Scott Williams — CPTO, KUGU HomeReady to accelerate your AI journey?
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