Case study · Technology Management
Distributed Big Data Processing Architecture
A data-processing architecture for a system that handles millions of records daily, reliably and at scale.
10M+
Records per day
99.9%
Uptime
50%
Faster processing
Auto
Elastic scaling
Challenge
The previous infrastructure could not sustain high data volumes; processing slowed and service quality suffered.
Approach
A distributed architecture was designed and deployed on scalable infrastructure so the system performs without serious degradation as load grows.
Our role
01
Architecture Design
Defined a distributed, scalable structure.
02
Optimization
Improved resource utilization and processing time.
03
Deployment
Deployed the system and established monitoring.
TechnologiesApache SparkKubernetesDockerKafka
