Ambari vs Apache Mesos: What are the differences?

Introduction: Apache Ambari and Apache Mesos are both popular open-source software tools for managing large-scale data clusters. However, they have key differences in terms of their architecture, functionality, and use cases.

1. Architecture: Ambari is primarily designed for managing and provisioning Hadoop-based software stacks, providing a comprehensive web-based interface and RESTful API. On the other hand, Mesos focuses on resource management and scheduling of applications in large-scale distributed environments, offering a decentralized architecture with a master-slave model.

2. Resource Management: Ambari focuses on managing and monitoring the resources used by Hadoop clusters, providing functionalities for configuration management, health checks, and service monitoring. Mesos, on the other hand, abstracts resources from the machines in a cluster and provides them as a single pool, allowing various frameworks to efficiently share and allocate resources.

3. Scheduling: Ambari primarily focuses on the automated scheduling and orchestration of Hadoop-related services and tasks, ensuring scalability and high availability. In contrast, Mesos provides a fine-grained scheduling mechanism that allows applications to dynamically share and utilize resources, with support for frameworks such as Spark, Marathon, and Chronos.

4. Containerization: Ambari supports containerization through integration with technologies like Docker and Kubernetes, allowing users to deploy and manage containerized applications within Hadoop clusters. Mesos, on the other hand, has built-in support for containerization, enabling users to run tasks within Mesos containers and manage their lifecycle efficiently.

5. Ecosystem: Ambari is tightly integrated with the Hadoop ecosystem, providing seamless management for related projects like HDFS, MapReduce, YARN, Hive, and HBase. Mesos, on the other hand, is designed to support a wider range of frameworks and applications, including but not limited to Hadoop, providing flexibility in accommodating various workloads.

6. Scalability: Ambari is primarily intended for managing medium to large-scale Hadoop clusters, providing features like automated rolling restarts, configuration versioning, and role-based access control. Mesos, on the other hand, is known for its scalability and ability to handle extremely large clusters, with proven deployments managing tens of thousands of nodes.

In summary, while both Ambari and Mesos are powerful tools for managing data clusters, Ambari is specifically tailored for managing Hadoop-based software stacks, focusing on configuration management, scheduling, and orchestration. Mesos, on the other hand, provides a more generalized resource management and scheduling framework, allowing for efficient utilization of resources by various applications and frameworks beyond Hadoop.