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Grafana vs Riemann: What are the differences?
Introduction Grafana and Riemann are both powerful monitoring tools used in the field of IT operations. While Grafana is an open-source analytics and visualization platform, Riemann is a real-time stream processing framework. Despite having some overlapping features, there are key differences between Grafana and Riemann that set them apart in terms of functionality and use cases.
Visualization vs Stream Processing: The main difference between Grafana and Riemann lies in their core functionalities. Grafana is primarily used for analytics and visualization, providing a user-friendly interface to create dashboards and charts based on collected metrics. On the other hand, Riemann focuses on real-time stream processing, allowing users to process and analyze large volumes of data in real-time.
Data Source Support: Grafana supports a wide range of data sources including popular monitoring systems such as Prometheus, InfluxDB, and Elasticsearch. Users can easily connect Grafana to these data sources and visualize the data in real-time. In contrast, Riemann is not tied to specific data sources and can ingest data from multiple streams, making it more flexible in terms of data input.
Alerting Capabilities: Grafana provides a robust alerting system where users can set up alerts based on predefined thresholds and rules. When the metrics cross these thresholds, Grafana sends out notifications or executes custom actions. Riemann, being a real-time stream processing framework, excels in complex event processing and allows users to define custom event processing rules, making it ideal for building complex alerting systems.
Dashboard and Visualization Flexibility: Grafana offers a highly customizable dashboard editor, allowing users to create visually appealing dashboards with drag-and-drop functionalities. Users can choose from a wide range of visualization options including graphs, charts, and tables. On the other hand, Riemann focuses more on real-time data processing and doesn't provide as many out-of-the-box visualization options as Grafana.
Community and Ecosystem: Grafana has a large and active community with extensive documentation, plugins, and integrations, making it easy to find support and extend its functionalities. Riemann, although it has a smaller community compared to Grafana, is highly extensible and can be integrated with various tools and systems to create a more comprehensive monitoring and alerting system.
Ease of Use and Learning Curve: Grafana is known for its user-friendly interface and intuitive design, making it relatively easier to learn for newcomers. It provides a graphical interface that doesn't require extensive coding knowledge to generate meaningful visualizations. Riemann, on the other hand, has a steeper learning curve due to its focus on real-time stream processing and custom event definitions. It requires more familiarity with functional programming and configuring event processing pipelines.
In summary, Grafana provides a user-friendly analytics and visualization platform with extensive data source support and customizable dashboards, while Riemann focuses on real-time stream processing and complex event processing, making it more suitable for building custom alerting systems in a functional programming environment.
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.
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 Riemann
- Sophisticated stream processing DSL5
- Clojure-based stream processing4
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Cons of Grafana
- No interactive query builder1