Your log analysis solution works through millions of lines of logs, which makes implementing a machine learning solution essential. Organizations are turning to machine learning log alerts as a replacement or enhancement of their traditional threshold alerts. As service uptime becomes a key differentiator, threshold alerts are only as good as your ability to foresee an issue.
For enterprise applications and startups to scale, they need to manage large volumes of data in real-time. Customers must have the ability to search for any product or service from your database within seconds. When you manage a relational database, data is spread across multiple tables. So, customers may experience lag during search and data retrieval. However, this is different with Elasticsearch and other NoSQL databases.
The aggregations framework has been part of Elasticsearch since version 1.0, and through the years it has seen optimizations, fixes, and even a few overhauls. Since the Elasticsearch 7.0 release, quite a few new aggregations have been added to Elasticsearch like the rare_terms, top_metrics or auto_date_histogram aggregation. In this blog post we will explore a few of those and take a closer look at what they can do for you.
The ability to monitor your Elastic Cloud deployment is critical for helping ensure its health, performance, and security. Our Elastic Observability solution provides unified visibility across your entire ecosystem — including your Elastic Cloud deployments. Elastic Observability allows you to bring your logs, metrics, and APM traces together at scale in a single stack so you can monitor and react to events happening anywhere in your environment.
You may have noticed how on sites like Google you get suggestions as you type. With every letter you add, the suggestions are improved, predicting the query that you want to search for. Achieving Elasticsearch autocomplete functionality is facilitated by the search_as_you_type field datatype. This datatype makes what was previously a very challenging effort remarkably easy.
More organizations than ever before have shifted to a hybrid work culture to reduce the impact of COVID-19. This unprecedented change has not only given rise to new security challenges, but has also considerably increased the surface area available for an attack. A blend of personal and corporate endpoints in use, geographical spread of resources, and a sharp spike in the overall number of security threats have further complicated the already labor-intensive security landscape.
Last month we launched the Elastic Contributor Program to recognize and reward the hard work of our awesome contributors, encourage knowledge sharing within the Elastic community, and build friendly competition around contributions. But how do you start contributing? In this blog post, we’ll walk through how to log in to the Elastic Contributor Program portal and set up your profile so you can begin submitting your own contributions and validating others’ contributions!
Elastic 7.6 introduced the inference processor for performing inference on documents as they are ingested through an ingest pipeline. Ingest pipelines are incredibly powerful and flexible but they are designed to work at ingest. So what happens if your data is already ingested? Introducing the new Elasticsearch inference pipeline aggregation, which lets you apply new inference models on data that's already been indexed.
