Application performance monitoring (APM) is much more than capturing and tracking errors and stack traces. Today’s cloud-based businesses deploy applications across various regions and even cloud providers. So, harnessing the power of metadata provided by the Elastic APM agents becomes more critical. Leveraging the metadata, including crucial information like cloud region, provider, and machine type, allows us to track costs across the application stack.

In previous blogs, we explored how Elastic Observability can help you monitor various AWS services and analyze them effectively: One of the more heavily used AWS container services is Amazon ECS (Elastic Container Service). While there is a trend toward using Fargate to simplify the setup and management of ECS clusters, many users still prefer using Amazon ECS with EC2 instances.

As we navigate our fast-paced digital era, organizations across various industries are in constant pursuit of strategies for efficient monitoring, performance tuning, and continuous improvement of their services. Elastic® and Kyndryl have come together to offer a solution for Mainframe Observability, engineered with an emphasis on organizations that are heavily reliant on mainframes, including the financial services industry (FSI), healthcare, retail, and manufacturing sectors.

IDC published a Market Perspective report discussing implementations to leverage Generative AI. The report calls out the Elastic AI Assistant, its value, and the functionality it provides. Of the various AI Assistants launched across the industry, many of them have not been made available to the broader practitioner ecosystem and therefore have not been tested. With Elastic AI Assistant, we’ve scaled out of that trend to provide working capabilities now.

As a leader in Security Analytics, we at Elastic are often asked for our recommendations for architectures for long-term data analysis. And more often than not, the concept of Limitless Data is a novel idea. Other security analytics vendors, struggling to support long-term data retention and analysis, are perpetuating a myth that organizations have no option but to deploy a slow and unwieldy data lake (or swamp) to store data for long periods of time. Let’s bust this myth.

Large Language Models (LLMs) can give notoriously inconsistent responses when asked the same question multiple times. For example, if you ask for help writing an Elasticsearch query, sometimes the generated query may be wrapped by an API call, even though we didn’t ask for it. This sometimes subtle, other times dramatic variability adds complexity when integrating generative AI into analyst workflows that expect specifically-formatted responses, like queries.

The latest versions of Elastic Observability’s most popular observability integrations now use the storage cost-efficient time series index mode for metrics by default. Kubernetes, Nginx, System, AWS, Azure, RabbitMQ, Redis, and more popular Elastic Observability integrations are time series data stream (TSDS) enabled integrations.

Elastic Search 8.9 introduces hybrid search with Reciprocal Rank Fusion (RRF) to combine vector, keyword, and semantic techniques for better results. This release also brings performance improvements in vector search and ingestion with response times that are up to 30%+ faster. Users also have more ingestion options with the new SharePoint Online connector, which includes document-level security.

As an SRE, have you ever had a situation where you were working on an application that was written with non-standard frameworks, or you wanted to get some interesting business data from an application (number of orders processed for example) but you didn’t have access to the source code?

Government and education leaders estimate that data volume at their organizations will increase by 59% over the next three years. Although having more information than you need is (arguably) better than not having it when you need it, the sheer volume of data can make it challenging for teams to pinpoint exactly what data will bring value to their mission goals.