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.
The term index is quite overloaded in the tech world. If you asked most developers what an index is, they might tell you it commonly refers to a data structure in a relational database (RDBMS) that is associated with a table, which improves the speed of data retrieval operations. But what is an Elasticsearch® index?
Navigating the complex terrain of IT systems, operational issues, and security breaches is no easy job, even for the seasoned CIO. And when tasked with the lofty goals of improving operational resilience, mitigating security risk, and enhancing customer experiences, dealing with the day-to-day operations is all the more challenging. Achieving these goals can often feel overwhelming, with no end to the journey in sight.
Elastic APM supports OpenTelemetry on multiple levels. One easy-to understand scenario, which we previously blogged about, is the direct OpenTelemetry Protocol (OTLP) support in APM Server. This means that you can connect any OpenTelemetry agent to an Elastic APM Server and the APM Server will happily take that data, ingest it into Elasticsearch®, and you can view that OpenTelemetry data in the APM app in Kibana®.
Elasticsearch® recently released time series data streams for metrics. This not only provides better metrics support in Elastic Observability, but it also helps reduce storage costs. We discussed this in a previous blog. In this blog, we dive into how to enable and use time series data streams by reviewing what a time series metrics document is and the mapping used for enabling time series. In particular, we will showcase this by using Elastic Observability’s Nginx integration.
The Elastic APM Java Agent automatically tracks many metrics, including those that are generated through Micrometer or the OpenTelemetry Metrics API. So if your application (or the libraries it includes) already exposes metrics from one of those APIs, installing the Elastic APM Java Agent is the only step required to capture them. You'll be able to visualize and configure thresholds, alerts, and anomaly detection — and anything else you want to use them for!
A comprehensive guide to support faster drug innovation and discovery in the pharmaceutical industry with generative AI/LLMs, custom models, and the Elasticsearch Relevance Engine (ESRE) Faster drug discovery leading to promising drug candidates is the main objective of the pharmaceutical industry. To support that goal, the industry has to find better ways to utilize both public and proprietary data — at speed and in a safe way.
In the highly competitive IT industry, staying ahead of the curve is crucial for success. As IT companies strive to meet the evolving needs of their customers, they are discovering that providing embedded services and comprehensive training can significantly enhance their sales efforts. The importance of having services is discussed in this Harvard Business Review article.
Are you interested to learn about the characteristics of Elasticsearch for vector search and what the design looks like? As always, design decisions come with pros and cons. This blog aims to break down how we chose to build vector search in Elasticsearch.
Maybe you came across the term “vector database” and are wondering whether it’s the new kid on the block of data retrieval systems. Maybe you are confused by conflicting claims about vector databases. The truth is, the approach used by vector databases has been around for a few years.
