The US government handles massive quantities of data — via separate agencies and disconnected data systems. Having a central dashboard to track this data is absolutely essential for uncovering and sharing cybersecurity vulnerabilities before they can do harm to citizens or critical infrastructure.
Imagine being able to mimic a celebrity’s look with a screenshot. Users could use the image to quickly find clothing sold online that matches the style. But, this is not the search experience of today. Customers struggle to find what they need, and if they can’t, they'll leave. Some of them don't remember the name (keyword) of what they are looking for, but have an idea of what it looks like or the actual image.
Modern computing environments and distributed workforces have produced new challenges to traditional information security approaches. Many traditional threat detection and response strategies rely on homogeneous environments, system baselines, and consistent control implementations. These strategies have been built on traditional environment assumptions that may no longer be true in your environment with the evolution of cloud computing, remote work, and modern culture.
Elastic has an entirely new Heartbeat/Synthetics workflow superior to the current workflow. If you’re a current user of the Elastic Uptime app, read on to learn about the improved workflow you can use today and should eventually migrate toward.
This post was originally published on the Tracetest blog. Want to run trace-based tests with Elastic APM? Today is your lucky day. We're happy to announce that Tracetest now integrates with Elastic Observability APM. Check out this hands-on example of how Tracetest works with Elastic Observability APM and OpenTelemetry! Tracetest is a CNCF project aiming to provide a solution for deep integration and system testing by leveraging the rich data in distributed system traces.
When thinking about holistic end-to-end observability, it can help to start with what you already have. Many government agencies are already strategically ingesting and storing logs — a key component of observability. More than a year and a half after the release of M-21-31, US government agencies continue to work through the logging maturity models outlined in the memorandum.
With more and more applications moving to the cloud, an increasing amount of telemetry data (logs, metrics, traces) is being collected, which can help improve application performance, operational efficiencies, and business KPIs. However, analyzing this data is extremely tedious and time consuming given the tremendous amounts of data being generated. Traditional methods of alerting and simple pattern matching (visual or simple searching etc) are not sufficient for IT Operations teams and SREs.
Historically, software development and SRE have worked in silos with different cultural perspectives and priorities. The goal of DevOps is to establish common and complementary practices across software development and operations. Sadly, in some organizations true collaboration is rare and we still have a way to go to build effective DevOps partnerships.
Artificial intelligence for IT Operations (or AIOps) has been playing an expanding role in helping SREs, DevOps, and developers effectively navigate the challenges around application and infrastructure complexity, pace of change, and data volume that characterize the operations landscape.
Elastic Observability provides a full-stack observability solution, by supporting metrics, traces, and logs for applications and infrastructure. In a previous blog, I showed you how to monitor your AWS infrastructure running a three-tier application. Specifically we reviewed metrics ingest and analysis on Elastic Observability for EC2, VPC, ELB, and RDS.
