Ambari vs Bosun

Overview

Bosun

Stacks18

Followers52

Votes3

GitHub Stars3.4K

Forks492

Ambari

Stacks44

Followers74

Votes2

Ambari vs Bosun: What are the differences?

# Introduction
Key differences between Ambari and Bosun:

1. **Purpose**: Ambari is mainly used for managing and monitoring Apache Hadoop clusters, providing a web-based dashboard for centralized management. In contrast, Bosun is a monitoring and alerting system that focuses on collecting and processing time-series data for real-time monitoring and alerting purposes.
   
2. **Scalability**: Ambari is designed to scale with larger and more complex Hadoop clusters, offering features for managing resources and configuration across many nodes. On the other hand, Bosun is more lightweight and efficient, suitable for smaller deployments or when real-time monitoring is a priority.

3. **Customization**: Ambari provides a comprehensive set of tools and features for managing various components of a Hadoop cluster, allowing users to customize configurations, metrics, and alerts. Bosun, while being highly customizable as well, is more focused on monitoring and alerting functionalities, with a specialized query language for creating custom alert conditions.

4. **Integration**: Ambari integrates seamlessly with other Apache Hadoop ecosystem tools, providing a unified platform for managing the entire data infrastructure. In contrast, Bosun can be integrated with various data sources and systems to collect metrics and trigger alerts, making it flexible for use in diverse environments.

5. **Community Support**: Ambari benefits from a strong and active community of users and developers contributing to its ongoing development and improvement. Bosun, being a relatively newer project, has a smaller but dedicated community that focuses on refining its monitoring and alerting capabilities.

6. **Ease of Use**: Ambari offers a user-friendly interface with graphical tools for configuring and monitoring Hadoop clusters, making it more accessible to users with varying levels of expertise. Bosun, while powerful in terms of monitoring capabilities, may require more technical proficiency to set up and utilize effectively.

In Summary, Ambari and Bosun differ in their purpose, scalability, customization options, integration capabilities, community support, and ease of use.

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CLI (Node.js)

Manual

Detailed Comparison

Bosun	Ambari
Bosun is an open-source, MIT licensed, monitoring and alerting system by Stack Exchange. It has an expressive domain specific language for evaluating alerts and creating detailed notifications. It also lets you test your alerts against history for a faster development experience.	This project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. It provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs.
Save time by testing alerting against historical data and reduce alert noise before an alert goes into production;Supports querying OpenTSDB, Graphite, and Logstash-Elasticsearch;Create notifications using Bosun's template language: include graphs, tables, and contextual information	Alerts; Ambari Python Libraries; Automated Kerberizaton; Blueprints; Configurations; Service Dashboards; Metrics
Statistics
GitHub Stars 3.4K	GitHub Stars -
GitHub Forks 492	GitHub Forks -
Stacks 18	Stacks 44
Followers 52	Followers 74
Votes 3	Votes 2
Pros & Cons
Pros 1 Powerful alerting 1 Query multiple tsdbs 1 Query Elasticsearch	Pros 2 Ease of use
Integrations
No integrations available	Hadoop Ubuntu Debian

What are some alternatives to Bosun, Ambari?

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.

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.

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).

Related Comparisons

Ambari vs Bosun: What are the differences?

# Introduction
Key differences between Ambari and Bosun:

1. **Purpose**: Ambari is mainly used for managing and monitoring Apache Hadoop clusters, providing a web-based dashboard for centralized management. In contrast, Bosun is a monitoring and alerting system that focuses on collecting and processing time-series data for real-time monitoring and alerting purposes.
   
2. **Scalability**: Ambari is designed to scale with larger and more complex Hadoop clusters, offering features for managing resources and configuration across many nodes. On the other hand, Bosun is more lightweight and efficient, suitable for smaller deployments or when real-time monitoring is a priority.

3. **Customization**: Ambari provides a comprehensive set of tools and features for managing various components of a Hadoop cluster, allowing users to customize configurations, metrics, and alerts. Bosun, while being highly customizable as well, is more focused on monitoring and alerting functionalities, with a specialized query language for creating custom alert conditions.

4. **Integration**: Ambari integrates seamlessly with other Apache Hadoop ecosystem tools, providing a unified platform for managing the entire data infrastructure. In contrast, Bosun can be integrated with various data sources and systems to collect metrics and trigger alerts, making it flexible for use in diverse environments.

5. **Community Support**: Ambari benefits from a strong and active community of users and developers contributing to its ongoing development and improvement. Bosun, being a relatively newer project, has a smaller but dedicated community that focuses on refining its monitoring and alerting capabilities.

6. **Ease of Use**: Ambari offers a user-friendly interface with graphical tools for configuring and monitoring Hadoop clusters, making it more accessible to users with varying levels of expertise. Bosun, while powerful in terms of monitoring capabilities, may require more technical proficiency to set up and utilize effectively.

In Summary, Ambari and Bosun differ in their purpose, scalability, customization options, integration capabilities, community support, and ease of use.

Ambari vs Bosun

Overview