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Next-Generation Data Platform Development for Automotive Aftermarket Data Management

sigma.software

Automotive

Data Platform Inefficiencies & Scalability Challenges

TecAlliance's existing digital platform, built on outdated data warehouse software, struggles with inefficiencies, scalability limitations, and maintaining data consistency. Manual processes for data updates and returns are time-consuming and prone to errors. The platform needs a modern, robust solution to handle increasing data volumes and support future growth.

About the Client

Leading provider of data management and processes in the automotive aftermarket, specializing in vehicle and spare parts data, repair information, and e-commerce solutions.

Project Goals

Develop a scalable and cost-efficient data platform based on modern cloud technologies.
Automate data processing and distribution workflows.
Improve data consistency and accuracy across all systems.
Enhance the B2B marketplace with automated return processes and vendor ratings.
Enable seamless integration of new data sources and modules.

Functional Requirements

Data Lake Management (ingestion, storage, versioning)
Data Transformation & Aggregation
Automated Return Processing (B2B Marketplace)
Vendor Rating Generation
Data Distribution to Internal Products
Customer Portal for data access
Test Automation Framework

Preferred Technologies

AWS (Lambda, Glue, S3)

Apache Hudi

Data Mesh Architecture

Java

Serenity BDD

JUnit

Behavior-Driven Development (BDD)

Data-Driven Testing (DDT)

Required Integrations

Existing TecDoc Catalogue
Internal TecAlliance Product Systems
B2B Marketplace Platform

Non-Functional Requirements

High Scalability (handling 200+ requests/second)
High Performance
Data Consistency & Accuracy
Cost-Effective Maintenance (Serverless Architecture)
Robust Security
High Availability

Expected Business Impact

The project will enable TecAlliance to modernize its data infrastructure, improve operational efficiency, reduce costs, and enhance customer experience. It will facilitate faster data processing, more accurate reporting, and the development of new data-driven services. Ultimately leading to better decision making and increased competitiveness in the automotive aftermarket.