Widget to Embed any Web Page in Collibra Dashboard
The beauty and the beast. Our beast is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases.
The beauty lets you visualize the data indexed in Elasticsearch with a variety of cool graphical tools, like Tile maps, bar charts, pie charts, timelines and more. If you loooove dashboards, This is a couple you should know!
Uh oh, we have a problem Kibana allows you to create visualizations using its administrator page, which is a designated UI. But what if you want to create those visualizations programmatically? What if you want to dynamically update those visualizations and dashboards so they will fit to your apps business logic? Free spirits want to create visualizations programmatically!
What should we do then? So we had to develop the ability to create and update visualizations and dashboards dynamically. One approach is to create all the optional visualizations in Kibana at the beginning, and when our fearless user will launch the app, all the visualizations will be ready for use.
It could be thousands of options. No bueno. This plugin will help us to create and update the visualizations dynamically from our app. Kibana-API The current version of our plugin has 3 features: Set a visualization — Create, edit, replace or add a visualization in embedded dashboards.
Index pattern functions — Create and set index-patterns from your app. Index patterns identify the Elasticsearch indices that are explorable via your Kibana visualizations and dashboards. How it works So how this magic happens? Surprisingly, we barely needed pixie dust or unicorns for that. Communicate the plugin We did use an equally great thing — post messages.
Since Kibana is running inside an iframe, isolated from the rest of the app, we needed to use post messages for app — plugin communication. Index the visualizations in Elasticsearch The next step is create the visualization.
Using this way you must pass all the relevant visualization structure parameters. A blogpost is not complete without a gif, so we made one as well The following gif shows a Kibana dashboard embedded inside a mock app. We can see that the visualizations in the dashboard change dynamically by the user. Every time the user clicks a button, a post messages is sent from the app to the plugin, visualizations are created and indexed into the.
Kibana Hacks: 5 Tips and Tricks
Let's see how creating dashboards starting from data in elasticsearch using kibana How to create dashboards with ElasticSearch and Kibana Wednesday, June 03, In my previous articles here the first and the second one , I have shown the use of ElasticSearch as a full-text search engine in e-commerce, the set and use of some advanced configurations and the creation of a products index which contains all saved products. The next step is to create a dashboard that can display the products, and the performed research carried out on them to elaborate more or less advanced statistics.
Rather than developing a custom solution with certain time-consuming efforts, we decided to use Kibana. It deals with a front end application that is part of the ElasticSearch stack that allows us to view data and searches for all indexed data and track the load of queries. Kibana can also be used for monitoring, managing, and securing the same stack. Here we can find the installers for all platforms, and download the one suitable for our needs.
The page we get is the following: Kibana interface is divided into several sections. In the open source version there are: Discover data interactive exploration , Visualize data analysis in graphs, tables, tags , Dashboards complex data views , Canvas documents creation , Maps georeferenced data analysis , Dev Tools tools to process and analyze queries and Management index and cluster management.
We can also install the X-Pack plugin to use the Graph and Monitoring sections. In our case, we type products, so as to create our Kibana index. Once created the index, in the Discover section, it is possible to filter the data by date or by one or more fields: Using the search bar, we can query between products using the KQL language Kibana Query language , which allows you to easily query using the autocomplete.
For example, we could type: category. You can select some field and add them to Selected fields, in order to have a custom result view. Once added the index and verified the correctness of the queries, we can create new data views. The visualizations consist of various types of graphs bar, cakes , tables, indicators, metrics, and tag clouds. Of course, they support data aggregations. In the Visualize section, we can create a new data visualization by using graphs.
For statistical purposes, we create some graphs of products grouped by category, brand, using simple vertical bar graphs. We get results similar to: You can also add filters to this view. We click then on the Save button to save our view. Another useful visualization is that of products by price range.
In this case, we define buckets in the price field and use them for a pie chart.
Surprisingly, we barely needed pixie dust or unicorns for that. Communicate the plugin We did use an equally great thing — post messages. Since Kibana is running inside an iframe, isolated from the rest of the app, we needed to use post messages for app — plugin communication.
Index the visualizations in Elasticsearch The next step is create the visualization. Using this way you must pass all the relevant visualization structure parameters. A blogpost is not complete without a gif, so we made one as well The following gif shows a Kibana dashboard embedded inside a mock app. We can see that the visualizations in the dashboard change dynamically by the user. Every time the user clicks a button, a post messages is sent from the app to the plugin, visualizations are created and indexed into the.
Kibana does not offer any user management features. This means whoever has the link to the Kibana dashboard can see the data. This might not always be a good way of working. But the saving grace is that we can still implement user and role restrictions in Kibana with the help of 3rd party integrations. This means there is good online community support available for both if needed.
Grafana vs Kibana vs Knowi: Battle Royale 2020
If the popularity of both projects has to be measured, then it is worth noting that on Github, Kibana has more commits than Grafana. Knowi, however, is not an open-source tool currently. Still, they do have their own forum on their website where you can seek help from other users. But what they lack in community may be made up for in customer support. As a relatively young startup looking to make a name for themselves, they have a dynamic, responsive, and strongly motivated team of solution engineers looking to prove that their product is the best on the market.
Machine Learning Currently, machine learning is making inroads in all possible spaces globally where there is data. If you have loads of big data you would not like to miss the opportunity to apply machine learning to get hidden insights and patterns from data.
Imagine, if you can predict any potential risk from logs data well in advance and take precautionary measures. This can take the log monitoring process to a whole new level.
Grafana does not currently support machine learning although the ELK stack does support some machine learning capabilities. Knowi has built-in integration with many machine learning algorithms like Classification, Regression, and Time-Series Anomaly Detection type Machine Learning use cases, with clustering and deep learning coming soon. This enables Knowi users to do predictive analysis using machine learning and set up a triggering alert based on the prediction in the monitoring workflow.
Purpose Till now, we have done a comparison of Grafana vs Kibana vs Knowi with respect to the features they offer. But we need to understand one thing that the underlying purpose of using the tools is slightly different.
Embed splunk Dashboard into external Website
Kibana is useful to convert log data from the ELK stack into visualizations and it also supports querying logs. But it does not support text querying. Knowi is an end-to-end data analytics platform that can sit on top of and across multiple databases, structured or unstructured. Knowi literally supports all the features and integrations that either Kibana or Grafana offers plus much more. That said, both Kibana and Grafana can be deployed for free or at a very low cost, while Knowi is an enterprise solution that does have licensing fees.
But with those added expenses come a lot of support, which can be nice when getting a complex data analytics solution off the ground. Conclusion People who work in the operation support team will tell you how important it is to monitor the health of applications, servers, and infrastructure.
They continuously need to monitor these systems since an outage can result in a huge loss to the business. Grafana or Kibana or Knowi can be a good choice for your monitoring solution.
How to Customize Kibana Dashboards
And actually, Kibana comes with a number of different panels such as table, histogram, terms, text, map etc. Index specific properties can be configured like the default index and the index pattern. A Kibana dashboard displays a set of visualizations in groups that can arrange freely. You can save a dashboard to share or reload at a later time. Kibana has support for creating dashboards dynamically via templates and advanced scripts.
This allows users to create a based dashboard, and then influence it with parameters. Templates and scripts must be stored on disk and they must be created by editing or creating a schema. Starting with Kibana Dashboards To get started with customizing Kibana dashboards at all, you need at least one saved visualization to use a dashboard. The first time you click the Dashboard tab, Kibana displays an empty dashboard.
You can build your dashboard by adding visualizations.