Apache Zeppelin vs Kibana

Overview

Kibana

Stacks20.6K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

Apache Zeppelin

Stacks190

Followers306

Votes32

GitHub Stars6.6K

Forks2.8K

Apache Zeppelin vs Kibana: What are the differences?

Introduction: In this comparison, we will highlight the key differences between Apache Zeppelin and Kibana.

Purpose: Apache Zeppelin is a web-based notebook that enables data-driven, interactive and collaborative data analytics while Kibana is primarily used for visualization of data stored in Elasticsearch. Zeppelin offers greater interactivity and collaboration features, while Kibana is focused on data visualization within the Elasticsearch ecosystem.
Supported Data Sources: Apache Zeppelin supports a variety of different data sources including Apache Spark, Hive, JDBC, and many others, making it a versatile tool for data analysis. On the other hand, Kibana is specifically designed to work with data stored in Elasticsearch, limiting its data source options to Elasticsearch indices.
Functionality: Apache Zeppelin provides a wide range of functionalities such as data visualization, collaboration, integration with multiple interpreters, and flexible integration with various data sources. Kibana, on the other hand, is more specialized in data visualization and exploration within Elasticsearch, offering features like dashboard creation, search, and filtering capabilities.
Ease of Use: Apache Zeppelin is known for its user-friendly interface, interactive environment, and support for multiple programming languages, making it easy for users to perform data analysis tasks. Kibana, while powerful in data visualization, may have a steeper learning curve for users who are not familiar with Elasticsearch query language or data visualization concepts.
Scalability: Apache Zeppelin is designed to scale horizontally and vertically, supporting distributed computation frameworks like Apache Spark, allowing users to handle large datasets and complex analytics tasks efficiently. Kibana, on the other hand, may face limitations in scalability as it is primarily focused on visualization and exploration of data within Elasticsearch indices.
Community and Ecosystem: Apache Zeppelin has a vibrant open-source community and supports a wide range of interpreters and integrations, allowing users to customize and extend its functionality. Kibana, being part of the Elasticsearch ecosystem, benefits from a strong community of Elasticsearch users and developers, with a focus on integrating with other Elastic products like Logstash and Beats.

In Summary, Apache Zeppelin and Kibana have key differences in their purposes, supported data sources, functionality, ease of use, scalability, and community/ecosystem support, making them suitable for different use cases in data analytics and visualization.

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CLI (Node.js)

Manual

Advice on Kibana, Apache Zeppelin

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	Apache Zeppelin
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.	A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.
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	-
Statistics
GitHub Stars 20.8K	GitHub Stars 6.6K
GitHub Forks 8.5K	GitHub Forks 2.8K
Stacks 20.6K	Stacks 190
Followers 16.4K	Followers 306
Votes 262	Votes 32
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 7 In-line code execution using paragraphs 5 Cluster integration 4 Zeppelin context to exchange data between languages 4 Multi-User Capability 4 In-line graphing
Integrations
Logstash Elasticsearch Beats	Cassandra Apache Spark R Language PostgreSQL Elasticsearch HBase Hadoop Apache Flink Python

What are some alternatives to Kibana, Apache Zeppelin?

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.

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.

Jupyter

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

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.

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.

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

Related Comparisons

Apache Zeppelin vs Kibana: What are the differences?

Introduction: In this comparison, we will highlight the key differences between Apache Zeppelin and Kibana.

Purpose: Apache Zeppelin is a web-based notebook that enables data-driven, interactive and collaborative data analytics while Kibana is primarily used for visualization of data stored in Elasticsearch. Zeppelin offers greater interactivity and collaboration features, while Kibana is focused on data visualization within the Elasticsearch ecosystem.
Supported Data Sources: Apache Zeppelin supports a variety of different data sources including Apache Spark, Hive, JDBC, and many others, making it a versatile tool for data analysis. On the other hand, Kibana is specifically designed to work with data stored in Elasticsearch, limiting its data source options to Elasticsearch indices.
Functionality: Apache Zeppelin provides a wide range of functionalities such as data visualization, collaboration, integration with multiple interpreters, and flexible integration with various data sources. Kibana, on the other hand, is more specialized in data visualization and exploration within Elasticsearch, offering features like dashboard creation, search, and filtering capabilities.
Ease of Use: Apache Zeppelin is known for its user-friendly interface, interactive environment, and support for multiple programming languages, making it easy for users to perform data analysis tasks. Kibana, while powerful in data visualization, may have a steeper learning curve for users who are not familiar with Elasticsearch query language or data visualization concepts.
Scalability: Apache Zeppelin is designed to scale horizontally and vertically, supporting distributed computation frameworks like Apache Spark, allowing users to handle large datasets and complex analytics tasks efficiently. Kibana, on the other hand, may face limitations in scalability as it is primarily focused on visualization and exploration of data within Elasticsearch indices.
Community and Ecosystem: Apache Zeppelin has a vibrant open-source community and supports a wide range of interpreters and integrations, allowing users to customize and extend its functionality. Kibana, being part of the Elasticsearch ecosystem, benefits from a strong community of Elasticsearch users and developers, with a focus on integrating with other Elastic products like Logstash and Beats.

Apache Zeppelin vs Kibana

Overview