Parasoft is showcasing its latest product innovations at embedded world Exhibition, booth 4-318, including new GenAI integration with Microsoft Visual Studio Code (VS Code) to optimize test automation of safety-critical applications while reducing development time, cost, and risk.
Cloudera DataFlow for the Public Cloud Launched
August 16, 2021
Cloudera announced the launch of Cloudera DataFlow for the Public Cloud, a cloud-native service for data flows to process hybrid streaming workloads on the Cloudera Data Platform (CDP).
With Cloudera DataFlow for the Public Cloud, users can now automate complex data flow operations, boost the operational efficiency of streaming data flows with auto-scaling capabilities, and cut down on cloud costs by eliminating infrastructure sizing guesswork.
Data-in-Motion volumes are expected to grow exponentially and many organizations already leverage Apache NiFi to capture and process data across hybrid cloud architectures by visually designing no-code data flows. While the cloud provides an easy outlet for processing or storing massive volumes, there are multiple challenges that must be addressed. Deploying dozens of sophisticated data flows into a single cluster can lead to operational and monitoring challenges. When multiple NiFi flows compete for the same resources, it can lead to performance issues. IT administrators often choose larger infrastructure sizes out of caution, leading to underutilization and high costs. Finally, companies want a pay-as-you-go model to avoid paying for resources not in use.
“Cloudera DataFlow automates and manages cloud-native data flows on Kubernetes...” said Dinesh Chandrasekhar, Head of Product Marketing, Data-in-Motion at Cloudera. “Now it is easy for our customers to boost the operational efficiency of their streaming workloads and save on infrastructure costs in the public cloud.”
Cloudera DataFlow for the Public Cloud is a powerful cloud-native service for NiFi on Kubernetes and includes key operational and monitoring capabilities that address these challenges easily and aren’t typically available with basic data flow services:
- Central Flow Catalog for manageability, discovery, and version control
- Central dashboard for monitoring, troubleshooting and performance tuning of data flows across multiple cloud clusters
- Simple deployment wizard and robust APIs for auto-scaling flows on Kubernetes managed by CDP
- Pre-built flows called “ReadyFlows” for some of the common streaming use cases
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