The latest News and Information on DevOps, CI/CD, Automation and related technologies.
In Part 1 of this series, we looked at the key EFS metrics you should monitor, and in Part 2 we showed you how you can use tools from AWS and Linux to collect and alert on EFS metrics and logs. Monitoring EFS in isolation, however, can lead to visibility gaps as you try to understand the full context of your application’s health and performance.
In Part 1 of this series, we looked at EFS metrics from several different categories—storage, latency, I/O, throughput, and client connections. In this post, we’ll show you how you can collect those metrics—as well as EFS logs—using built-in and external tools.
Amazon Elastic File System (EFS) provides shared, persistent, and elastic storage in the AWS cloud. Like Amazon S3, EFS is a highly available managed service that scales with your storage needs, and it also enables you to mount a file system to an EC2 instance, similar to Amazon Elastic Block Store (EBS).
Data has gone from scarce, expensive, and hard to find and collect to rich and cheap, hard to process and understand with the digital age. In data science solutions, traditional software was used to capture, store, understand and analyze, but not all verticals of data science are essential for individuals and businesses. So Data visualization comes into play to make your tasks easy.
