Dejavu vs Kibana

Overview

Kibana

Stacks20.6K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

Dejavu

Stacks22

Followers41

Votes6

GitHub Stars8.4K

Forks518

Dejavu vs Kibana: What are the differences?

Key Differences Between Dejavu and Kibana

Dejavu and Kibana are both tools used for data visualization and exploration, but they have some key differences. Here are the top 6 differences between Dejavu and Kibana:

Data Source Support: Dejavu allows users to connect to various data sources like Elasticsearch, MySQL, PostgreSQL, and more, while Kibana is primarily designed to work with Elasticsearch as its data source. This makes Dejavu more flexible and suitable for different data storage options.
User Interface: Dejavu has a user-friendly and intuitive interface that makes it easy for users to interact with and explore their data. On the other hand, Kibana has a more sophisticated and feature-rich interface that might be overwhelming for some users, especially beginners.
Query and Aggregation Capabilities: Kibana offers advanced query and aggregation capabilities, allowing users to create complex search queries and perform aggregations on their data. In contrast, Dejavu provides a simpler query and aggregation functionality, which may be sufficient for basic data exploration but lacks advanced capabilities.
Visualization Options: Kibana provides a wide range of visualization options, including charts, tables, maps, and more. It also offers a variety of customization features to create visually appealing and interactive visualizations. Dejavu, on the other hand, has a more limited set of visualization options, mainly focused on presenting tabular data.
Integration with Existing Systems: Kibana seamlessly integrates with the full Elastic Stack, including Elasticsearch, Logstash, and Beats. This allows users to ingest, search, analyze, and visualize their data all within the Elastic Stack ecosystem. Dejavu, although it can connect to different data sources, does not have the same level of integration with other systems.
Community and Support: Kibana has a large and active community of users and developers, providing extensive documentation, tutorials, and support resources. Dejavu, being a relatively smaller and less popular tool, may have limited community support available.

In summary, Dejavu offers broader data source support, a user-friendly interface, and simpler query capabilities, while Kibana provides a more powerful and feature-rich environment with advanced visualization options, integration with Elastic Stack, and a stronger community support.

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Advice on Kibana, Dejavu

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	Dejavu
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.	dejaVu fits the unmet need of being a hackable data browser for Elasticsearch. Existing browsers were either built with a legacy UI and had a lacking user experience or used server side rendering (I am looking at you, Kibana).
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 8.4K
GitHub Forks 8.5K	GitHub Forks 518
Stacks 20.6K	Stacks 22
Followers 16.4K	Followers 41
Votes 262	Votes 6
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 2 Available as a chrome app 2 Open-source (MIT License) 1 Clean and modern data browsing UI 1 Available as a docker image
Integrations
Logstash Elasticsearch Beats	Elasticsearch

What are some alternatives to Kibana, Dejavu?

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 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.

Solr

Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.

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.

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

Dejavu vs Kibana: What are the differences?

Key Differences Between Dejavu and Kibana

Dejavu and Kibana are both tools used for data visualization and exploration, but they have some key differences. Here are the top 6 differences between Dejavu and Kibana:

Data Source Support: Dejavu allows users to connect to various data sources like Elasticsearch, MySQL, PostgreSQL, and more, while Kibana is primarily designed to work with Elasticsearch as its data source. This makes Dejavu more flexible and suitable for different data storage options.
User Interface: Dejavu has a user-friendly and intuitive interface that makes it easy for users to interact with and explore their data. On the other hand, Kibana has a more sophisticated and feature-rich interface that might be overwhelming for some users, especially beginners.
Query and Aggregation Capabilities: Kibana offers advanced query and aggregation capabilities, allowing users to create complex search queries and perform aggregations on their data. In contrast, Dejavu provides a simpler query and aggregation functionality, which may be sufficient for basic data exploration but lacks advanced capabilities.
Visualization Options: Kibana provides a wide range of visualization options, including charts, tables, maps, and more. It also offers a variety of customization features to create visually appealing and interactive visualizations. Dejavu, on the other hand, has a more limited set of visualization options, mainly focused on presenting tabular data.
Integration with Existing Systems: Kibana seamlessly integrates with the full Elastic Stack, including Elasticsearch, Logstash, and Beats. This allows users to ingest, search, analyze, and visualize their data all within the Elastic Stack ecosystem. Dejavu, although it can connect to different data sources, does not have the same level of integration with other systems.
Community and Support: Kibana has a large and active community of users and developers, providing extensive documentation, tutorials, and support resources. Dejavu, being a relatively smaller and less popular tool, may have limited community support available.

Dejavu vs Kibana

Overview

Dejavu vs Kibana: What are the differences?