ZEISS

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.

Industry

Manufacturing

Vertical Use Case

Predictive Maintenance

Technical Use Case

Data Ingest and ETL
Streaming
Machine Learning

Customer Testimonial

Video Spark + AI Summit Talk

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

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