A leading company in the healthcare industry has been providing value and care to its clients worldwide for over 20 years. With more than 5,000 employees, this healthcare provider focuses on developing, manufacturing, and distributing continuous glucose monitoring systems for diabetes management.
Pearson, founded in the 19th century, is one of the world leaders in providing education services all around the globe. Francisco Muniz is the Performance Architect for Pearson, responsible for Performance Alignment across Pearson’s Virtual Learning. This position entails working with many different parts of the organization, such as Architecture, Development, and QA. As such, Francisco was leading and overseeing the important Octoperf and Loadrunner software switching project.
Kubernetes is hard. Last year, we started the developer experience product at American Airlines. As we transitioned into the later half of 2020 and into 2021, we wanted to tackle Kubernetes app deployments. We aimed to make it easy for the users to do the right things, no matter how difficult those tasks were. Through our Kubernetes journey, we created reproducible patterns for application teams to use to make things even easier.
Founded in 1934 as a postal and telephone betting service, William Hill was historically synonymous with the world of horse racing. Since then, the company has continued to grow into other sporting avenues, with the retail arm—which opened in 1966—now amounting to more than 1,400 shops. Fully embracing the potential of the digital revolution, the company now comprises over 10,000 employees over six brands, operating through offices in ten countries.
Bayer Crop Science uses Google Cloud to analyze billions of acres of land to better understand the characteristics of the soil that produces our food crops. Bayer’s teams of data scientists are leveraging services from across Google Cloud to load, store, analyze, and visualize geospatial data to develop unique business insights. And because much of this important work is done using publicly-available data, you can too!
Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing.
