Sensu vs Kibana | What are the differences?

Overview

Sensu

Stacks204

Followers251

Votes56

GitHub Stars2.9K

Forks386

Kibana

Stacks20.7K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

Kibana vs Sensu: What are the differences?

Developers describe Kibana as "Explore & Visualize Your Data". Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. On the other hand, Sensu is detailed as "Open source monitoring framework". A monitoring framework that aims to be simple, malleable, and scalable. Essentially, Sensu takes the results of “check” scripts run across many systems, and if certain conditions are met; passes their information to one or more “handlers”. Checks are used, for example, to determine if a service like Apache is up or down.

Kibana and Sensu can be categorized as "Monitoring" tools.

Some of the features offered by Kibana are:

Flexible analytics and visualization platform
Real-time summary and charting of streaming data
Intuitive interface for a variety of users

On the other hand, Sensu provides the following key features:

Execute service checks
Send notifications
Collect metrics

"Easy to setup" is the top reason why over 76 developers like Kibana, while over 11 developers mention "Support for almost anything" as the leading cause for choosing Sensu.

Kibana and Sensu are both open source tools. It seems that Kibana with 12.4K GitHub stars and 4.8K forks on GitHub has more adoption than Sensu with 2.96K GitHub stars and 389 GitHub forks.

According to the StackShare community, Kibana has a broader approval, being mentioned in 907 company stacks & 479 developers stacks; compared to Sensu, which is listed in 33 company stacks and 7 developer stacks.

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Advice on Sensu, Kibana

Leonardo Henrique da

Pleno QA Enginneer at SolarMarket

Dec 8, 2020

Decided

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

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Comments

matteo1989it

Jun 26, 2019

Reviewon

Kibana

Grafana

Elasticsearch

I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics

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Comments

StackShare

Jun 25, 2019

Needs advice

From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."

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Comments

Detailed Comparison

Sensu	Kibana
Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.	Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.
Health checks & custom metrics; alerts & incident management; real-time inventory; auto-remediation & custom workflows; container monitoring; Kubernetes monitoring; telemetry & service health checking; multi-cloud monitoring	Flexible analytics and visualization platform;Real-time summary and charting of streaming data;Intuitive interface for a variety of users;Instant sharing and embedding of dashboards
Statistics
GitHub Stars 2.9K	GitHub Stars 20.8K
GitHub Forks 386	GitHub Forks 8.5K
Stacks 204	Stacks 20.7K
Followers 251	Followers 16.4K
Votes 56	Votes 262
Pros & Cons
Pros 13 Support for almost anything 11 Easy setup 9 Message routing 7 Devs can code their own checks 5 Ease of use Cons 1 Written in Go 1 Plugins	Pros 88 Easy to setup 65 Free 45 Can search text 21 Has pie chart 13 X-axis is not restricted to timestamp Cons 7 Unintuituve 4 Works on top of elastic only 4 Elasticsearch is huge 3 Hardweight UI
Integrations
ServiceNow.com Prometheus InfluxDB Grafana PagerDuty	Logstash Elasticsearch Beats

What are some alternatives to Sensu, Kibana?

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Graphite

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand

Lumigo

Lumigo is an observability platform built for developers, unifying distributed tracing with payload data, log management, and real-time metrics to help you deeply understand and troubleshoot your systems.

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

Jaeger

Jaeger, a Distributed Tracing System

Telegraf

It is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics.