The OpsRamp Monitor is back, capturing the latest buzz around what’s trending in the world of ITOps and related technology. It’s been a busy month!
Many of you are familiar with Splunk’s Machine Learning Toolkit (MLTK) and the Deep Learning Toolkit (DLTK) for Splunk and have started working with either one to address security, operations, DevOps or business use cases. A frequently asked question that I often hear about MLTK is how to organize the data flow in Splunk Enterprise or Splunk Cloud.
For the newest instalment in our series of interviews asking leading technology specialists about their achievements in their field, we’ve welcomed Mark Kerzner, software developer and thought leader in cybersecurity training who is also the VP at training solutions company, Elephant Scale. His company has taught tens of thousands of students at dozens of leading companies. Elephant Scale started by publishing a book called ‘Hadoop Illuminated‘.
The Java Persistence API (JPA) is used in most Java applications to interact with a relational database. One of its most popular implementations is the Hibernate ORM, because it uses object-relational mapping to abstract database interactions and makes implementing simple CRUD operations very simple. But this abstraction also has its downsides. Hibernate uses a lot of internal optimizations and hides all database interactions behind its API.
18 months later, organizations around the globe are slowly but surely starting to welcome their employees back to the office – or at least thinking about it. But this is not a “back to normal” kind of thing. Most will recognize that something’s different this time around. Firstly, there is no clear one-size-fits-all return-to-office strategy.
Containers are lightweight, portable, easily scalable, and enable you to run multiple workloads on the same host efficiently, particularly when using an orchestration platform like Kubernetes or Amazon ECS. But containers also introduce monitoring challenges. Containerized environments may comprise vast webs of distributed endpoints and dependencies that rely on complex network communication.
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).
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.
