InfluxDB vs. Kibana

InfluxDB is very good at storing monitoring metrics (e.g. performance data). InfluxDB is not the right choice if you need to store other data types (like plain text, data relations etc.).

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Kibana is indeed a powerful tool and has many use cases especially in environments that rely heavily on real-time log analysis and visualisation. Kibana’s ability to handle large volumes of log data and present it in an accessible, searchable format is invaluable. We use Kibana to monitor security related issues and it proactively alerts our Slack channels about any anomality or issues.

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• Perfect handling telemetry data.
• Low latency, near real time.
• SQL-like language makes it easier to query.

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• Fast searches with powerful index.
• Beautiful data visualizations.
• Real-time observability.

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• GUI based administrator console

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• Some performance issues with large datasets.
• Linking to dashboards makes extremely long urls.
• Lack of reports.

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InfluxDB is a near perfect product for time series database engines. The relatively small list of cons are heavily outweighed by it's ability to just work and be a very flexible and powerful database engine. The community and support provided by the corporation are the only areas I have little experience.

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Its usability is generally good and it provides teams with a basic to intermediate understanding about data visualization. It is very user-friendly when it comes to creating dashboards. The UI is very good and simple. Its integration with other tools for alerting and reporting is amazing. But its advance features have a learning curve and a first timer needs some time to use the advance features.

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We have worked with the InfluxDB support team a few times so far and it has been positive. Issues submitted are worked on promptly and we have good feedback.

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We did not use the official Kibana support. Documentation was easy enough to follow.

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To be honest, I didn't look at alternatives since InfluxDB performs very well if you can oversee the lack of security and HA features. But for all challenges, there is an easy solution which brings you forward (e.g. read load balancing can be achieved by using a common HTTPS load balancer).

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Kibana is free; it was the first and only thing we've tried in this area.

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• Provided us a time series DB.
• With the SQL-like language, it is very easy to learn.
• Empowered us to keep track of our events.

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• Issues that affect checkout experiences for customers are able to be prioritized and solved quickly.
• We are able to more efficiently use resources due to the automation of reporting alerts. Decreasing employee resources needed.
• Visualization allows us to quickly share issues and explain to coworkers in order to escalate issues that can cost our bottom line.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info