Analytics

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. By leveraging Spark on Kubernetes as the foundation along with a first class job management API many of our customers have been able to quickly deploy, monitor and manage the life cycle of their spark jobs with ease. In addition, we allowed users to automate their jobs based on a time-based schedule.

Are you ready to turbo-charge your data flows on the cloud for maximum speed and efficiency? We are excited to announce the general availability of Cloudera DataFlow for the Public Cloud (CDF-PC) – a brand new experience on the Cloudera Data Platform (CDP) to address some of the key operational and monitoring challenges of standard Apache NiFi clusters that are overloaded with high-performant flows.

We just announced Cloudera DataFlow for the Public Cloud (CDF-PC), the first cloud-native runtime for Apache NiFi data flows. CDF-PC enables Apache NiFi users to run their existing data flows on a managed, auto-scaling platform with a streamlined way to deploy NiFi data flows and a central monitoring dashboard making it easier than ever before to operate NiFi data flows at scale in the public cloud.

For more than 20 years, OpenTable has connected foodies and novice diners with the restaurants they love. But how does its technology work on the back end? To make a long story short: data. Beyond the app and website, OpenTable provides restaurants with software that manages their floor plans, phone reservations, walk-ins, shift scheduling, turn times, and more.

Every enterprise is trying to collect and analyze data to get better insights into their business. Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis. This whole architecture made a lot of sense when there was a consistent and predictable flow of data to process.