Kibana vs Tableau

Overview

Kibana

Stacks20.6K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

Tableau

Stacks1.3K

Followers1.4K

Votes8

Kibana vs Tableau: What are the differences?

Key Differences between Kibana and Tableau

Kibana and Tableau are both data visualization tools that are widely used in the field of analytics and business intelligence. While they share similarities in terms of their purpose, there are key differences that set them apart from each other.

Data Source Compatibility: Kibana is specifically designed to work with Elasticsearch, a distributed search and analytics engine, while Tableau is compatible with a wide range of data sources including relational databases, cloud services, and spreadsheets. This difference in data source compatibility allows Tableau to integrate with a larger variety of systems, providing more flexibility to users.
Functionality: Tableau offers a wider range of functionalities compared to Kibana. With Tableau, users have access to advanced features such as predictive analytics, statistical modeling, and powerful visualizations. On the other hand, Kibana focuses more on providing real-time analytics and log analysis capabilities for Elasticsearch data.
User Interface: Tableau is known for its user-friendly interface and drag-and-drop capabilities, allowing non-technical users to create visualizations easily. Kibana, while offering a user-friendly interface as well, requires a certain level of technical knowledge to utilize its full potential. It is more suited for users with experience in Elasticsearch and data analysis.
Customization Options: The customization options in Tableau are more extensive compared to Kibana. Tableau allows users to fully tweak the appearance and behavior of visualizations, add custom calculations, and create interactive dashboards. While Kibana does offer some customization features, they are more limited in scope.
Deployment: Kibana is typically deployed within the Elastic Stack, which includes Elasticsearch and Logstash, allowing for a seamless integration of data analytics and visualization. Tableau, on the other hand, can be deployed both on-premise and in the cloud, providing more flexibility in terms of deployment options.
Cost: Tableau is a commercial software that requires a license for its full functionality. It offers different pricing plans based on the features and deployment options. On the other hand, Kibana is open-source and free to use, providing a more cost-effective solution for those who have already adopted Elasticsearch.

In summary, the key differences between Kibana and Tableau lie in their data source compatibility, functionality, user interface, customization options, deployment options, and cost. While Kibana is focused on real-time analytics and Elasticsearch integration, Tableau offers a wider range of functionalities and data source compatibility, making it a preferred choice for users seeking comprehensive data visualization capabilities.

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Advice on Kibana, Tableau

matteo1989it

Jun 26, 2019

Reviewon

Kibana

Grafana

Elasticsearch

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

757k views757k

Comments

StackShare

Jun 25, 2019

Needs advice

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."

663k views663k

Comments

abrahamfathman

Jun 26, 2019

Reviewon

Kibana

Splunk

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.

2.29M views2.29M

Comments

Detailed Comparison

Kibana	Tableau
Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.	Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Flexible analytics and visualization platform;Real-time summary and charting of streaming data;Intuitive interface for a variety of users;Instant sharing and embedding of dashboards	Connect to data on prem or in the cloud—whether it’s big data, a SQL database, a spreadsheet, or cloud apps like Google Analytics and Salesforce. Access and combine disparate data without writing code. Power users can pivot, split, and manage metadata to optimize data sources. Analysis begins with data. Get more from yours with Tableau.; Exceptional analytics demand more than a pretty dashboard. Quickly build powerful calculations from existing data, drag and drop reference lines and forecasts, and review statistical summaries. Make your point with trend analyses, regressions, and correlations for tried and true statistical understanding. Ask new questions, spot trends, identify opportunities, and make data-driven decisions with confidence.; Answer the “where” as well as the “why.” Create interactive maps automatically. Built-in postal codes mean lightning-fast mapping for more than 50 countries worldwide. Use custom geocodes and territories for personalized regions, like sales areas. We designed Tableau maps specifically to help your data stand out.; Ditch the static slides for live stories that others can explore. Create a compelling narrative that empowers everyone you work with to ask their own questions, analyzing interactive visualizations with fresh data. Be part of a culture of data collaboration, extending the impact of your insights.
Statistics
GitHub Stars 20.8K	GitHub Stars -
GitHub Forks 8.5K	GitHub Forks -
Stacks 20.6K	Stacks 1.3K
Followers 16.4K	Followers 1.4K
Votes 262	Votes 8
Pros & Cons
Pros 88 Easy to setup 65 Free 45 Can search text 21 Has pie chart 13 X-axis is not restricted to timestamp Cons 7 Unintuituve 4 Works on top of elastic only 4 Elasticsearch is huge 3 Hardweight UI	Pros 6 Capable of visualising billions of rows 1 Intuitive and easy to learn 1 Responsive Cons 3 Very expensive for small companies
Integrations
Logstash Elasticsearch Beats	No integrations available

What are some alternatives to Kibana, Tableau?

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Sensu

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Graphite

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand

Lumigo

Lumigo is an observability platform built for developers, unifying distributed tracing with payload data, log management, and real-time metrics to help you deeply understand and troubleshoot your systems.

Related Comparisons

Kibana vs Tableau: What are the differences?

Key Differences between Kibana and Tableau

Data Source Compatibility: Kibana is specifically designed to work with Elasticsearch, a distributed search and analytics engine, while Tableau is compatible with a wide range of data sources including relational databases, cloud services, and spreadsheets. This difference in data source compatibility allows Tableau to integrate with a larger variety of systems, providing more flexibility to users.
Functionality: Tableau offers a wider range of functionalities compared to Kibana. With Tableau, users have access to advanced features such as predictive analytics, statistical modeling, and powerful visualizations. On the other hand, Kibana focuses more on providing real-time analytics and log analysis capabilities for Elasticsearch data.
User Interface: Tableau is known for its user-friendly interface and drag-and-drop capabilities, allowing non-technical users to create visualizations easily. Kibana, while offering a user-friendly interface as well, requires a certain level of technical knowledge to utilize its full potential. It is more suited for users with experience in Elasticsearch and data analysis.
Customization Options: The customization options in Tableau are more extensive compared to Kibana. Tableau allows users to fully tweak the appearance and behavior of visualizations, add custom calculations, and create interactive dashboards. While Kibana does offer some customization features, they are more limited in scope.
Deployment: Kibana is typically deployed within the Elastic Stack, which includes Elasticsearch and Logstash, allowing for a seamless integration of data analytics and visualization. Tableau, on the other hand, can be deployed both on-premise and in the cloud, providing more flexibility in terms of deployment options.
Cost: Tableau is a commercial software that requires a license for its full functionality. It offers different pricing plans based on the features and deployment options. On the other hand, Kibana is open-source and free to use, providing a more cost-effective solution for those who have already adopted Elasticsearch.

Kibana vs Tableau

Overview

Kibana vs Tableau: What are the differences?