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Kibana vs Power BI: What are the differences?
Introduction
Kibana and Power BI are both popular tools used for data visualization and analysis. While they serve similar purposes, there are several key differences between the two.
Storage and Data Sources: Kibana primarily works with Elasticsearch for storage and data retrieval, while Power BI supports a wide range of data sources including databases, Excel files, cloud services, etc. This difference in data sources gives Power BI an advantage when it comes to integrating data from various systems.
Data Modeling and Transformations: Power BI offers a comprehensive data modeling capability, allowing users to define relationships and perform complex transformations using Power Query. Kibana, on the other hand, focuses more on visualizing data and does not offer extensive data modeling or transformation features. This makes Power BI a better choice for users who need advanced data preparation capabilities.
Embedded Analytics: Power BI enables easy embedding of reports and dashboards into other applications or websites using its Embedded API. Kibana also provides embedding options but requires additional configuration and knowledge of programming languages like JavaScript. Power BI's straightforward embedding process makes it a preferred choice when it comes to building integrated solutions.
Advanced Analytics: Power BI offers built-in advanced analytics functionalities such as forecasting, clustering, and sentiment analysis through its integration with Azure Machine Learning. While Kibana supports some advanced analytics features, it requires the use of additional plugins and extensions. Power BI's native advanced analytics capabilities make it a more robust tool for data analysis.
Collaboration and Sharing: Power BI provides seamless collaboration and sharing options, allowing users to collaborate on reports and dashboards in real-time, create alerts, and share content with specific individuals or groups. Kibana, although it supports basic sharing features, falls behind in terms of collaborative functionalities. Power BI's collaboration features make it more suitable for team collaboration and sharing.
Pricing Model: Kibana is part of the Elastic Stack, which is available as open-source software, making it free to use. However, certain features and functionalities may require a license. Power BI, on the other hand, offers both free and paid versions, with the free version having limitations on data storage, refresh rates, and collaboration features. Power BI's pricing model provides more flexibility, whether users require a free solution or need to access premium features.
**In Summary, Kibana primarily works with Elasticsearch, lacks advanced data modeling capabilities, requires more effort for embedding and collaboration, and may require additional licensing for certain features. Power BI supports versatile data sources, provides extensive data modeling capabilities, offers straightforward embedding and collaboration options, includes advanced analytics features, and has a more flexible pricing model.
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.
Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.
And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.
Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.
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 Power BI
- Cross-filtering17
- Powerful Calculation Engine2
- Access from anywhere2
- Intuitive and complete internal ETL2
- Database visualisation2
- Azure Based Service1
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Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3
Cons of Power BI
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