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  5. Elasticsearch vs Kibana

Elasticsearch vs Kibana

OverviewDecisionsComparisonAlternatives

Overview

Elasticsearch
Elasticsearch
Stacks35.5K
Followers27.1K
Votes1.6K
Kibana
Kibana
Stacks20.6K
Followers16.4K
Votes262
GitHub Stars20.8K
Forks8.5K

Elasticsearch vs Kibana: What are the differences?

Elasticsearch and Kibana are commonly used for managing and visualizing data. Elasticsearch is a distributed, scalable search and analytics engine, while Kibana is a data visualization and exploration platform. Together, they offer powerful capabilities for searching, analyzing, and visualizing data in real-time. Let's explore the key differences between them:

  1. Functionality: Elasticsearch serves as a search engine and data store, providing full-text search, document indexing, and advanced analytics capabilities. It excels in fast data retrieval and indexing large volumes of structured or unstructured data. Kibana, on the other hand, specializes in data visualization, offering intuitive dashboards, charts, and graphs to explore and present data in a visually appealing and interactive manner.

  2. Querying and Aggregation: Elasticsearch supports full-text search, filtering, aggregations, and complex queries using the Elasticsearch Query DSL. It enables users to perform advanced analytics, slice and dice data, and generate meaningful insights. Kibana leverages Elasticsearch's querying capabilities but provides a user-friendly interface for creating visualizations, building queries through a visual query builder, and conducting ad-hoc data exploration.

  3. User Interface: Elasticsearch primarily exposes its functionality through a RESTful API, which allows developers to interact with the search engine programmatically. Kibana, on the other hand, offers a web-based GUI specifically designed for data visualization and exploration. It provides a user-friendly interface to create, customize, and share dashboards, visualizations, and reports.

  4. Data Visualization: While Elasticsearch can return search results and aggregations in JSON format, Kibana excels in transforming raw data into rich visual representations. It offers a wide range of visualization types, such as line charts, bar charts, pie charts, maps, and more. Users can create interactive dashboards by combining multiple visualizations, applying filters, and drilling down into specific data points for deeper analysis.

  5. Integration with Elastic Stack: Elasticsearch and Kibana are tightly integrated components of the Elastic Stack. Elasticsearch stores and indexes data, while Kibana provides a visual interface to explore and analyze that data. They can be used together to create powerful search and analytics solutions. Kibana relies on Elasticsearch as its primary data source, and it requires Elasticsearch to be installed and configured.

In summary, Elasticsearch focuses on search, indexing, and analytics capabilities, making it ideal for storing and retrieving large volumes of data. Kibana specializes in data visualization and exploration, allowing users to create interactive dashboards and visually analyze data. When used together, Elasticsearch and Kibana provide a comprehensive solution for managing and deriving insights from data.

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Advice on Elasticsearch, Kibana

matteo1989it
matteo1989it

Jun 26, 2019

ReviewonKibanaKibanaGrafanaGrafanaElasticsearchElasticsearch

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
Rana Usman
Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceonFirebaseFirebaseElasticsearchElasticsearchAlgoliaAlgolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k
Comments
StackShare
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

Detailed Comparison

Elasticsearch
Elasticsearch
Kibana
Kibana

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

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.

Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License
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
-
GitHub Stars
20.8K
GitHub Forks
-
GitHub Forks
8.5K
Stacks
35.5K
Stacks
20.6K
Followers
27.1K
Followers
16.4K
Votes
1.6K
Votes
262
Pros & Cons
Pros
  • 329
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
Cons
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
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
Integrations
Beats
Beats
Logstash
Logstash
Logstash
Logstash
Beats
Beats

What are some alternatives to Elasticsearch, Kibana?

Algolia

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Grafana

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

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

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

Netdata

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

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

Sensu

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

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

Typesense

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Lumigo

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.

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