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The Hortonworks Data Platform, powered by Apache Hadoop, is a massively scalable and 100% open source platform for storing, processing and analyzing large volumes of data. It is designed to deal with data from many sources and formats in a very quick, easy and cost-effective manner. The Hortonworks Data Platform consists of the essential set of Apache Hadoop projects including MapReduce, Hadoop Distributed File System (HDFS), HCatalog, Pig, Hive, HBase, ZooKeeper and Ambari. Hortonworks is the major contributor of code and patches to many of these projects. These projects have been integrated and tested as part of the Hortonworks Data Platform release process and installation and configuration tools have also been included.

Unlike other providers of platforms built using Apache Hadoop, Hortonworks contributes 100% of our code back to the Apache Software Foundation. The Hortonworks Data Platform is Apache-licensed and completely open source. We sell only expert technical support, training and partner-enablement services. All of our technology is, and will remain free and open source. Please visit the Hortonworks Data Platform page for more information on Hortonworks technology. For more information on Hortonworks services, please visit either the Support or Training page. Feel free to Contact Us directly to discuss your specific needs.


1. Using Ambari Views
2. Preparing Ambari Server for Views
3. Running Ambari Server Standalone
1. Prerequisites
2. Standalone Server Setup
3. Reverse Proxy
4. Configuring Views for Kerberos
5. Using the Tez View
1. Configuring Your Cluster for Tez View
2. Creating or Editing the Tez View Instance
2.1. Modifying a Tez View instance on an Ambari-managed cluster
2.2. Creating a new Tez View instance for a manually-deployed cluster:
2.3. User Permissions for Tez Views
2.4. Kerberos Setup for Tez Views
3. Using the Tez View
3.1. Understanding Directed Acyclic Graphs (DAGs), Vertices, and Tasks
3.2. Identifying the Tez DAG for Your Job
3.3. Understanding How Your Tez Job Is Executed
3.4. Identifying Causes of Failed Jobs
3.5. Viewing All Failed Tasks
3.6. Using Counters to Identify the Cause of Slow-Performing Jobs
6. Using the Pig View
1. Configuring Your Cluster
1.1. Setup HDFS Proxy User
1.2. Setup WebHCat Proxy User
1.3. Setup HDFS User Directory
2. Creating the Pig View Instance
2.1. Getting Correct Configuration Values for Manually-Deployed Clusters
2.2. User Permissions for Pig Views
2.3. Kerberos Setup for Pig Views
3. Using the Pig View
3.1. Writing Pig Scripts
3.2. Viewing Pig Script Execution History
3.3. User-Defined Functions (UDFs) Tab
7. Using the Capacity Scheduler View
1. Configuring your Cluster for the Capacity Scheduler View
2. Creating a Capacity Scheduler View Instance
2.1. User Permissions for Capacity Scheduler Views
3. Using the Capacity Scheduler View
3.1. Setting up Queues
3.2. Configuring Queues
3.3. Configuring Cluster Scheduler Settings
3.4. Applying the Configuration Changes
4. Troubleshooting
8. Using the Hive View
1. Configuring Your Cluster
1.1. Setup HDFS Proxy User
1.2. Setup HDFS User Directory
2. Creating the Hive View Instance
2.1. Settings and Cluster Configuration
2.2. User Permissions for Hive Views
2.3. Kerberos Setup for Hive Views
3. Using the Hive View
3.1. Query Tab
3.2. Saved Queries Tab
3.3. History Tab
3.4. UDF Tab
4. Troubleshooting
9. Using the Slider View
1. Deploying the Slider View
10. Using the Files View
1. Configuring Your Cluster
2. Creating and Configuring a Files View Instance
2.1. Kerberos Settings
2.2. Cluster Configuration: Local
2.3. Cluster Configuration: Custom
2.4. Troubleshooting