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Kibana vs Splunk: What are the differences?
Kibana: 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; Splunk: Search, monitor, analyze and visualize machine data. Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
Kibana belongs to "Monitoring Tools" category of the tech stack, while Splunk can be primarily classified under "Log Management".
Kibana is an open source tool with 12.4K GitHub stars and 4.81K GitHub forks. Here's a link to Kibana's open source repository on GitHub.
Airbnb, DigitalOcean, and 9GAG are some of the popular companies that use Kibana, whereas Splunk is used by Starbucks, Intuit, and Razorpay. Kibana has a broader approval, being mentioned in 908 company stacks & 481 developers stacks; compared to Splunk, which is listed in 31 company stacks and 29 developer stacks.
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.
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 Splunk
- Ability to style search results into reports2
- Alert system based on custom query results2
- API for searching logs, running reports2
- Query engine supports joining, aggregation, stats, etc2
- Query any log as key-value pairs1
- Splunk language supports string, date manip, math, etc1
- Granular scheduling and time window support1
- Custom log parsing as well as automatic parsing1
- Dashboarding on any log contents1
- Rich GUI for searching live logs1
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Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3
Cons of Splunk
- Splunk query language rich so lots to learn1