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Using Grafana with Elasticsearch for Log Analytics

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Using Grafana with Elasticsearch for Log Analytics

Learn how to employ Grafana and Elasticsearch to achieve in-depth log analytic analysis!

· Integration Zone ·
Free Resource

Grafana is an open-source alternative to Kibana.  Grafana is best known as a visualization / dashboarding tool focused on graphing metrics from various data sources, such as InfluxDB. Even though Grafana started its life as a Kibana fork, it didn’t originally support using Elasticsearch as a Data Source.  Starting with version 2.5 Grafana added support for Elasticsearch as a Data Source — good news that we at Sematext got very excited about. Elasticsearch is typically not used to store pure metrics.  It is used more often for storing time series data like logs and other types of events (think IoT).  Grafana 2.5 was limited to the display of numerical values, but as of version 2.6 Grafana supports tabular display of textual data as well. Of course, most logs include numerical data, too, which means we can now use Grafana to render both logs and metrics from those logs stored in Logsene – perfect!

The Logsene API is compatible with Elasticsearch, which means you can use the Grafana (from v2.6 and up) with your data in Logsene simply by using Grafana’s Elasticsearch Data Source and pointing it to Logsene. You only need to do two things:

  1. Create a Data Source
  2. Add a Table Panel to a Dashboard

Watch this short video to see Grafana and Logsene together in action:

We hope you like this new, alternative way to derive insight from your data in Logsene.  Got ideas how we could make it more useful for you?  Let us know via comments, email or @sematext.

Not using Logsene yet? Check out the free 30-day trial by registering here (ping us if you’re a startup, a non-profit, or educational institution – we’ve got special pricing for you!).  There’s no commitment and no credit card required. Even better — combine Logsene with SPM to make the integration of performance metrics, logs, events and anomalies more robust for those looking for a single pane of glass.

log ,big data

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