Optimising Big Data Infrastructure with AWS
Building a scalable, efficient, and data-driven e-commerce infrastructure with AWS and Kubernetes, enabling faster data processing, optimised analytics, and automated workflows. By leveraging AWS-native solutions, the new architecture reduced processing times, improved operational efficiency, and provided actionable insights, enhancing customer engagement and driving smarter business decisions.
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Client Overview
The client is a SaaS provider offering cloud-based treasury solutions for financial institutions and banks. The platform supports comprehensive front office, mid office, and back office treasury transactions, operations, and reporting. As the product matured and the user base expanded, the client required a more resilient and scalable development infrastructure to enable faster iteration, enhanced security, and environment-specific testing.

Challenge
The existing infrastructure was unable to support the growing demands of big data processing, creating challenges in analytics, performance, and scalability. Key obstacles included:
Data Processing Bottlenecks
The infrastructure struggled to handle large data sets efficiently, leading to delays in generating business-critical insights.
Limited Analytical Capabilities
Data silos and fragmented storage prevented comprehensive analysis of customer behaviour, sales trends, and operational efficiency.
Manual and Inefficient Workflows
Data ingestion and processing pipelines required significant manual intervention, increasing operational overhead and slowing decision-making.
Scalability and Resource Constraints
With fluctuating data volumes, the existing setup lacked the flexibility to scale dynamically, leading to resource inefficiencies.
Solution
To address these challenges, Bion designed and deployed a scalable, secure, and highly automated big data infrastructure on AWS. The key components included:
1) Kubernetes Cluster Deployment
- Scalable Environment: Deployed a Kubernetes (K8s) cluster on AWS to ensure a flexible and scalable processing environment.
- Resource Optimisation: Configured the cluster to dynamically allocate resources, improving efficiency and reducing operational costs.
2) Big Data Processing Frameworks
- Integration of Data Tools: Implemented Apache Spark and Hadoop within the Kubernetes cluster for distributed big data processing.
- Automated Workflows: Established data pipelines to automate the ingestion, transformation, and analysis of diverse datasets, including customer interactions and sales trends.
3) Data Storage Solutions
- Scalable Storage: Utilised Amazon S3 and Amazon RDS for structured and unstructured data, ensuring cost-effective and secure storage.
- Centralised Data Lake: Consolidated disparate data sources into a unified data lake, enabling comprehensive analytics and reporting.
Results

Faster Data Processing
The scalable Kubernetes environment improved performance, reducing data processing times by 60%.

Better Insights
With optimised data workflows and processing frameworks, actionable insights increased by 40%, enabling data-driven business strategies.

Higher Efficiency
Automation of data workflows reduced operational costs by 25%, allowing IT teams to focus on innovation and strategic initiatives.

Improved Experience
Real-time data analysis has boosted customer engagement, improved inventory management, and strengthened market positioning.
Technology Stack
The following technologies were utilised to successfully enhance data processing, analytics, and operational efficiency.
- Cloud Computing: Amazon VPC, IAM, EKS, EC2, ECR, Secrets Manager, RDS, CloudFront, S3
- Infrastructure as Code: Terraform/Terragrunt
By leveraging this robust technology stack, the client achieved a scalable, data-driven, and cost-efficient cloud environment, ensuring long-term business growth and competitive advantage.
