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Ambari

43
74
+ 1
2
collectd

98
156
+ 1
5
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Ambari vs collectd: What are the differences?

1. Scalability: Ambari is designed for managing large-scale clusters, providing a scalable and centralized solution for cluster management. Collectd, on the other hand, is more focused on collecting system performance metrics on individual servers or systems rather than cluster management. 2. User Interface: Ambari provides a user-friendly web interface for monitoring and managing Hadoop clusters, making it easier for administrators to navigate and perform tasks. Collectd, however, lacks a comprehensive graphical user interface and is mainly configured through text-based configuration files. 3. Monitoring Capabilities: Ambari offers a wide range of monitoring capabilities specifically tailored for Hadoop clusters, including metrics, alerts, and a customizable dashboard. Collectd is more generic and versatile, capable of monitoring various types of system performance metrics beyond Hadoop clusters. 4. Integration: Ambari seamlessly integrates with various Hadoop ecosystem components, ensuring compatibility and smooth operation within the Hadoop ecosystem. Collectd can be integrated with different monitoring and visualization tools, making it a flexible option for system monitoring in a variety of environments. 5. Community Support: Ambari benefits from a strong community of users and contributors, leading to regular updates, bug fixes, and new feature implementations. Collectd also has a supportive community but may not have as large of a following or frequent updates as Ambari. 6. Management features: Ambari provides robust cluster management features such as configuration management, service monitoring, and deployment automation to streamline and simplify administrative tasks. Collectd, being more focused on data collection, may lack the same level of built-in management features for overseeing and controlling system resources efficiently.

In Summary, Ambari is tailored for scalable cluster management with a user-friendly interface, extensive monitoring capabilities, and strong integration within the Hadoop ecosystem, while Collectd excels in generic system performance data collection with more flexibility and versatility but may lack the same level of cluster management features.

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Pros of Ambari
Pros of collectd
  • 2
    Ease of use
  • 2
    Open Source
  • 2
    Modular, plugins
  • 1
    KISS

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What is Ambari?

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.

What is collectd?

collectd gathers statistics about the system it is running on and stores this information. Those statistics can then be used to find current performance bottlenecks (i.e. performance analysis) and predict future system load (i.e. capacity planning). Or if you just want pretty graphs of your private server and are fed up with some homegrown solution you're at the right place, too.

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Jobs that mention Ambari and collectd as a desired skillset
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San Francisco, United States
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What are some alternatives to Ambari and collectd?
Hue
It is open source and lets regular users import their big data, query it, search it, visualize it and build dashboards on top of it, all from their browser.
Zookeeper
A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications.
Apache Mesos
Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers.
Yarn
Yarn caches every package it downloads so it never needs to again. It also parallelizes operations to maximize resource utilization so install times are faster than ever.
Kubernetes
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.
See all alternatives