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Kibana vs Thanos: What are the differences?
- Key difference between Kibana and Thanos: Kibana is a data visualization platform that provides a user-friendly interface to explore, analyze, and visualize data stored in ElasticSearch. On the other hand, Thanos is a scalable, highly available, and durable Prometheus platform that provides long-term storage and global query capabilities.
- Integration capabilities: Kibana integrates seamlessly with the Elastic Stack, allowing users to leverage the full power of ElasticSearch for data storage and retrieval. Thanos, on the other hand, integrates with Prometheus, extending its capabilities with long-term storage and cross-cluster query support.
- Storage architecture: Kibana relies on ElasticSearch for data storage, utilizing its distributed and scalable architecture. In contrast, Thanos introduces a global-scale storage architecture by leveraging object storage systems like Amazon S3 or Google Cloud Storage, which enables efficient querying across multiple Prometheus instances.
- Data retention: Kibana does not provide specialized features for long-term data retention and relies on the capabilities of ElasticSearch for data persistence. However, Thanos is specifically designed for long-term data retention, allowing users to store and query data over extended periods efficiently.
- Horizontal scalability: Kibana achieves horizontal scalability by deploying multiple instances and configuring load balancers. In contrast, Thanos scales horizontally by distributing query workload across multiple Prometheus instances and coordinating data retrieval from the backend storage.
- Federation support: Thanos introduces the concept of federation, enabling efficient querying across multiple Prometheus servers by merging the results of individual Prometheus queries. Kibana, on the other hand, does not have built-in federation support.
In summary, Kibana is a data visualization platform integrated with ElasticSearch, while Thanos is a scalable Prometheus platform with long-term storage and global query capabilities. Their key differences lie in integration capabilities, storage architecture, data retention, horizontal scalability, and federation support.
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 Kibana
- Easy to setup88
- Free64
- 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
- Can build dashboards3
- More "user-friendly"3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
Pros of Thanos
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Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3