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Cloud-Native Data Pipeline and Custom ML Solution for E-Commerce Scalability
  1. case
  2. Cloud-Native Data Pipeline and Custom ML Solution for E-Commerce Scalability

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Cloud-Native Data Pipeline and Custom ML Solution for E-Commerce Scalability

valor-software.com
eCommerce
Retail

Current Challenges in Data Processing and Machine Learning

The client faces high operational costs, inconsistent performance during peak loads, and unreliable machine learning predictions due to a rigid AWS-based architecture. Inefficient resource utilization, scalability limitations, and communication delays across global teams further hinder decision-making and customer satisfaction.

About the Client

Mid-sized e-commerce retailer specializing in online sales and customer-centric solutions

Project Goals for Enhanced Data Infrastructure

  • Reduce data processing inefficiencies and operational costs
  • Implement a scalable, real-time data pipeline for peak load resilience
  • Develop a custom machine learning solution for accurate personalized recommendations
  • Improve cross-team communication and decision-making efficiency

Core System Functionalities and Features

  • Stream processing with Apache Beam/Dataflow for real-time ETL
  • Pub/Sub message queuing for event-driven architecture
  • BigQuery as centralized data warehouse for scalable analytics
  • Custom TensorFlow ML pipeline for personalized recommendations
  • Global team collaboration tools for cross-timezone coordination

Target Technology Stack

Google Cloud Platform (GCP)
Dataflow
Pub/Sub
BigQuery
AI Platform
TensorFlow

System Integration Requirements

  • Existing e-commerce platform APIs
  • Third-party analytics tools
  • Global team communication platforms

Non-Functional Requirements

  • Auto-scaling during traffic spikes
  • Sub-second query latency in analytics
  • Cost-optimized cloud resource utilization
  • Enterprise-grade data security and compliance

Expected Business Impact of Modernized Data Infrastructure

The solution will reduce cloud costs by 40-60%, enable real-time analytics at scale, and improve ML prediction accuracy for personalized recommendations by 30%, directly increasing conversion rates. Streamlined operations will save 10+ management hours monthly while supporting global team collaboration across time zones.

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