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Kibana vs Nagios: What are the differences?

Key Differences Between Kibana and Nagios

Kibana and Nagios are two popular tools used for monitoring and visualization in the IT industry. While they share some similarities, there are significant differences between the two. Here are the key differences between Kibana and Nagios:

  1. Data Visualization Capabilities: Kibana is primarily designed for data visualization and analysis. It provides interactive and customizable dashboards, charts, graphs, and maps to visualize data stored in Elasticsearch. On the other hand, Nagios focuses more on monitoring and alerting, and it lacks advanced data visualization features.

  2. Technological Focus: Kibana is part of the Elastic Stack, which includes Elasticsearch, Logstash, and Beats. It is built on Elasticsearch, a distributed search and analytics engine. Kibana's focus is on handling and visualizing Elasticsearch data. In contrast, Nagios is focused on monitoring various IT infrastructure components, including servers, network devices, applications, and services.

  3. Alerting Capabilities: Nagios is known for its powerful alerting capabilities. It can send notifications and alerts via various channels such as emails, SMS, and custom scripts when specific events or issues occur. Kibana, on the other hand, does not have native alerting functionality. However, it can be integrated with other tools, such as Elasticsearch Watcher, to achieve similar alerting capabilities.

  4. Ease of Use: Kibana offers a user-friendly and intuitive interface that allows users to interact with data and create visualizations without much technical knowledge. It provides a drag-and-drop interface and pre-built visualization options. Nagios, while powerful, has a steeper learning curve and requires more technical expertise to set up and configure.

  5. Scalability: Kibana is highly scalable and can handle large amounts of data. It can be easily deployed in a distributed manner to handle high volumes of data and provide performance optimizations. Nagios, while scalable to some extent, may face challenges when monitoring large-scale environments with thousands of hosts and services.

  6. Community and Ecosystem: Both Kibana and Nagios have active communities and support from a wide range of users. However, Kibana benefits from being part of the Elastic Stack, which has a vast ecosystem, extensive documentation, and a vibrant community. Nagios also has a dedicated community, but it may have a narrower focus compared to the broader ecosystem of Elastic Stack.

In summary, Kibana is a powerful data visualization tool optimized for Elasticsearch, while Nagios is a versatile monitoring tool with robust alerting capabilities. Kibana offers advanced data visualization and analytics features, focusing on Elasticsearch data, while Nagios excels in monitoring various IT infrastructure components.

Advice on Kibana and Nagios
Needs advice
on
GrafanaGrafana
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KibanaKibana

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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Replies (7)
Recommends
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GrafanaGrafana
at

For our Predictive Analytics platform, we have used both Grafana and Kibana

Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

  • Creating and organizing visualization panels
  • Templating the panels on dashboards for repetetive tasks
  • Realtime monitoring, filtering of charts based on conditions and variables
  • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
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Recommends
on
KibanaKibana

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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Bram Verdonck
Recommends
on
GrafanaGrafana
at

After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .

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Recommends
on
KibanaKibana

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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Recommends
on
KibanaKibana

Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).

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Recommends
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GrafanaGrafana

I use Grafana because it is without a doubt the best way to visualize metrics

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Povilas Brilius
PHP Web Developer at GroundIn Software · | 0 upvotes · 594.9K views
Recommends
on
KibanaKibana
at

@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.

Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.

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Decisions about Kibana and Nagios
Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 354.6K views

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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Matthias Fleschütz
Teamlead IT at NanoTemper Technologies · | 2 upvotes · 124.5K views
  • free open source
  • modern interface and architecture
  • large community
  • extendable I knew Nagios for decades but it was really outdated (by its architecture) at some point. That's why Icinga started first as a fork, not with Icinga2 it is completely built from scratch but backward-compatible with Nagios plugins. Now it has reached a state with which I am confident.
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Pros of Kibana
Pros of Nagios
  • 88
    Easy to setup
  • 64
    Free
  • 45
    Can search text
  • 21
    Has pie chart
  • 13
    X-axis is not restricted to timestamp
  • 9
    Easy queries and is a good way to view logs
  • 6
    Supports Plugins
  • 4
    Dev Tools
  • 3
    Can build dashboards
  • 3
    More "user-friendly"
  • 2
    Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
  • 2
    Easy to drill-down
  • 1
    Up and running
  • 53
    It just works
  • 28
    The standard
  • 12
    Customizable
  • 8
    The Most flexible monitoring system
  • 1
    Huge stack of free checks/plugins to choose from

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Cons of Kibana
Cons of Nagios
  • 6
    Unintuituve
  • 4
    Elasticsearch is huge
  • 3
    Hardweight UI
  • 3
    Works on top of elastic only
    Be the first to leave a con

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    What is Kibana?

    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.

    What is Nagios?

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

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    Jobs that mention Kibana and Nagios as a desired skillset
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    San Francisco, United States
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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

    ElasticsearchKibanaLogstash+4
    12
    5164
    GitHubPythonReact+42
    49
    40723
    GitHubGitPython+22
    17
    14208
    GitHubMySQLSlack+44
    109
    50664
    What are some alternatives to Kibana and Nagios?
    Datadog
    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
    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.
    Loggly
    It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.
    Graylog
    Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    See all alternatives