Geolocation can be automatically built into the Graylog platform by using the "GeoIP Resolver" plugin with a MaxMind database. However, you can further improve your ability to extract meaningful and useful data by leveraging the functionality of pipelines and lookup tables. In fact, these powerful features allow you to do much more than the basic plugin.
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.
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.
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.
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?
Starting April 21, 2020, all requests to Elasticsearch Service on Elastic Cloud must use HTTP over TLS (HTTPS) with support for TLS 1.2. We’ve decided to make this change in the best interest of our users so we can ensure the security of data in transit and stay up to date with modern encryption, security protocols, and practices.
