Cacti vs Kibana

Overview

Cacti

Stacks89

Followers202

Votes10

Kibana

Stacks20.6K

Followers16.4K

Votes262

GitHub Stars20.8K

Forks8.5K

Cacti vs Kibana: What are the differences?

1. Data Collection and Monitoring: Cacti primarily focuses on network monitoring and data collection using SNMP (Simple Network Management Protocol) to gather performance data, while Kibana is designed for log analysis and visualization from various sources such as Elasticsearch, Logstash, and Beats.

2. Visualization and Dashboards: Cacti offers basic graphing and visualization capabilities for network metrics, whereas Kibana provides advanced visualization features like interactive charts, maps, and dashboards to analyze log data effectively.

3. Alerting and Notification: Cacti does not include built-in alerting functionality, requiring users to integrate with external monitoring tools for alerting purposes. In contrast, Kibana offers robust alerting and notification mechanisms to notify users based on specific conditions or thresholds.

4. User Interface and Customization: Cacti has a traditional and somewhat rigid user interface, limiting customization options for users. Kibana, on the other hand, provides a modern and flexible interface that allows extensive customization and configuration to suit individual preferences and requirements.

5. Data Processing Capabilities: Cacti focuses on historical data storage and visualization, whereas Kibana offers powerful data processing capabilities through Elasticsearch, enabling real-time data analysis, filtering, and aggregation for timely insights.

6. Integration and Ecosystem: Cacti has limited integration capabilities compared to Kibana, which seamlessly integrates with the Elastic Stack components like Elasticsearch, Logstash, Beats, and X-Pack for a comprehensive data analytics ecosystem.

In Summary, Cacti is tailored for network monitoring and SNMP-based data collection, while Kibana excels in log analysis, visualization, alerts, and real-time data processing within a rich ecosystem.

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CLI (Node.js)

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Advice on Cacti, Kibana

Leonardo Henrique da

Pleno QA Enginneer at SolarMarket

Dec 8, 2020

Decided

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

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Comments

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

Detailed Comparison

Cacti	Kibana
Cacti is a complete network graphing solution designed to harness the power of RRDTool's data storage and graphing functionality. Cacti provides a fast poller, advanced graph templating, multiple data acquisition methods, and user management features out of the box.	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.
Unlimited number of graph items can be defined for each graph optionally utilizing CDEFs or data sources from within cacti.;Automatic grouping of GPRINT graph items to AREA, STACK, and LINE[1-3] to allow for quick re-sequencing of graph items.;Auto-Padding support to make sure graph legend text lines up.;Graph data can be manipulated using the CDEF math functions built into RRDTool. These CDEF functions can be defined in cacti and can be used globally on each graph.;Data sources can be created that utilize RRDTool's "create" and "update" functions. Each data source can be used to gather local or remote data and placed on a graph.	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 89	Stacks 20.6K
Followers 202	Followers 16.4K
Votes 10	Votes 262
Pros & Cons
Pros 3 Free 3 Rrdtool based 2 Fast poller 1 Graphs from snmp 1 Graphs from language independent scripts	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 Elasticsearch is huge 4 Works on top of elastic only 3 Hardweight UI
Integrations
RRDtool	Logstash Elasticsearch Beats

What are some alternatives to Cacti, Kibana?

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.

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.

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

Jaeger

Jaeger, a Distributed Tracing System

Related Comparisons

Cacti vs Kibana: What are the differences?

In Summary, Cacti is tailored for network monitoring and SNMP-based data collection, while Kibana excels in log analysis, visualization, alerts, and real-time data processing within a rich ecosystem.

Cacti vs Kibana

Overview