We're excited to announce that autoscaling is now available on Elastic Cloud. In our initial release, autoscaling monitors the storage utilization of your Elasticsearch data nodes and the available memory capacity for your machine learning jobs.
It is incredibly useful to be able to identify the most unusual data in your Elasticsearch indices. However, it can be incredibly difficult to manually find unusual content if you are collecting large volumes of data. Fortunately, Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data. And with machine learning, the larger the dataset, the better.
Back in our 7.10 release of the Elastic Stack, we announced the beta of our Ruby and Python clients for Elastic Enterprise Search. Now, with 7.11, both the Ruby and Python clients are generally available. We’ve also begun work on a PHP client. All client source code for both enterprise-search-ruby and enterprise-search-python is available on GitHub. Documentation on how to get started with each client is available on elastic.co.
When it comes to your SIEM, your data is only as useful as your ability to ingest and analyze it. To solve complex security problems, your team ideally needs the ability to comprehensively monitor events within your environment with contextual insights from high-volume data sources.
Graphical processing units, or GPUs, aren’t just for PC gaming. Today, GPUs are used to train neural networks, simulate computational fluid dynamics, mine Bitcoin, and process workloads in data centers. And they are at the heart of most high-performance computing systems, making the monitoring of GPU performance in today's data centers just as important as monitoring CPU performance.
The cold tier of searchable snapshots, previously beta in Elasticsearch 7.10, is now generally available in Elasticsearch 7.11. This new data tier reduces your cluster storage by up to 50% over the warm tier while maintaining the same level of reliability and redundancy as your hot and warm tiers.
Security information and event management (SIEM) systems are centralized logging platforms that enable security teams to analyze event data in real time for early detection of targeted cyber attacks and data breaches. A SIEM is used as a tool to collect, store, investigate, and report on log data for threat detection, incident response, forensics, and regulatory compliance.
With an increasing number of organizations migrating their applications and workloads to containers, the ability to monitor and track container health and usage is more critical than ever. Many teams are already using the Metricbeat docker module to collect Docker container monitoring data so it can be stored and analyzed in Elasticsearch for further analysis. But what happens when users are using Amazon Elastic Container Service (Amazon ECS)? Can Metricbeat still be used to monitor Amazon ECS? Yes!
We are excited to announce the new Elastic Cloud usage analysis page. You can now explore and analyze your Elastic Cloud usage to better understand how the resources you consume contribute to your monthly bill. Your Elastic Cloud monthly bill consists of usage fees for the resources you used, including: Understanding your resource utilization allows you to make smarter decisions about your Elastic deployments as well as identify areas where you may be able to save costs.
Istio is an open source service mesh that can be used by developers and operators to successfully control, secure, and connect services together in the world of distributed microservices. While Istio is a powerful tool for teams, it's also important for administrators to have full visibility into its health. In this blog post, we'll take a look at monitoring Istio and its microservices with Elastic Observability. As the Istio docs mention.
