Skip to content

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.

abstract-digital-data-flow-background-image (1)

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.

ecommerce-banner-1

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

The implementation of the new AWS-based big data infrastructure delivered measurable improvements in performance, efficiency, and business intelligence.
022-data analytics

Faster Data Processing

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

027-strategic plan

Better Insights

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

017-deployment-1

Higher Efficiency

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

015-customer feedback

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.

case study-new relic