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Grafana vs Jaeger vs Kibana: What are the differences?
Grafana vs Jaeger vs Kibana: Key Differences
Grafana, Jaeger, and Kibana are three popular open-source tools used for monitoring, visualization, and analysis in the field of observability and log management. While there are similarities in their functionalities, there are also key differences that set them apart. Here are six key differences between Grafana, Jaeger, and Kibana:
Data Visualization: Grafana is primarily focused on data visualization and dashboarding. It provides a wide range of visualization options and allows users to create customized dashboards to monitor and analyze data from various sources. Jaeger, on the other hand, is specifically designed for distributed tracing, providing deep insights into the interactions between components in a distributed system. Kibana, like Grafana, offers data visualization capabilities, but it is more oriented towards log and event data analysis.
Observability vs Tracing: Grafana and Kibana are both designed for general observability, allowing users to visualize metrics, logs, and events. They provide a holistic view of various aspects of an application or system. Jaeger, on the other hand, is specifically geared towards distributed tracing, enabling users to monitor and trace requests as they propagate through a distributed system, helping to diagnose and solve performance issues.
Supported Data Sources: Grafana supports a wide range of data sources, including Prometheus, InfluxDB, Elasticsearch, and more, making it a versatile tool for integration and visualization of various data types. Jaeger primarily supports tracing data and integrates well with popular frameworks and libraries such as OpenTracing and OpenTelemetry. Kibana, being part of the Elastic Stack, is tightly integrated with Elasticsearch, making it a powerful tool for analyzing and visualizing log and event data stored in Elasticsearch.
Querying and Filtering: Grafana allows users to query and filter data from different sources using its powerful query language. It offers flexibility in data exploration and filtering options. Jaeger focuses on providing insights into request tracing and provides a query language specifically tailored for tracing data analysis. Kibana, being part of the Elastic Stack, leverages Elasticsearch's query capabilities for log and event data analysis, providing a robust querying and filtering experience.
Alerting and Notification: Grafana provides a robust alerting and notification system, allowing users to set up and customize alerts based on various conditions and send notifications via various channels. Jaeger, being primarily focused on tracing, does not provide native alerting capabilities and relies on integration with other tools for alerting. Kibana, being part of the Elastic Stack, integrates with Elasticsearch's alerting features, allowing users to set up alerts and notifications based on search queries and conditions.
Community and Ecosystem: Grafana has a large and vibrant community, with a wide range of plugins and extensions available for additional functionality. It has been widely adopted and integrated with various open-source projects and systems, making it a versatile choice for data visualization. Jaeger, being a dedicated distributed tracing tool, has a smaller but rapidly growing community, with integrations and extensions specific to tracing workflows. Kibana benefits from the extensive ecosystem of the Elastic Stack, with a rich set of plugins and integrations available for log and event analysis.
In summary, Grafana stands out as a versatile tool for data visualization, while Jaeger excels in distributed tracing and Kibana focuses on log and event analysis. The choice between these tools depends on the specific requirements and use cases in observability and log management.
Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:
- Must be able to get custom data from AS400,
- Able to display automation test results,
- System monitoring / Nginx API,
- Able to get data from 3rd parties DB.
Grafana is almost solving all the problems, except AS400 and no database to get automation test results.
You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.
We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.
this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/
I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.
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.
I learned a lot from Grafana, especially the issue of data monitoring, as it is easy to use, I learned how to create quick and simple dashboards. InfluxDB, I didn't know any other types of DBMS, I only knew about relational DBMS or not, but the difference was the scalability of both, but with influxDB, I knew how a time series DBMS works and finally, Telegraf, which is from the same company as InfluxDB, as I used the Windows Operating System, Telegraf tools was the first in the industry, in addition, it has complete documentation, facilitating its use, I learned a lot about connections, without having to make scripts to collect the data.
The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.
Pros of Grafana
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
Pros of Jaeger
- Open Source7
- Easy to install7
- Feature Rich UI6
- CNCF Project5
Pros of Kibana
- Easy to setup88
- Free65
- 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
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
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Cons of Grafana
- No interactive query builder1
Cons of Jaeger
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
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