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Fluentd vs Grafana: What are the differences?
Introduction
In this Markdown document, we will provide a comparison between Fluentd and Grafana, highlighting their key differences.
Deployment Purpose: Fluentd is a data collector tool designed to collect, unify, and send data from various sources to different destinations, allowing for easy log management and analysis. On the other hand, Grafana is a powerful data visualization and analytics tool used to create interactive dashboards and real-time monitoring for metrics collected from various data sources.
Data Collection and Integration: Fluentd is specifically designed for data collection and integration across different sources and systems. It supports numerous data inputs and outputs, making it versatile and capable of collecting logs, events, and metrics from various applications, servers, and devices. In contrast, while Grafana can connect to different data sources for visualization, it relies on plugins and data sources such as Prometheus, Graphite, and Elasticsearch to collect and store data.
Data Processing and Transformation: Fluentd allows users to preprocess and transform data using flexible plugins and built-in features. It supports filtering, parsing, buffering, and other data processing capabilities, enabling data enrichment before forwarding it to destinations. Grafana, on the other hand, primarily focuses on visualizing and analyzing already collected data, providing powerful visualization components and query builders without extensive data processing capabilities.
Alerting and Notification: Fluentd does not provide built-in alerting and notification features. While it can be integrated with other tools or services to enable alerting workflows, monitoring, and sending notifications, these functionalities are not native to Fluentd's core functionality. In comparison, Grafana offers powerful alerting mechanisms where users can easily set up alert rules based on specific metrics, conditions, and thresholds, allowing for proactive monitoring and alert notifications.
User Interface and Dashboarding: Fluentd does not come with a built-in user interface or dashboarding capabilities. It mainly focuses on data collection and forwarding, leaving the visualization and analysis to other tools. On the other hand, Grafana provides a user-friendly interface and advanced dashboarding capabilities. Users can create custom dashboards using interactive panels, graphs, tables, and other visualization components, allowing for comprehensive data exploration and analysis.
Community and Plugin Ecosystem: Fluentd has a thriving open-source community with a wide range of plugins available. These plugins provide additional features and functionalities to extend Fluentd's capabilities, making it highly flexible and customizable to specific use cases. Grafana also has an active community supporting the development of plugins and extensions but with a primary focus on visualizations and data source integrations, enhancing its dashboarding and monitoring capabilities.
In summary, Fluentd excels in data collection and integration, with extensive support for various sources and destinations, while Grafana focuses on data visualization and analytics, providing powerful dashboarding and alerting features.
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.
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.
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/
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 Fluentd
- Open-source11
- Easy9
- Great for Kubernetes node container log forwarding9
- Lightweight9
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
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Cons of Fluentd
Cons of Grafana
- No interactive query builder1