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HDF > Develop with Hadoop > Hello World

Getting Started with HDF Sandbox

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In this tutorial, you will learn about the different features available in the HDF sandbox. HDF stands for Hortonworks DataFlow. HDF was built to make processing data-in-motion an easier task while also directing the data from source to the destination. You will learn about quick links to access these tools that way when you finish the tutorial, you will know how to access NiFi, Storm, Ranger and Ambari UIs. Therefore, you will be able to focus on using these apache tools for dealing with data-in-motion.



Note: It may be good idea for each procedure to start with an action verb (e.g. Install, Create, Implement, etc.)


What is the Sandbox?

The Sandbox is a straightforward, pre-configured, learning environment that contains the latest developments from Apache Big Data related tools, specifically these tools were assembled together into Hortonworks DataFlow. The Sandbox comes packaged in a virtual environment that can run in the cloud or on your personal machine. The Sandbox allows you to learn and explore HDP on your own.

Learn the Host Address of Your Environment

Once you installed the Sandbox VM or container, it resolves to the host on your environment, the address of which varies depending upon the Virtual Machine you are using(Vmware, VirtualBox, etc) or container (Docker, etc). As, a general thumb rule, wait for the installation to complete and confirmation screen will tell you the host your sandbox resolves to. For example:

In case of Docker: host would be

Add Sandbox Hostname to Your Hosts File

To be able to access HDF services on the Sandbox from our local machine’s browser, you will need to add mapped to your IP address to the list of hosts in your hosts file. The location of the hosts file is different for each OS.

For mac users, enter the following command in your terminal:

echo '{Host-Name}' | sudo tee -a /private/etc/hosts

NOTE: In single machine, just replace {Host-Name} with

For most Linux users, enter the following command in your terminal:

echo '{Host-Name}' | sudo tee -a /private/etc/hosts

NOTE: In single machine, just replace {Host-Name} with

1. For Windows 10 users, open Notepad as an administrator. Open the “hosts” file at this path: c:WindowsSystem32driversetchosts and add:   localhost

Note: Make sure to remove the “#” hash symbol from the left side of the line and add “”.

Then save the file.

Connect to the Welcome Screen

Append the port number :18888 to your host address, open your browser, and access Sandbox Welcome page at http://_host_:18888/.

Click on Launch Dashboard to go to Ambari with a Analyze Traffic Patterns with Apache NiFi tutorial and Quick Links to view some services of HDP environment.

Multiple Ways to Execute Terminal Commands

Note: For all methods below, the login credential instructions will be the same to access the Sandbox through the terminal.

  • Login using username as root and password as hadoop.
  • After first time login, you will be prompted to retype your current password, then change your password.
  • If you are using Putty on Windows then go to terminal of your sandbox in oracle virtualBox –> Press Alt+F5 –> enter username – root –> enter password – hadoop –> it will ask you to set new password –> set new password.

Open your terminal (mac and linux) or putty (windows). Type the following command to access the Sandbox through SSH:

# Usage:
      ssh <username>@<hostname> -p <port>;
# Example:
      ssh root@ -p 12222;

Shell Web Client Method:

Open your web browser. Type the following text into your browser to access the Sandbox through the shell:

# Usage:
    #  _host_:14200

Explore Ambari

Navigate to Ambari welcome page using the url given on Sandbox welcome page or access at _host_:18080.

Note: Both the username and password to login are admin.

Services Provided By the Ambari

Service URL
Sandbox Welcome Page http://host:18888
Ambari Dashboard http://host:18080
NiFi UI http://host:19090/nifi
Ranger UI http://host:16080
Storm UI http://host:18744
Solr UI http://host:18886
Grafana UI http://host:13000
Log Search UI http://host:62888

The following Table Contains Login Credentials:

Service User Password
Ambari, OS admin refer to setup ambari admin password

Setup Ambari admin Password Manually

  1. Start your sandbox and open a terminal (mac or linux) or putty (windows)
  2. SSH into the sandbox as root using ssh root@ -p 2222. For Azure and VMware users, your _host_ and _port_ will be different.
  3. Type the following commands:
# Updates password
# If Ambari doesn't restart automatically, restart ambari service
ambari-agent restart

Note: Now you can login to ambari as an admin user to perform operations, such as starting and stopping services.

Explore Ambari Welcome Screen 5 Key Capabilities

Enter the Ambari Welcome URL and then you should see the following options:

  1. Operate Your Cluster” will take you to the Ambari Dashboard which is the primary UI for Hadoop Operators
  2. Manage Users + Groups” allows you to add & remove Ambari users and groups
  3. Clusters” allows you to grant permission to Ambari users and groups
  4. Ambari User Views” list the set of Ambari Users views that are part of the cluster
  5. Deploy Views” provides administration for adding and removing Ambari User Views

Explore Ambari Dashboard Links

Enter the Ambari Dashboard URL and Click on:

1. Metrics, Heatmap and Configuration

and then the

2. Dashboard, Services, Hosts, Alerts, Admin and User Views icon (represented by 3×3 matrix ) to become familiar with the Ambari resources available to you.


Congratulations, you’ve finished your first tutorial! Now you know about the different features available in the HDF sandbox. You know how to access NiFi, Storm, Ranger and Ambari UIs. Therefore, you are now ready to focus on using these apache tools for dealing with data-in-motion. If you want to learn more about HDF, check out the documentation in the Further Reading section below.

Further Reading

Appendix A: Troubleshoot

Troubleshoot Problems

Check Hortonworks Community Connection(HCC) for answers to problems you may come across during your hadoop journey.

Technique for Finding Answers in HCC

  • Insert quotes around your tutorial related problem
  • Be specific by including keywords (error, tutorial name, etc.)