Alternatives to Kibana logo

Alternatives to Kibana

Grafana, Loggly, Graylog, Splunk, and Prometheus are the most popular alternatives and competitors to Kibana.
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What is Kibana and what are its top alternatives?

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.
Kibana is a tool in the Monitoring Tools category of a tech stack.
Kibana is an open source tool with 14K GitHub stars and 5.6K GitHub forks. Here’s a link to Kibana's open source repository on GitHub

Top Alternatives of Kibana

Kibana alternatives & related posts

related Grafana posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 12 upvotes · 1.5M views
atUber TechnologiesUber Technologies
Prometheus
Prometheus
Graphite
Graphite
Grafana
Grafana
Nagios
Nagios

Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

https://eng.uber.com/m3/

(GitHub : https://github.com/m3db/m3)

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Grafana
Grafana
Kibana
Kibana

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)
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Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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    Splunk
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    Kibana logo
    Kibana

    related Splunk posts

    Kibana
    Kibana
    Splunk
    Splunk
    Grafana
    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.

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    related Prometheus posts

    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 12 upvotes · 1.5M views
    atUber TechnologiesUber Technologies
    Prometheus
    Prometheus
    Graphite
    Graphite
    Grafana
    Grafana
    Nagios
    Nagios

    Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

    By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

    To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

    See more
    Raja Subramaniam Mahali
    Raja Subramaniam Mahali
    Prometheus
    Prometheus
    Kubernetes
    Kubernetes
    Sysdig
    Sysdig

    We have Prometheus as a monitoring engine as a part of our stack which contains Kubernetes cluster, container images and other open source tools. Also, I am aware that Sysdig can be integrated with Prometheus but I really wanted to know whether Sysdig or sysdig+prometheus will make better monitoring solution.

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    Tableau logo

    Tableau

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    Tableau helps people see and understand data.
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      Tableau
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      Kibana
      New Relic logo

      New Relic

      15.6K
      4.2K
      1.9K
      15.6K
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      SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
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      New Relic
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      Sebastian Gębski
      Sebastian Gębski
      CTO at Shedul/Fresha · | 4 upvotes · 440.7K views
      atFresha EngineeringFresha Engineering
      CircleCI
      CircleCI
      Jenkins
      Jenkins
      Git
      Git
      GitHub
      GitHub
      New Relic
      New Relic
      AppSignal
      AppSignal
      Sentry
      Sentry
      Logentries
      Logentries

      Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).

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      Jerome Dalbert
      Jerome Dalbert
      Senior Backend Engineer at StackShare · | 4 upvotes · 170K views
      atStackShareStackShare
      Heroku
      Heroku
      New Relic
      New Relic
      Skylight
      Skylight
      Rails
      Rails
      Pingdom
      Pingdom
      Slack
      Slack

      We currently monitor performance with the following tools:

      1. Heroku Metrics: our main app is Hosted on Heroku, so it is the best place to get quick server metrics like memory usage, load averages, or response times.
      2. Good old New Relic for detailed general metrics, including transaction times.
      3. Skylight for more specific Rails Controller#action transaction times. Navigating those timings is much better than with New Relic, as you get a clear full breakdown of everything that happens for a given request.

      Skylight offers better Rails performance insights, so why use New Relic? Because it does frontend monitoring, while Skylight doesn't. Now that we have a separate frontend app though, our frontend engineers are looking into more specialized frontend monitoring solutions.

      Finally, if one of our apps go down, Pingdom alerts us on Slack and texts some of us.

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      related Datadog posts

      Robert Zuber
      Robert Zuber
      CTO at CircleCI · | 8 upvotes · 386.7K views
      atCircleCICircleCI
      Datadog
      Datadog
      PagerDuty
      PagerDuty
      Honeycomb
      Honeycomb
      Rollbar
      Rollbar
      Segment
      Segment
      Amplitude
      Amplitude
      PostgreSQL
      PostgreSQL
      Looker
      Looker

      Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

      We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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      StackShare Editors
      StackShare Editors
      Grafana
      Grafana
      StatsD
      StatsD
      Airflow
      Airflow
      PagerDuty
      PagerDuty
      Datadog
      Datadog
      Celery
      Celery
      AWS EC2
      AWS EC2
      Flask
      Flask

      Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

      Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

      There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

      Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

      Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

      Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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