Here at Cloudera, we’re committed to helping make the lives of data practitioners as painless as possible. For data scientists, we continue to provide new Applied Machine Learning Prototypes (AMPs), which are open source and available on GitHub. These pre-built reference examples are complete end-to-end data science projects. In Cloudera Machine Learning (CML), you can deploy them with the single click of a button, bringing data scientists that much closer to providing value.
In the first blog of the Universal Data Distribution blog series, we discussed the emerging need within enterprise organizations to take control of their data flows. From origin through all points of consumption both on-prem and in the cloud, all data flows need to be controlled in a simple, secure, universal, scalable, and cost-effective way.
When it comes to hybrid cloud and digital transformation, it’s all about application services and leveraging appropriate on-premise, service provider, and hyperscaler cloud resources and services seamlessly and efficiently.
Google’s data cloud enables customers to drive limitless innovation and unlock the value of their data via its robust offerings under a single, unified interface. By migrating their data ecosystems to Google Cloud, organizations are able to break down their data silos and harness the full potential of their data. However, historically, migrating data warehouses has not been an easy task.
