Operations | Monitoring | ITSM | DevOps | Cloud

Editor’s note: CVE-2020-0601, unsurprisingly, has created a great deal of interest and concern. There is so much going on that we could not adequately provide a full accounting in a single blog post! This post focuses on detection of the vulnerability based on network logs, specifically Zeek as well as Endpoint. If you are collecting vulnerability scan data and need to keep an eye on your inventory of systems that are at risk, then check out Anthony Perez’s blog.

Welcome to 2020! (We’re a little slow with that on the Loki team.) To kick off the year we are releasing Loki 1.3! Anyone running Loki in microservices mode will be excited by this release as it introduces the Loki Query Frontend. (If you aren’t using microservices, be patient – good things will be coming your way soon.) The query frontend sits in front of the queriers and allows sharding queries based on time.

AWS S3 Server Access logs provide detailed records for requests made to S3 buckets. They’re useful for many applications. For example, access log information can be useful in security and access audits. It can also help generate customer insights and better understand your Amazon S3 bill. Coralogix makes it easy to integrate with your S3 server access logs via a Lambda function.

eBPF has revolutionized the observability landscape in the Linux kernel. Throughout our previous blog post series, I covered the fundamental building blocks of the eBPF ecosystem, scratched the surface of XDP and showed how closely it cooperates with the eBPF infrastructure to introduce a fast-processing datapath in the networking stack. Nevertheless, eBPF is not exclusive to kernel-space tracing.

When making investments in our tech stack, we tend to have doubts about companies that don’t use their own products and services. At Elastic, we deploy the full suite of our technology across the enterprise. We do so because our technology not only works, but it makes us more efficient and flexible on so many levels. And it can do the same for you and your business, too.

The Deep Learning Toolkit (DLTK) was launched at .conf19 with the intention of helping customers leverage additional Deep Learning frameworks as part of their machine learning workflows. The app ships with four separate containers: Tensorflow 2.0 - CPU, Tensorflow 2.0 GPU, Pytorch and SpaCy. All of the containers provide a base install of Jupyter Lab & Tensorboard to help customers develop and create neural nets or custom algorithms.

Workload management is a powerful Splunk Enterprise feature that allows you to assign system resources to Splunk workloads based on business priorities. In this blog, I will describe four best practices for using workload management. If you want to refresh your knowledge about this feature or use cases that it solves, please read through our recent series of workload management blogs — part 1, part 2, and part 3.

In this blog post, we discuss using Telegraf as your core metrics collection platform with the Splunk App for Infrastructure (SAI) version 2.0, the latest version of Splunk’s infrastructure monitoring app that was recently announced at Splunk .conf19. This blog post assumes you already have some familiarity with Telegraf and Splunk. We provided steps and examples to make sense of everything along the way, and there are also links to resources for more advanced workflows and considerations.

Let’s face it—distributed streaming is an exciting technology that can be leveraged in many ways. Use cases include messaging, log aggregation, distributed tracing, and event sourcing, among others. Distributed streaming can result in significant benefits for companies that choose to use it, but, when not implemented correctly, it can initiate a frustrating technical debt cycle. How do you know if you’re properly implementing Kafka in your environment?