Case Study: Carl Zeiss achieves scalable data processing and faster product innovation with Databricks

Carl Zeiss - Customer Case Study

ZEISS Group, a global leader in high‑end optical systems for microscopy, camera lenses, industrial metrology and semiconductor manufacturing, faced a data scaling and operational challenge: they could not process and analyze massive volumes of unstructured data on a single machine, and excessive time spent managing infrastructure and clusters distracted the team from building and shipping new products.

By adopting Azure Databricks, ZEISS consolidated data engineering and data science on a highly scalable, fully managed platform with automated cluster management, multi‑language collaborative notebooks, unified batch and streaming support for IoT data, job scheduling and a REST API for automation. The result was dramatically faster data preparation, reduced engineering effort through reusable code and automated pipelines, and refocused teams on delivering product innovation rather than infrastructure management.

Carl Zeiss

Jan-Philipp Simen

Data Scientist