Challenge
A leading automotive manufacturer wanted to integrate data from many different sources into its data lakehouse. Because the data types and source systems are very different and complex, a robust, standardized framework for integration needed to be developed that would, among other things, detect data quality problems early on and thus lead to an improvement in quality.
Approach
Our project team developed a standardized framework based on the Medallion Architecture with Bronze, Silver and Gold layers. During the project, the team successfully integrated data from more than ten sources. The data engineers at statworx attached great importance to data quality in order to meet the client’s high requirements. In workshops with various departments, they prioritized data quality and developed a dashboard to monitor it. They also introduced automated tests and deployments using CI/CD pipelines to reduce errors. For the technical implementation, the team used Databricks and Azure components such as Azure Data Factory.
Results
Thanks to the new framework, the car manufacturer can now integrate data more efficiently and reliably. Automated tests and faster onboarding of new data sources make the company more flexible. It can react more quickly to changes and make decisions based on solid data. The Data Quality Dashboard helps to keep a constant eye on and improve data quality.
This development shows how important it is to focus on quality and efficiency when integrating data. With the right system and the right tools, a company can make better use of its data.