Operations | Monitoring | ITSM | DevOps | Cloud

When ingesting data into Elasticsearch, it is often beneficial to enrich documents with additional information that can later be used for searching or viewing the data. Enrichment is the process of merging data from an authoritative source into documents as they are ingested into Elasticsearch. For example, enrichment can be done with the GeoIP Processor which processes documents that contain IP addresses and adds information about the geographical location associated with each IP address.

George Lucas once said, “Education is the single most important job of the human race.” When considering the requirement of education in the mastering of any role or skill, there is no debate to the truth behind his words. Education is the cornerstone on which the future is built, which is why Elastic is launching the Elastic for Students and Educators program.

What I love about our free and open Elastic SIEM is how easy it is to add new data sources. I’ve learned how to do this firsthand, and thought it’d be helpful to share my experience getting started. Last October, I joined Elastic Security when Elastic and Endgame combined forces. Working with our awesome security community, I’ve had the opportunity to add new data sources for our users to complement our growing catalog of integrations.

For many companies, Elastic included, wikis developed with Confluence are a critical source of content, procedures, policies, and plenty of other important info, spanning teams across the entire organization. But sometimes finding a particular nugget of information can be tricky, especially when you’re not exactly sure where that information was located. Was it in the wiki? In a Word doc? In Salesforce? A GitHub issue? Somewhere else?

Software services are at the heart of modern business in the digital age. Just look at the apps on your smartphone. Shopping, banking, streaming, gaming, reading, messaging, ridesharing, scheduling, searching — you name it. Society runs on software services. The industry has exploded to meet demands, and people have many choices on where to spend their money and attention. Businesses must compete to attract and retain customers who can switch services with the swipe of a thumb.

As Elasticsearch users are pushing the limits of how much data they can store on an Elasticsearch node, they sometimes run out of heap memory before running out of disk space. This is a frustrating problem for these users, as fitting as much data per node as possible is often important to reduce costs. But why does Elasticsearch need heap memory to store data? Why doesn't it only need disk space?

Let’s be honest. No one wakes up in the morning thinking of reasons to contact customer support. It’s tedious, onerous, and can eat into your evening Netflix time. Thankfully, most brands realize that customer experiences drive brand loyalty and repeat purchases.

Binary classification aims to separate elements of a given dataset into two groups on the basis of some learned classification rule. It has extensive applications from security analytics, fraud detection, malware identification, and much more. Being a supervised machine learning method, binary classification relies on the presence of labeled training data that can be used as examples from which a model can learn what separates the classes.

If you want to skip ahead to see the MITRE ATT&CK eval round 2 results visualized in an easy-to-configure Kibana dashboard, check it out here.

To achieve unified observability, we need to gather all of the logs, metrics, and application traces from an environment. Storing them in a single datastore drastically increases our visibility, allowing us to monitor other distributed environments as well. In this blog, we will walk through one way to set up observability of your Kubernetes environment using the Elastic Stack — giving your team insight into the metrics and performance of your deployment.