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Amazon Quicksight vs Kibana: What are the differences?
Introduction
Amazon Quicksight and Kibana are both popular data visualization tools used for analyzing data and generating insights. While they have similarities in terms of providing interactive visualizations, there are key differences that set them apart.
Data Source Compatibility: The first difference between Amazon Quicksight and Kibana lies in their data source compatibility. Quicksight is specifically designed to work seamlessly with AWS data sources, making it an ideal choice for users who heavily rely on AWS services like Redshift, S3, or RDS. On the other hand, Kibana is agnostic to data sources and can connect to various databases, Elasticsearch being its primary source. This flexibility makes Kibana suitable for users with diverse data sources.
Complex Data Analysis: Amazon Quicksight, being a managed BI service, provides a user-friendly drag-and-drop interface that allows users to create visualizations and analyze data more intuitively. It focuses on simplifying the process for users who don't have extensive technical expertise. In contrast, Kibana offers advanced analytics capabilities through its Elasticsearch engine. Users can perform complex data aggregations, create custom queries, and apply mathematically complex operations for in-depth analysis.
Real-time Data Visualization: Another major difference between the two tools is their ability to handle real-time data visualization. Kibana's integration with Elasticsearch allows users to visualize and explore real-time data streams with low latency. It excels at monitoring and analyzing live data, making it popular for use cases such as log analysis or IoT applications. Quicksight, on the other hand, doesn't provide native support for real-time data visualization, making it better suited for analyzing static or pre-aggregated data.
Embedded Analytics and Dashboarding: Quicksight offers powerful embedded analytics capabilities, allowing developers to integrate interactive charts, reports, and dashboards within their applications. This flexibility enables users to seamlessly consume data insights without leaving their preferred application environment. Kibana, although it provides visualization features, lacks the same level of embedded analytics support. It primarily focuses on providing a dedicated analytics and monitoring platform instead of embedding within other applications.
Customization and Extensibility: When it comes to customization and extensibility options, Kibana offers more flexibility compared to Amazon Quicksight. With Kibana, users have the ability to create custom visualizations using its open-source plugin framework. This means users can extend Kibana's capabilities by developing their own visualizations or leveraging the numerous community-contributed plugins. Quicksight, while offering a wide range of visualization options, has a more limited scope for customization and extensibility.
Integration with Ecosystem: Lastly, the integration with the existing technology ecosystem is an important difference to consider. As an AWS service, Amazon Quicksight seamlessly integrates with other AWS tools and services, making it an ideal choice for organizations already utilizing AWS infrastructure. On the other hand, Kibana's compatibility extends beyond AWS, allowing integration with various systems, databases, and cloud platforms, making it a versatile option for users working with non-AWS environments.
In summary, the key differences between Amazon Quicksight and Kibana lie in data source compatibility, complexity of data analysis capabilities, real-time data visualization support, embedded analytics and dashboarding features, customization and extensibility options, and integrations with existing technology ecosystems.
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 Amazon Quicksight
- Dataset versionning1
- Good integration with aws Glue ETL services1
- More features (table calculations, functions, insights)1
- Better integration with aws1
- Super cheap1
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
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Cons of Amazon Quicksight
- Very basic BI tool1
- Only works in AWS environments (not GCP, Azure)1
Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3
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