Customer Case Study


ZEISS Group is a leading manufacturer of high-end optical systems in various fields like microscopy, camera lenses, industrial metal-ology and also semiconductor manufacturing.



Vertical Use Case

Predictive Maintenance

Technical Use Case

  • Data Ingest and ETL
  • Streaming
  • Machine Learning

The Challenges

  • They were not able to scale on a single machine to process and analyze a massive volume of unstructured data that needed to be prepared for downstream analytics.
  • Spending too much time managing infrastructure and clusters too away from their ability to build new products and bring them to market./li>

The Solution

Azure Databricks provides ZEISS Group with a unified analytics platform that simplifies operations and accelerates data preparation to accelerate data science driven innovation.

  • Highly scalable, fully managed, Azure service that unifies both data engineering and data science in a single platform.
  • Automated cluster management simplifies the provisioning of clusters at any scale.
  • Support for multiple languages (SQL, Scala, Python, R) to ensure all team members are productive within the collaborative notebook environment.
  • Unification of both batch data sts and live stream data from their IOT devices, allowing them to reuse code across both workloads which significantly reduces data engineering effort.
  • Jobs scheduler allows you to schedule Spark jobs and reduces the complexity around things like specifying dependencies, complex constraints like timeouts, and establishing retry policies.
  • REST API helps them to automatically build and deploy the jobs that they are writing which was not possible prior to using Databricks.

Databricks allows us to focus on delivering a great product, rather than being bogged down by infrastructure or the complexities of building a data pipelines.

Jan-Philipp Simen: Data Scientist at ZEISS Group