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Grafana vs Kibana vs Logstash: What are the differences?
Introduction
Grafana, Kibana, and Logstash are all popular data visualization and analytics tools used in the field of data analysis. Each of them serves a different purpose and has its own unique features. In this Markdown code, we will discuss the key differences between Grafana, Kibana, and Logstash.
Data Sources: Grafana primarily focuses on time-series data and can connect to various databases, cloud services, and APIs to retrieve data for visualization and analysis. Kibana, on the other hand, is specifically designed for Elasticsearch and is used to analyze and visualize data stored in Elasticsearch indices. Logstash acts as a data pipeline, enabling the ingestion of data from various sources and subsequently transforming and enriching it before sending it to Elasticsearch or other outputs.
Visualization Capabilities: Grafana provides a rich set of visualization options, including interactive dashboards, graphs, heat maps, and alerting features. It offers a wide range of pre-built panels and supports custom panels. Kibana also offers a diverse set of visualizations such as bar charts, line charts, heat maps, and maps. It additionally provides features like coordinate maps and tag clouds. Logstash mainly focuses on data processing and transformation rather than visualization.
Data Transformation and Enrichment: Logstash is a powerful tool for data transformation and enrichment. It enables users to perform various operations on the incoming data, such as parsing, filtering, and adding additional fields. Grafana and Kibana, although they both support some advanced data transformations, do not have the extensive range of data processing capabilities that Logstash offers.
Built-in vs. Standalone Tools: Grafana and Kibana are both standalone tools that can be directly installed and used for data visualization and analysis. Grafana provides a user-friendly interface, allowing users to create and customize dashboards easily. Kibana integrates seamlessly with Elasticsearch, providing the ability to perform complex queries on data stored in Elasticsearch indices. Logstash, on the other hand, is primarily used as part of the ELK (Elasticsearch, Logstash, Kibana) stack, where it serves as the data processing component.
Data Collection and Ingestion: Grafana does not have built-in data collection capabilities and relies on data sources to provide the required data for visualization. Kibana relies on Elasticsearch to index and store data, which can be ingested from various sources using Logstash. Logstash acts as a central data ingestion tool, collecting and processing data from numerous sources, including log files, databases, and message queues.
Community and Ecosystem: Grafana and Kibana both have active communities and a wide range of plugins and extensions available. Grafana has a strong community with numerous plugins developed by third-party developers and offers a marketplace for extensions. Kibana also has an active open-source community with a variety of plugins and integrations available. Logstash benefits from being part of the ELK stack and has a supportive community, although it may not have the same level of plugin and extension availability as Grafana and Kibana.
In summary, Grafana is a versatile tool for time-series data visualization, while Kibana is specifically designed for analyzing data stored in Elasticsearch. Logstash, in contrast, is primarily used for data collection, transformation, and enrichment. Each tool serves a different purpose and has its own unique features, making them suitable for different use cases in the field of data analysis.
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 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 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
Pros of Logstash
- Free69
- Easy but powerful filtering18
- Scalable12
- Kibana provides machine learning based analytics to log2
- Great to meet GDPR goals1
- Well Documented1
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Cons of Grafana
- No interactive query builder1
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
Cons of Logstash
- Memory-intensive4
- Documentation difficult to use1