Apache Ambari
A completely open source management platform for provisioning, managing, monitoring and securing Apache Hadoop clusters. Apache Ambari takes the guesswork out of operating Hadoop.
Apache Ambari, as part of the Hortonworks Data Platform, allows enterprises to plan, install and securely configure HDP making it easier to provide ongoing cluster maintenance and management, no matter the size of the cluster.
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For additional details about this release review the following resources:
Ambari key features
Ambari makes Hadoop management simpler by providing a consistent, secure platform for operational control. Ambari provides an intuitive Web UI as well as a robust REST API, which is particularly useful for automating cluster operations. With Ambari, Hadoop operators get the following core benefits:
- Simplified Installation, Configuration and Management. Easily and efficiently create, manage and monitor clusters at scale. Takes the guesswork out of configuration with Smart Configs and Cluster Recommendations. Enables repeatable, automated cluster creation with Ambari Blueprints.
- Centralized Security Setup. Reduce the complexity to administer and configure cluster security across the entire platform. Helps automate the setup and configuration of advanced cluster security capabilities such as Kerberos and Apache Ranger.
- Full Visibility into Cluster Health. Ensure your cluster is healthy and available with a holistic approach to monitoring. Configures predefined alerts — based on operational best practices — for cluster monitoring. Captures and visualizes critical operational metrics — using Grafana — for analysis and troubleshooting. Integrated with Hortonworks SmartSense for proactive issue prevention and resolution.
- Highly Extensible and Customizable. Fit Hadoop seamlessly into your enterprise environment. Highly extensible with Ambari Stacks for bringing custom services under management, and with Ambari Views for customizing the Ambari Web UI.
Ambari User Views
It’s time to put a new face on Hadoop using the Ambari Views framework. A “view” is a way of extending Ambari that allows 3rd parties to plug in new resource types along with the APIs, providers and UI to support them. Ambari is the only open source and open community effort designed to provide a compelling user experience for Hadoop while delivering consistent lifecycle management and security. Most notably, there are the Ambari User Views contributions actively being worked in the community. Ambari User Views are designed to provide capabilities that assist with the operational aspects of data application development and workload management.
| User View | Description |
|---|---|
| Tez | The Tez View helps you understand and optimize your cluster resource usage. Using the view, you can optimize and accelerate individual SQL queries or Pig jobs to get the best performance in a multi-tenant Hadoop environment. |
| Hive | Hive View allows the user to write & execute SQL queries on the cluster. It shows the history of all Hive queries executed on the cluster whether run from Hive view or another source such as JDBC/ODBC or CLI. It also provides graphical view of the query execution plan. This helps the user debug the query for correctness and for tuning the performance. It integrates Tez View that allows the user to debug any Tez job, including monitoring the progress of a job (whether from Hive or Pig) while it is running. This view contribution can be found here. |
| Pig | Pig View is similar to the Hive View. It allows writing and running a Pig script. It has support for saving scripts, and loading and using existing UDFs in scripts. This view contribution can be found here. |
| Capacity Scheduler | Capacity Scheduler View helps a Hadoop operator setup YARN workload management easily to enable multi-tenant and multi-workload processing. This view provisions cluster resources by creating and managing YARN queues. This view contribution can be found here. |
| Files | Files View allows the user to manage, browse and upload files and folders in HDFS. This view contribution can be found here. |