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Graylog vs Kibana: What are the differences?
Introduction
Graylog and Kibana are both popular log management and analysis tools used by organizations to collect, analyze, and visualize their log data. While there are some similarities between the two, there are also key differences that set them apart. In this article, we will explore the main differences between Graylog and Kibana.
Data Storage and Search: Graylog uses Elasticsearch as its backend and provides an integrated search functionality that allows users to search and analyze log data. On the other hand, Kibana is primarily a visualization tool that relies on Elasticsearch for data storage and search. This means that Graylog offers more comprehensive search capabilities out of the box compared to Kibana.
Alerting and Notifications: Graylog has built-in alerting and notification features that allow users to set up alert conditions on log events and receive alerts via various channels such as email, Slack, or PagerDuty. Kibana, on the other hand, does not have native alerting functionality and requires third-party integrations or custom development to achieve similar alerting capabilities.
User Interface and Ease of Use: Graylog has a user-friendly web interface that is specifically designed for log analysis, making it easy for users to navigate and interact with log data. Kibana, on the other hand, has a more general-purpose interface that is part of the Elastic Stack, which includes Elasticsearch and other components. This can make it more complex for users who are primarily focused on log analysis and may require additional configuration and customization.
Data Ingestion and Pipelines: Graylog provides powerful data ingestion capabilities with its flexible and scalable log processing pipelines. Users can easily enrich and transform log data using various built-in functionalities. In comparison, Kibana does not have native log processing capabilities and relies on Logstash or other data processing frameworks for similar functionalities.
Enterprise Features and Support: Graylog offers enterprise-level features such as multi-tenancy, role-based access control, and high availability clustering out of the box. It also provides commercial support options for organizations that require dedicated technical assistance. Kibana, being an open-source project, may require additional effort and custom development to achieve similar enterprise-level features and support.
Community and Ecosystem: Graylog has a vibrant and active community of users and contributors, with a dedicated marketplace for plugins and integrations. This makes it easier for users to find and extend the functionality of Graylog with community-built plugins. Kibana, being part of the Elastic Stack, also has a strong community and ecosystem, but the availability and maturity of specific integrations may vary.
In summary, Graylog offers more comprehensive search capabilities, built-in alerting and notification features, a dedicated log analysis user interface, powerful log processing pipelines, enterprise-level features and support, and a vibrant community and marketplace compared to Kibana.
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."
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
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)
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
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 .
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.
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).
@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.
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.
Pros of Graylog
- Open source19
- Powerfull13
- Well documented8
- Alerts6
- User authentification5
- Flexibel query and parsing language5
- Alerts and dashboards3
- User management3
- Easy query language and english parsing3
- Easy to install2
- Manage users and permissions1
- A large community1
- Free Version1
Pros of Kibana
- Easy to setup88
- Free65
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
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Cons of Graylog
- Does not handle frozen indices at all1
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
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