A Semantic Schema for Data Quality Management in a Multi-Tenant Data Platform

TL;DR

This paper presents a semantic schema management system that enhances data quality and supports schema evolution in a multi-tenant data platform, enabling scalable data processing for a global marketplace.

Contribution

It introduces a semantic schema management approach with versioning, testing, and transformation capabilities tailored for multi-tenant data platforms.

Findings

System processes over one billion events daily

Supports schema evolution with versioning and testing

Operates successfully in a production environment

Abstract

Schibsted Media Group is a global marketplace company with presence in more than 20 countries. It is undergoing a digital transformation to convert data silos to a multi-tenant system based on a common data platform. Good data quality based on a common schema on the semantic level is essential for building successful data-driven products across marketplaces. To solve this challenge, we developed the data quality tooling based on a semantic schema management system to support schema evolution with versioning, testing and transformation. It can monitor the data quality requirements for different applications and handle incoming data consisting of multiple schema versions. Today the system is operating in production and processes over one billion events per day for over 100 applications.