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Implementation of Scalable Lakehouse Architecture for Unified Data Management
  1. case
  2. Implementation of Scalable Lakehouse Architecture for Unified Data Management

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Implementation of Scalable Lakehouse Architecture for Unified Data Management

coderio.com
Automotive
eCommerce
Information technology

Data Management Challenges in Automotive E-commerce

The client faced significant challenges in managing large volumes of heterogeneous data from multiple sources, including vehicle listings, transaction records, and customer interactions. Existing systems lacked scalability, suffered from data silos, and provided limited analytical capabilities, resulting in delayed business insights and suboptimal decision-making.

About the Client

Leading online platform for used car sales leveraging technology for transparent vehicle transactions

Key Project Goals

  • Establish a unified data architecture with three-tier processing layers
  • Implement scalable cloud-based data storage and processing infrastructure
  • Enable real-time analytics through integrated business intelligence tools
  • Improve data governance and security compliance
  • Reduce data processing time by 70% for faster business insights

Core System Functionalities

  • Multi-source data ingestion pipeline with real-time extraction
  • Three-layer architecture (raw, refined, warehouse) with automated data transformation
  • Amazon Redshift integration for BI dashboard implementation
  • Role-based access control for data security
  • Automated data quality validation framework

Technology Stack

AWS Glue
AWS Lambda
AWS S3
AWS MWAA
AWS CloudFormation
PySpark
Python
Amazon Redshift

System Integrations

  • Legacy CRM systems for customer data
  • Vehicle inspection IoT devices
  • Third-party pricing APIs
  • Existing BI visualization tools
  • Payment gateway transaction systems

Non-Functional Requirements

  • Horizontal scalability to handle 10x data growth
  • 99.99% system availability with automated failover
  • End-to-end data encryption and GDPR compliance
  • Real-time processing latency under 500ms
  • Cost-optimized storage tiering strategy

Business Impact Projections

The implementation will enable real-time pricing optimization, reduce data processing delays by 75%, and support 360-degree customer analytics. The architecture will provide a 40% reduction in infrastructure costs through optimized cloud resource utilization while maintaining compliance with automotive industry regulations and data security standards.

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