I. Introduction
Kibana is a visual interface tool that you can use to explore, visualize, and build a dashboard for the log data generated by Elasticsearch clusters. It was developed by a Dutch company named Elastic together with two other open-source tools namely Elasticsearch and Logstash. Elasticsearch is a database for semi-structured and document-oriented data. Logstash helps to collect, parse, and store logs. The three tools are together referred to as ELK or Elastic stack.
Kibana provides you with different ways to visualize your data including line graphs, heat maps, pie charts, and histograms to build and share dashboards from your Elasticsearch data.
TL;DR:
II. Benefits of Kibana
The following are the benefits that Kibana users enjoy:
II.1. Open Source
Kibana is an open-source tool. You can use it to analyze your logs without paying anything.
II.2. Powerful Integration with Elasticsearch
Kibana is tightly coupled with the ELK stack, and it integrates well with Elasticsearch. Thus, if your project uses Elasticsearch as the database, Kibana will give you the best performance.
II.3. Interactive Charts
Kibana provides interactive charts and reports that can help you to interactively navigate through huge volumes of log data. It allows you to zoom in and out of specific datasets, dynamically drag time windows, and drill down into reports to extract valuable insights from your data.
II.4. Mapping Support
Kibana has powerful geospatial features to help you layer in geographical information on top of your data and then visualize the results on maps. This is made possible by the Elastic Maps service that provides baseman tiles, shapefiles, and other key features that are good for geodata visualization.
II.5. Pre-built Filters and Aggregations
Kibana comes with pre-built filters and aggregations that you can use to run different types of analytics like top-N queries, histograms, and trends with just a few clicks.
II.6. Easily Accessible Dashboards
It’s easy to set up dashboards in Kibana and share them with others. You only need a web browser to view the dashboard and explore the data. It also facilitates secure sharing of dashboards and visualizations via its option that restricts information viewing to protect data leakage.
II.7. Customized Visualizations
Kibana provides its users with basic visualization tools like pie charts, histograms, and line graphs, plus the option of allowing them to design their own images. This makes it possible for Kibana users to meet their own needs and preferences. This is achieved using Vega Grammar, which is the visualization language that Kibana fully integrates with.
III. Limitations of Kibana
The following are the limitations associated with Kibana:
III.1. Can only Visualize Elasticsearch Data
Although Kibana has tight integration with Elasticsearch, it doesn’t support integration with other data sources. Thus, you can only use it to visualize Elasticsearch data. Most organizations today store/retrieve data across different data sources (SQL databases, NoSQL databases, REST APIs, and more). Thus, Kibana only working with Elasticsearch could be an issue.
III.2. No out-of-the-box Alerting System
Kibana does not have an out-of-the-box alerting system. They can only configure alerts by implementing Elastalert, use the X-Pack plugin, or use a hosted ELK stack such as Logz.io. They can configure alerts in Elasticsearch via the API or using functions called watchers.
III.3. No User Management Capabilities
Kibana does come with any user management features. Due to this, any individual with a link to your dashboard can view the data. To add user management features to Kibana, you must rely on integrations with third-party tools.
III.4. Not Good for Non-technical Business Users
Kibana has a steep learning curve, and it requires individuals with prior knowledge of the ELK stack. Thus, it can be overwhelming for non-technical business users.
IV. Best Kibana Alternatives
There are a number of data visualization tools in the market that can be used instead of Kibana. In this section, we will be discussing the top 3 best alternatives to Kibana.
IV.1. Grafana
Grafana is an open-source data visualization tool that can be used to visualize any data. It is mostly used by InfluxDB, Graphite, and also Elasticsearch.
Grafana users can also write their own plugins from scratch and use them for integrations with different data sources. It is also a good tool for time series analytics, allowing you to study, analyze, and monitor your data over a period of time. It also supports different types of visualizations that users can use to visualize their data. Grafana has customization options that users can use to slice and dice data the way they want. Grafana users can also play around with different colors, sizes of visualizations, labels, and more.
IV.1.a. Grafana Features
Below are the common Grafana features:
IV.1.a.1. Visualizations
Grafana comes with different types of visualizations, from graphs to histograms, that you can use to visualize and understand your data. Each individual visualization is known as a panel. When combined, the panels make up a dashboard.
IV.1.a.2. Display Dashboards
With Grafana, you can visualize your data using pre-built templates and custom reports. You can also share your reports with other users.
IV.1.a.3. Alerts
Grafana allows you to configure thresholds and conditions for which you should receive alerts. You can then receive notifications via PagerDuty, Slack, Webhooks, and Gmail.
IV.1.a.4. Annotations
With Grafana, you can annotate or add notes on your graphs. This makes it possible for you to mark important points on your visualizations. The notes can act as a reminder for future action, mark a special event on your visualization, or provide guidance to an onboarding team member.
IV.1.b. Advantages of Grafana
The following are the benefits of using Grafana:
IV.1.b.1. Open Source
Grafana is a completely open-source data visualization tool. It also has a vibrant community of users. Grafana users can create their own plugins or use plugins developed by other users.
IV.1.b.2. Native Support for many Integrations
Unlike Kibana, Grafana natively supports integration with a wide range of databases. If Grafana doesn’t have a native connector to your database, you can create a plugin through which Grafana can interact with your database.
IV.1.b.3. Customizable Dashboards
Grafana offers a wide range of visualizations in the form of histograms, heatmaps, and charts for visualizing data. You can also create custom visualizations using plugins to meet your specific needs.
IV.1.b.4. Good for User Management
Grafana has good user management features. You can create roles and users to allow you to control and monitor users who access your data and dashboards.
IV.1.c. Disadvantages of Grafana
The following are the limitations of Grafana:
IV.1.c.1. Not Friendly for Non-technical People
Grafana is not a user-friendly log analytics tool for non-technical people. It requires you to write plugins to connect to some data sources, or to get some visualizations, which requires coding knowledge.
IV.1.c.2. Inability to Join Data
Kibana lacks the capability to aggregate or join data from multiple sources as it is not a data store of its own. It is not capable of handling correlations across multiple data types.
IV.1.c.3. No Support for Machine Learning
Grafana does not support machine learning. This means that you cannot build machine learning models with Grafana.
IV.1.c.4. Doesn’t Support Log Search and Analysis
Grafana doesn’t offer support for querying text data, thus, you cannot use it to search through the logs.
IV.2. Knowi
Knowi is a data analytics platform that natively connects to Elasticsearch and other NoSQL, SQL, and REST-API data sources. It does this through data virtualization, a technology that allows it to connect to any data source in real-time without the need for time-consuming ETL processes. This allows users to quickly connect to their Elasticsearch indexes and run analytics on it.
Knowi is capable of working with unstructured data, a feature not provided by many BI tools currently available in the market.
Knowi was founded in 2015 and today it boasts of providing BI services to many companies in the world including Fortune 500 companies and startups.
IV.2.a. Knowi Features
The following are the most popular features offered by Knowi:
IV.2.a.1. Visualizations
Knowi supports over 30 visualizations that you can use to present your data visually. Knowi also allows its users to create custom visualizations using JavaScript to help them meet their specific needs.
IV.2.a.2. Integration with Data Sources
You can integrate Knowi with 36+ structured and unstructured data sources. It also has native support for integration with NoSQL databases including Elasticsearch. Knowi also offers a powerful REST API integration to connect to APIs and join data from different sources.
IV.2.a.3. Log Search and Analysis
Knowi supports querying on text data, thus, you can use it to search through the logs.
IV.2.a.4. Alerts
Knowi allows you to configure alerts to be notified of changes in your data or business. It sends the alerts in real-time after detecting the configured conditions, anomalies, or thresholds in data. Knowi can send alerts via Slack, Webhooks, or Email.
IV.2.a.5. Search-based Analytics
Knowi has a search-based analytics feature powered by artificial intelligence. It allows non-technical users to type questions in plain English (similar to Google Search) and get answers back in the form of tables, charts and graphs. Knowi has also introduced search-based analytics on Slack and Microsoft Teams.
IV.2.a.6. Machine Learning
You can use Knowi to perform machine learning tasks such as Classification, Regression Analysis, and Time-Series anomaly detection. It also allows users to prepare their data, create, and train machine learning models via a guided data preparation wizard.
IV.2.a.7. Multi-source Joins
Knowi allows users to join data across Elasticsearch indexes, and different data sources altogether including NoSQL, SQL, and REST-API’s. It supports INNER JOIN, LEFT OUTER JOIN, FULL OUTER JOIN, RIGHT OUTER JOIN, and LOOP JOIN.
IV.2.b. Advantages of Knowi
The following are the benefits that users enjoy when using Knowi:
IV.2.b.1. Supports many Integrations
Knowi supports integration with many data sources. It also allows users to connect to NoSQL data sources without reliance on third-party tools.
IV.2.b.2. Works well with Unstructured Data
Knowi uses data virtualization to work with any type of data, including unstructured data, without considering the underlying data structure.
IV.2.b.3. Supports Multi-Index and Multi-Source Joins
Knowi comes into play when you’ve reached the limitations of Kibana, primarily from its inability to perform multi-index joins or joins with other databases. With Knowi, you can visualize data of different data types from multiple Elasticsearch indexes or other completely different data sources in the same visualization widget or dashboard.
IV.2.b.4. Good for Non-technical Users
Knowi’s search-based analytics feature makes it a suitable tool for use even by non-technical users. They can ask questions from their data using natural language and get answers back, without needing prior knowledge of the underlying query language
IV.2.b.5. User Authorization
Knowi has out-of-the-box user management features. You can create both roles and users, and then grant them access to your dashboards. This will help you control who views your data and dashboards.
IV.2.c. Disadvantages of Knowi
The following are the disadvantages of using Knowi:
IV.2.c.1. Doesn’t Support Desktop Installations
Although Knowi supports cloud and on-premise deployments, it doesn’t support desktop installations.
IV.2.c.2. Sophisticated User Interface
Knowi has an intuitive business user interface. However, its user interface for data engineers is complex, and it may take some time for users to get used to.
IV.2.c.3. Doesn’t have the “prettiest” Out-of-the-Box Visualizations
Knowi’s out-of-the-box visualizations are not very beautiful, but it allows its users to customize them if they know CSS/JavaScript.
IV.2.c.4. Not Open Source
Knowi is a commercial tool.
IV.3. Splunk
Splunk is a software platform that makes it easy for you to collect and manage huge volumes of machine-generated data and search for specific information within it. It is used for application management, business and web analytics, security, and compliance.
Splunk is a scalable software that indexes and searches for log files generated by a system and analyzes their data for operational intelligence. Splunk captures, correlates, and indexes real-time data. It then creates visualizations, alerts, reports, and dashboards from the data. This helps businesses to recognize common data patterns, identify potential problems, and apply intelligence to business operations.
Splunk provides a browser-like interface that you can use to examine, monitor, and search through machine-generated big data. Splunk does not need a database for data storage as it uses indexes to store data.
IV.3.a. Splunk Features
The following are the core features offered by Splunk:
IV.3.a.1. Data Ingestion
Splunk can gather any form of data like XML, JSON, CSV, and unstructured machine data such as application and web logs from devices and applications like databases, websites, operating systems, servers, and more. The unstructured data can then be modeled into a data structure according to the user requirements.
IV.3.a.2. Data Indexing
Splunk indexes the collected data and maintains the associated metadata to accelerate searching and querying using different conditions.
IV.3.a.3. Data Searching
Splunk has a powerful search feature that allows you to navigate through your data. You can use searches to retrieve events from an index, calculate metrics, search for specific conditions within a particular time window, identify patterns from your data, and predict future trends.
IV.3.a.4. Alerts
You can configure Splunk to send you notifications when the search results for both real-time and historical searches meet certain conditions. You can also configure the alerts to trigger different actions like sending the alert information to specific email addresses, posting the alerts on an RSS feed, and executing a custom script.
IV.3.a.5. Dashboards
Splunk dashboard are made up of panels of modules such as fields, search boxes, charts, and more. The dashboard panels are normally connected to saved searches. They display completed search results and data from real-time searches in the form of charts, pivots, and reports.
IV.3.a.6. Data Model
You can model the indexed data in Splunk into one or more datasets based on specialized domain knowledge. This facilitates easy navigation by end users as they can analyze the business cases without having to learn the technical details of the search processing language that Splunk has used.
IV.3.b. Advantages of Splunk
The following are the benefits offered by Splunk:
IV.3.b.1. Powerful Search Features
Splunk has powerful search features to help you search for the required data within your logs easily and quickly.
IV.3.b.2. Integrated Machine Learning
Splunk has inbuilt machine learning features, thus, you can apply machine learning to the data streams without relying on third-party tools or writing code.
IV.3.b.3. Preconfigured Modules
Splunk comes with a library of preconfigured modules to help you get fast insights from your data. The modules are a collection of dashboards, metrics, and alerts that provide you with the most requested insights out of the box.
IV.3.c. Disadvantages of Splunk
The following are the disadvantages of using Splunk:
IV.3.c.1. Very Expensive
Splunk is a very expensive platform compared to other log data analysis tools, especially when managing huge volumes of data.
IV.3.c.2. Difficult to Optimize Searches
It may be difficult and impractical for Splunk users to optimize searches for improved speed.
IV.3.c.3. Steep Learning Curve
Splunk has a very steep learning curve, hence, you may spend too much time learning how to use the tool.
V. Conclusion
Kibana is a data visualization tool that can help you to visualize the log data generated by Elasticsearch clusters. Kibana, together with Elasticsearch and Logstash, form the ELK stack. Thus, Kibana has tight integration with Elasticsearch. Kibana has powerful data visualization features that can help you to visualize your log data. It also gives you the ability to create custom visualizations.
Although Kibana provides powerful log data visualization features, it comes with a number of limitations, with the greatest limitation being that it can only be used to visualize Elasticsearch data. Thus, if your log data is stored in another database rather than Elasticsearch, you will have to look for an alternative tool to Kibana for data visualization.
There are many alternatives to Kibana, but the top ones are Grafana, Knowi, and Splunk.
Grafana is an open-source tool good for data visualization. It is mostly used by InfluxDB, Graphite, and Elasticsearch. Grafana allows users to create their own plugins from scratch and use them to perform integrations with data sources. It has a powerful alerting system and allows users to annotate their graphs.
Knowi is a powerful BI tool that allows you to visualize your data and shorten the distance between your raw data and actions. It can connect to any data source, including NoSQL databases, without relying on third-party tools and join data from multiple sources. It supports different types of visualizations and gives users the ability to create custom visualizations. Knowi is a good tool for non-technical BI users because of its search-based analytics feature that allows the users to ask questions in a natural language and get responses in the form of charts and graphs.
Splunk is a BI platform that you can use to collect and manage huge volumes of machine-generated data and search for specific information within it. Splunk can help you to gather any type of data and configure alerts to get notifications when your search results meet certain conditions. It indexes your data for speedy searches within the data. Splunk also comes with inbuilt machine learning features, allowing you to apply machine learning to your raw data without the use of third-party tools.
