ITOps Times news digest: Unravel for AWS Databricks, Instana now collects W3C User Timing events, cloudtamer.io enables continuous compliance

Unravel Data has announced new monitoring support for AWS Databricks. Unravel for AWS Databricks provides monitoring, troubleshooting, and application performance management for the platform.

Key features of Unravel for AWS Databricks include APM, root cause analysis, and reporting, alerting, and dashboards. 

Unravel already supported other AWS tools, such as Amazon EMR and Cloudera/Hortonworks on IaaS for AWS. 

Instana now collects W3C User Timing events
The APM solution can now integrate data from W3C Trace Context and User Timing APIs. Developers don’t have to update any code to enable this functionality. 

“Developers and Operations staff need maximum observability to work as effectively as possible,” said Chris Farrell, Instana technical director and APM strategist. “By combining data from proprietary sources and W3C User Timing and Trace Context, Instana provides the best of both visibility worlds, a powerful way to provide maximum visibility with minimum effort.”

Cloudtamer.io enables continuous compliance in the cloud
This will allow companies to detect, report, and remediate policy violations across cloud providers. Apart from compliance checks, cloudtamer.io provides a near-real-time view of how resources are complying policies. 

In addition to offering a policy engine, the company also offers many checks and sample policies to help companies bootstrap their compliance posture. 

Synopsys announced support for TensorFlow for Microcontrolers on ARC EM and ARC HS processors
According to Synopsys, TensorFlow Lite for Microcontrollers is designed for executing machine learning models on memory-constrained devices. 

By enabling TensorFlow Lite for Microcontrollers on ARC processors, developers can deploy machine learning inferencing at the edge while reducing the impact of network latency. 

“TensorFlow Lite for Microcontrollers enables developers to quickly generate machine learning models for easy deployment of neural networks on low-power devices,” said Pete Warden, technical lead at Google. “The optimized implementation of the software on Synopsys’ ARC processors allows users to efficiently develop voice, gesture classification, and other machine learning-based applications on resource-constrained devices.”