7.6 brings log categorization to the Logs App, allowing users to see a trend view by grouping logs with similar messaging and formats.
With out-of-the-box support for common data sources and default dashboards to boot, the Elastic Stack is all about the it-just-works experience. Ship logs from Kubernetes, MySQL, and more. Index your data into Elasticsearch and visualize it all in Kibana in minutes. Skip ahead to get started with Elastic Logs. (And if you don't see the module you need, build it or leverage the community. Open source for the win!)
Keep a pulse on all of the logs flowing in from your servers, virtual machines, and containers in a centralized view built for infrastructure operations. Pin structured fields like IP or event type, and explore related logs without leaving your current screen. Dive into the Logs app in Kibana for a console-like experience across all your logs — streaming in real time.
Looking for patterns in your event data? Instead of scrolling and manually identifying similar logs, see trends instantly with the log categorization view within the UI. Analyze events that have been grouped together based on their messages and formats so you can take action quicker.
Preparing your logs for fast, centralized search is easy with Elastic — no matter the type or number of sources. Beats ship logs from your systems directly to Elasticsearch, so you can start analyzing them in one place right away. Use Filebeat modules with ingest node pipelines for common log types to pre-process documents before indexing.
And if you’re looking for even more processing muscle, Logstash can serve as a dedicated data stream processing layer by ingesting, parsing, and transforming even your most complex data.
The experience you have on one laptop is the same you’ll have on hundreds of nodes with petabytes of data. You can skip the re-architecting headaches. And don’t worry about prioritizing data types or sources (forcing you to leave valuable data on the floor). Ingest and index all that’s important to you.
Uniform data modeling with the Elastic Common Schema (ECS) means you can define a common set of document fields and centrally analyze data from diverse sources.
You shouldn't have to attend to every log message or transaction — just the ones that are important or noteworthy.
Elastic's machine learning features extend the Elastic Stack to automatically model the behavior of your Elasticsearch data and alert you on issues in real time.
We're here to help at every phase — from technical migration assistance to analyst training directly from Elastic experts.
- Apache 2
- Windows Events
- Your App
Filebeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing audit event types, accounts, and commands.
Filebeat module assumes default log locations, unmodified file formats, and supported versions of the products generating the logs. See the documentation for more details.
At telecommunications giant Sprint, sysadmins used to comb through logs, run shell scripts, and grep for what they knew. Now, they use Elastic to quickly troubleshoot performance issues, improve customer satisfaction, simplify B2B relationships, and streamline retail systems.