Systems | Development | Analytics | API | Testing

The Cloudera Operational Database (COD) experience is a managed dbPaaS solution which abstracts the underlying cluster instance as a Database. It can auto-scale based on the workload utilization of the cluster and will be adding the ability to auto-tune (better performance within the existing infrastructure footprint) and auto-heal (resolve operational problems automatically) later this year.

A Shift Towards Industry 4.0 Is Improving Manufacturing Efficiency And Increasing Innovation In Part II of our series with Michael Ger, Managing Director of Manufacturing and Automotive at Cloudera, he looks in greater detail at how AI, big data, and machine learning are impacting connected living and the evolution of autonomous driving.

Cloudera’s Operational Database provides unparalleled scale and flexibility for applications, enabling enterprises to bring together and process data of all types and from more sources, while providing developers with the flexibility they need. In this blog, we’ll look into capabilities that make Operational Database the right choice for hyperscale.

Since the financial crisis of 2008, regulators have been consistently working to identify emerging risks that can potentially result in financial stability events. The growth in cloud adoption across the Financial Services Industry (FSI) and the associated increase in reliance on third-party infrastructure providers has gained the attention of regulators at global, regional, and national levels.

Approximately 300,000 restaurants and food service operators across the United States rely on US Foods as their national distributor for food and supplies. In return, US Foods takes a holistic approach to servicing customers by going beyond food offerings. The distributor provides a comprehensive suite of e-commerce technology and business solutions that help restaurants manage their entire business.

Connected Manufacturing’s Pivot to an Enterprise Data Solution Connected Manufacturing is at a turning point and it is catalyzed by a real, measurable change and shift in data types – real-time and time-series data is growing 50% faster than latent or static data forms and streaming analytics projected to grow at a 28% CAGR, leaving legacy data platforms that specialize in static historical data solutions, functioning on-prem or in discrete clouds, inadequate in addressing today’s rea