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Dejavu
Dejavu

7
18
+ 1
5
Kibana
Kibana

4.5K
3K
+ 1
227
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Dejavu vs Kibana: What are the differences?

Developers describe Dejavu as "A modern, open-source data browser for 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). On the other hand, Kibana is detailed as "Explore & Visualize Your Data". 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 belongs to "Search Tools" category of the tech stack, while Kibana can be primarily classified under "Monitoring Tools".

Dejavu and Kibana are both open source tools. It seems that Kibana with 12.6K GitHub stars and 4.89K forks on GitHub has more adoption than Dejavu with 6.05K GitHub stars and 373 GitHub forks.

What is Dejavu?

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).

What is Kibana?

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.
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    What are some alternatives to Dejavu and Kibana?
    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.
    Mirage
    The Elasticsearch query DSL supports 100+ query APIs ranging from full-text search, numeric range filters, geolocation queries to nested and span queries. Mirage is a modern, open-source web based query explorer for Elasticsearch.
    Elastic
    Elastic is an Elasticsearch client for the Go programming language.
    Searchkit
    Searchkit is a suite of React components that communicate directly with your Elasticsearch cluster. Each component is built in React and is fully customisable to your needs.
    Elassandra
    Elassandra is a fork of Elasticsearch modified to run on top of Apache Cassandra in a scalable and resilient peer-to-peer architecture. Elasticsearch code is embedded in Cassanda nodes providing advanced search features on Cassandra tables and Cassandra serve as an Elasticsearch data and configuration store.
    See all alternatives
    Decisions about Dejavu and Kibana
    StackShare Editors
    StackShare Editors
    Icinga
    Icinga
    Graphite
    Graphite
    Logstash
    Logstash
    Elasticsearch
    Elasticsearch
    Grafana
    Grafana
    Kibana
    Kibana

    One size definitely doesn鈥檛 fit all when it comes to open source monitoring solutions, and executing generally understood best practices in the context of unique distributed systems presents all sorts of problems. Megan Anctil, a senior engineer on the Technical Operations team at Slack gave a talk at an O鈥橰eilly Velocity Conference sharing pain points and lessons learned at wrangling known technologies such as Icinga, Graphite, Grafana, and the Elastic Stack to best fit the company鈥檚 use cases.

    At the time, Slack used a few well-known monitoring tools since it鈥檚 Technical Operations team wasn鈥檛 large enough to build an in-house solution for all of these. Nor did the team think it鈥檚 sustainable to throw money at the problem, given the volume of information processed and the not-insignificant price and rigidity of many vendor solutions. With thousands of servers across multiple regions and millions of metrics and documents being processed and indexed per second, the team had to figure out how to scale these technologies to fit Slack鈥檚 needs.

    On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they鈥檝e tried to build usability into Grafana that reflects the team鈥檚 mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

    See more
    Grafana
    Grafana
    Splunk
    Splunk
    Kibana
    Kibana

    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.

    See more
    Elasticsearch
    Elasticsearch
    Grafana
    Grafana
    Kibana
    Kibana

    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

    See more
    Kibana
    Kibana
    Grafana
    Grafana

    For our Predictive Analytics platform, we have used both Grafana and Kibana

    Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

    For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

    • Creating and organizing visualization panels
    • Templating the panels on dashboards for repetetive tasks
    • Realtime monitoring, filtering of charts based on conditions and variables
    • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
    See more
    Interest over time
    Reviews of Dejavu and Kibana
    Avatar of siddharthlatest
    primary, secondary education at Appbase
    Review ofDejavuDejavu

    Disclaimer: I am one of the contributors to the codebase.

    We use dejavu in production at appbase.io, and it was originally designed to scratch our own itch of a lack of a databrowser that's widely available (Elasticsearch plugin, Chrome App, Hosted on github pages) and has a modern UI.

    How developers use Dejavu and Kibana
    Avatar of Clarabridge Engage
    Clarabridge Engage uses KibanaKibana

    Used for graphing internal logging data; including metrics related to how fast we serve pages and execute MySQL/ElasticSearch queries.

    Avatar of Wirkn Inc.
    Wirkn Inc. uses KibanaKibana

    Our Kibana instances uses our ElasticSearch search data to help answer any complicated questions we have about our data.

    Avatar of Hevelop
    Hevelop uses KibanaKibana

    Kibana is our tools to query data in Elasticsearch clusters set up as catalog search engine.

    Avatar of Diogo Silva
    Diogo Silva uses KibanaKibana

    Perfect for exploring and visualizing the data available at ElasticSearch

    Avatar of Tongliang Liu
    Tongliang Liu uses KibanaKibana

    Log visualization. Wish it could add built-in alert functionality.

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    How much does Kibana cost?
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