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Kibana vs LogDNA: What are the differences?
# Introduction
1. **Deployment**: Kibana is typically self-hosted, requiring users to set up and maintain their own infrastructure, while LogDNA is a cloud-based solution, allowing for quicker deployment and lower maintenance requirements.
2. **Integration**: Kibana integrates seamlessly with the Elasticsearch stack, providing advanced analytics capabilities, while LogDNA caters to a wider range of log sources and third-party integrations, offering more flexibility in data collection.
3. **User Interface**: Kibana offers a highly customizable and feature-rich interface, empowering users to create complex visualizations and dashboards, whereas LogDNA provides a simplified and user-friendly UI, focusing on ease of use and quick log analysis.
4. **Scalability**: Kibana is better suited for large-scale deployments and complex data processing due to its strong integration with Elasticsearch, whereas LogDNA excels in handling high log volumes and provides real-time log streaming with minimal latency.
5. **Search Capabilities**: Kibana offers advanced search features and query language support, enabling users to perform complex searches and aggregations efficiently, while LogDNA provides fast and intuitive search functionality with AI-powered insights, simplifying log analysis for users with varying levels of expertise.
6. **Security and Compliance**: Kibana offers a wide array of security features and compliance options, making it a suitable choice for enterprises with strict security requirements, whereas LogDNA provides robust encryption and access control measures, ensuring data protection and compliance with industry standards.
In Summary, Kibana and LogDNA differ in deployment, integration capabilities, user interface design, scalability, search functionality, and security features.
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 LogDNA
- Easy setup6
- Cheap4
- Extremely fast3
- Powerful filtering and alerting functionality2
- Graphing capabilities1
- Export data to S31
- Multi-cloud1
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Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3
Cons of LogDNA
- Limited visualization capabilities1
- Cannot copy & paste text from visualization1
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