Need advice about which tool to choose?Ask the StackShare community!
Apache Zeppelin vs Kibana: What are the differences?
Introduction: In this comparison, we will highlight the key differences between Apache Zeppelin and Kibana.
Purpose: Apache Zeppelin is a web-based notebook that enables data-driven, interactive and collaborative data analytics while Kibana is primarily used for visualization of data stored in Elasticsearch. Zeppelin offers greater interactivity and collaboration features, while Kibana is focused on data visualization within the Elasticsearch ecosystem.
Supported Data Sources: Apache Zeppelin supports a variety of different data sources including Apache Spark, Hive, JDBC, and many others, making it a versatile tool for data analysis. On the other hand, Kibana is specifically designed to work with data stored in Elasticsearch, limiting its data source options to Elasticsearch indices.
Functionality: Apache Zeppelin provides a wide range of functionalities such as data visualization, collaboration, integration with multiple interpreters, and flexible integration with various data sources. Kibana, on the other hand, is more specialized in data visualization and exploration within Elasticsearch, offering features like dashboard creation, search, and filtering capabilities.
Ease of Use: Apache Zeppelin is known for its user-friendly interface, interactive environment, and support for multiple programming languages, making it easy for users to perform data analysis tasks. Kibana, while powerful in data visualization, may have a steeper learning curve for users who are not familiar with Elasticsearch query language or data visualization concepts.
Scalability: Apache Zeppelin is designed to scale horizontally and vertically, supporting distributed computation frameworks like Apache Spark, allowing users to handle large datasets and complex analytics tasks efficiently. Kibana, on the other hand, may face limitations in scalability as it is primarily focused on visualization and exploration of data within Elasticsearch indices.
Community and Ecosystem: Apache Zeppelin has a vibrant open-source community and supports a wide range of interpreters and integrations, allowing users to customize and extend its functionality. Kibana, being part of the Elasticsearch ecosystem, benefits from a strong community of Elasticsearch users and developers, with a focus on integrating with other Elastic products like Logstash and Beats.
In Summary, Apache Zeppelin and Kibana have key differences in their purposes, supported data sources, functionality, ease of use, scalability, and community/ecosystem support, making them suitable for different use cases in data analytics and visualization.
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 Apache Zeppelin
- In-line code execution using paragraphs7
- Cluster integration5
- Multi-User Capability4
- In-line graphing4
- Zeppelin context to exchange data between languages4
- Privacy configuration of the end users2
- Execution progress included2
- Multi-user with kerberos2
- Allows to close browser and reopen for result later2
Sign up to add or upvote prosMake informed product decisions
Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3