As organizations migrate to Elastic from incumbent vendors, quickly onboarding log data from their current solution into Elastic is one of the first orders of business. Data onboarding often involves having to adjust ingestion architecture and implement configuration changes across data sources. We want to ensure that users trialing or migrating to Elastic can get data in quickly to start seeing the power of Elastic solutions as quickly as possible.

Our Kibana team has been hard at work implementing and executing on a new Kibana strategic vision to streamline the dashboard creation process and sand down the rough edges of creating visualizations for dashboards. We accomplished our goal and reduced the overall time it takes users to go from a blank slate to a meaningful dashboard that conveys insights about the data.

The University of California at Davis is an agriculturally focused university of more than 30,000 students. Founded in 1905, the university performs federally funded research for the U.S. Department of Defense, U.S. Department of Agriculture, and other agencies. It’s also home to an electric power substation, police and fire departments, and even an airport. All of this combined is a digital security challenge for Jeff Rowe, the university’s cybersecurity architect.

A modified version of this blog post appeared in the June 2021 issue of Signal magazine. Decisions that need to be made in an instant require answers in real time, but existing big data systems are unable to return queries quickly enough for real-time analytics. And with growing data being queried by more connected users than ever before, it’s getting increasingly challenging to maintain fast reaction times.

TL;DR: We recommend that you use the new point-in-time functionality in Elasticsearch if you can. The scroll API is no longer recommended for deep pagination (even though it still works). Most data is constantly changing. When querying an index in Elasticsearch, you are essentially searching for data at a given point of time.

Honeywell is a Fortune 100 company that produces commercial and consumer products. With roots dating to 1906, the multinational conglomerate offers chemicals, industrial manufacturing, engineering services, aerospace systems, and much more. The United States-based company employs 110,000 workers globally, and posts revenue of nearly $37 billion. Honeywell is a key player in 50 industries. It produces everything from N95 masks to automated warehouse solutions and airport security scanners.

I hate reinventing the wheel once I find a good setup. On top of that, I dislike searching for all the links I used to come up with the “ultimate setup” for different services. So, I decided to outline for myself (and for you of course) my default setup when I deploy on Elastic Cloud to set myself up for success and automate insight for the future. Most of my setup steps make monitoring accessible or automate various warnings to myself.

Security teams defending Windows environments often rely on anti-malware products as a first line of defense against malicious executables. Microsoft provides security vendors with the ability to register callbacks that will be invoked upon the creation of processes on the system. Driver developers can call APIs such as PsSetCreateProcessNotifyRoutineEx to receive such events.

It’s no secret that organisations are up against skilled, relentless and determined adversaries. Security operations teams need to continuously test their detection capabilities by carrying out adversary emulation plans that are made up of varying tactics, techniques and procedures (TTPs) and track key metrics of their coverage in order to close any existing gaps. There are many tools available for running adversary emulation plans and performing purple team exercises.

Elastic Cloud on Kubernetes (ECK) is an easy way to get the Elastic Stack up and running on top of Kubernetes. That’s because ECK automates the deployment, provisioning, management, and setup of Elasticsearch, Kibana, Beats, and more. As logging and metric data — or time series data — has a predictable lifespan, you can use hot, warm, and cold architecture to easily manage your data over time as it ages and becomes less relevant.