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Amazon CloudWatch vs Kibana: What are the differences?
Amazon CloudWatch is a comprehensive monitoring and logging service provided by AWS, while Kibana is a visualization tool that works with Elasticsearch for analyzing and visualizing log data. Let's explore the key differences between them.
Data Source: Amazon CloudWatch primarily collects and monitors data from AWS resources, such as Amazon EC2 instances, RDS databases, and Lambda functions, providing insights into the overall health and performance of these services. On the other hand, Kibana is typically used in conjunction with Elasticsearch and is designed to analyze and visualize data stored in Elasticsearch clusters, which can include logs, metrics, and other types of data.
Data Analysis Capabilities: Amazon CloudWatch offers basic monitoring and alerting capabilities, allowing users to set alarms based on predefined metrics and thresholds. While it provides some built-in analysis features like aggregations and statistics, its focus is mainly on monitoring rather than in-depth data analysis. Kibana, on the other hand, provides a wide range of advanced data visualization and exploration features. Users can create interactive dashboards, perform ad-hoc queries, apply filters, and use various visualization plugins to gain more insights from their data.
Supported Ecosystems: Amazon CloudWatch is tightly integrated with the AWS ecosystem, making it the go-to choice for monitoring and managing AWS resources. It seamlessly integrates with other AWS services, allowing users to access CloudWatch metrics, logs, and events from a centralized console. In contrast, Kibana is part of the Elastic Stack, a set of open-source tools including Elasticsearch, Logstash, and Beats. It is designed to work with Elasticsearch, making it a preferred choice for analyzing and visualizing data stored in Elasticsearch clusters, regardless of the source of the data.
Storage and Retention: The way data is stored and retained also differs between Amazon CloudWatch and Kibana. Amazon CloudWatch stores data in a highly compressed and efficient manner, enabling users to view historical data for up to 15 months. It automatically manages data retention and aggregates metrics on different time scales to optimize storage. In contrast, Kibana relies on Elasticsearch for data storage, which allows users to scale horizontally and store large volumes of data. The retention period and storage options in Kibana depend on the Elasticsearch cluster configuration and can be customized according to specific needs.
Visualization Options: When it comes to data visualization, the options offered by Amazon CloudWatch and Kibana vary. Amazon CloudWatch provides a set of predefined graphs and visualizations to display metrics and log data. Users can choose from line charts, stacked area graphs, and other basic visualizations. In comparison, Kibana offers a wide range of customizable visualization types, including bar charts, pie charts, heat maps, and scatter plots. It also provides advanced features like time series analysis, time bucketing, and filtering to create rich and interactive visualizations.
Extensibility and Customization: Lastly, the degree of extensibility and customization available in Amazon CloudWatch and Kibana differs. Amazon CloudWatch offers limited extensibility options, allowing users to create custom metrics and dashboards but within the confines of its predefined capabilities. On the other hand, Kibana provides a highly extensible platform with numerous plugins and integrations available. Users can extend its capabilities by creating custom visualizations, building custom data pipelines with Logstash, and integrating with various third-party tools.
In summary, Amazon CloudWatch is primarily focused on monitoring and managing AWS resources, providing basic analysis and alerting capabilities, while Kibana is a powerful data visualization and analysis tool designed to work with Elasticsearch, offering advanced visualization options, extensibility, and customizability.
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 CloudWatch
- Monitor aws resources76
- Zero setup46
- Detailed Monitoring30
- Backed by Amazon23
- Auto Scaling groups19
- SNS and autoscaling integrations11
- Burstable instances metrics (t2 cpu credit balance)5
- HIPAA/PCI/SOC Compliance-friendly3
- Native tool for AWS so understand AWS out of the box1
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 CloudWatch
- Poor Search Capabilities2
Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3
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