To create your first Sandbox, and to see how it is an integral part of the KBC workflow in our Getting Started tutorial.

A sandbox is a safe environment for you to

  • explore, analyze and experiment with copies of selected Storage data.
  • test, troubleshoot and develop transformations without modifying any Storage data.

You can fill a sandbox with any data from Storage. However, to simplify transformation development, KBC provides a specific loader for your transformations. It automatically fills the sandbox with relevant tables and takes the Input Mapping of the transformation into account.

Each user has one sandbox per project and backend (MySQL, Snowflake and Redshift) to their disposal. Sandboxes with different backends are very similar; but there are few specifics, mostly related to access management — see below.

Sandbox credentials

Important: The backend of the sandbox does not have to match the backend of the original data. For example, you can load data from Snowflake into a Redshift sandbox.

Also, your sandbox might be deleted after 14 days of inactivity unless extended; make sure not to use it as a permanent data storage!

Loading Data

Data can be loaded into your sandbox in two different ways:

  • Plain loading — Copying any tables from Storage into a Sandbox database.
  • Transformation loading — Loading specifically tailored for transformation development. Select a transformation and all relevant tables based on the transformation input mapping are automatically loaded.

If you, while developing your transformation, need to add data to the tables already specified in the input mapping, the two ways can be combined. However, in that case, perform the plain load after the transformation load, because the transformation load deletes all data in the sandbox.

Plain Loading

Go to the Transformations section and click the Sandbox button in the upper right corner.

Screenshot - Plain Sandbox

If no sandbox exists, create it first by clicking Create Sandbox for the desired backend:

Screenshot - Plain Sandbox

Then click the Load data button to load tables or whole buckets into the sandbox. You can limit the number of rows that are loaded; this is useful for sampling a large table.

MySQL sandbox

The imported tables will have full tables names — including both a bucket and table name.

By default, the sandbox content is deleted before loading new data. Use the Preserve existing data option to keep its content and add new data to it.

Transformation Loading

This type of loading is intended for gradual development and debugging of your transformation code. Data loaded through the transformation loading is tied to a specific transformation bucket and input mapping. Only data specified in the input mapping is loaded into your transformation sandbox. You can choose whether the transformation itself is performed on the load or not.

To create a sandbox for a specific transformation, go to the Transformations section and select the respective transformation:

Screenshot - Transformation Sandbox

The Sandbox backend is defined by the transformation backend. In the transformation detail, click the Create Sandbox button:

Screenshot - Transformation Sandbox

Clicking the Create button will get you the connection credentials:

Screenshot - Transformation Sandbox

Choose how the data will be loaded and processed:

  • Load input tables only — load the tables specified in the input mapping;
  • Prepare transformation — execute transformation dependencies, that means the sandbox workspace is prepared for the current transformation (use only if there are any dependencies); and
  • Execute transformation without writing to Storage API — this is a dry-run for validation.

Once the sandbox is ready, you will get a notification. Or, watch the progress on the Jobs page.

Important: Transformation loading always deletes the contents of the sandbox first.

Additional Sandbox Actions

Except loading data, sandbox supports several other basic actions. To access them, go to the Transformations section and click the Sandbox button at the top.

  • Connect (MySQL and Snowflake only) — Connect to the sandbox using a web SQL client.
  • SSL (MySQL and Redshift only) — Show secure connection information.
  • Drop Sandbox — Deletes the sandbox database (and all its tables).
  • Extend Expiration — Postpone the sandbox expiration date for another period (14 days for MySQL, 5 days for Jupyter and RStudio).

In the same place you can also see the sandbox connection credentials. To copy & paste individual values, use the copy icon:

Screenshot - Plain Sandbox

Backend Specifics

Even though sandboxes with different backends are very similar, let’s take a look at a few specifics that are mostly related to access management.

MySQL Sandbox

For a single user, MySQL credentials are shared within all projects. Each project sandbox is represented as a database assigned to the user. The user and password remain the same until you delete all MySQL sandboxes in all your projects.

For instance, user_4 can have assigned the sand_232_3800, sand_258_3849 and sand_1067_46400 databases. These are sandboxes in projects 232, 258 and 1067. You can easily switch between them in your favorite MySQL client.

Connecting to Sandbox

To connect to a MySQL sandbox, use any MySQL client; for instance, Sequel Pro or DBeaver. We recommend using an SSL secure connection. To use a secure connection, download the SSL certificate and use it in your MySQL client.

Sequel Pro configuration:

Sequel Pro Configuration Screenshot

DBeaver configuration:

DBeaver Configuration Screenshot


MySQL sandboxes use MariaDB 5.5.44.

Redshift Sandbox

Credentials for Redshift sandboxes are not shared between projects. Each project, including the sandbox, sits on a different cluster. Therefore, all your projects have their own set of credentials.

Connecting to Sandbox

Almost any PostgreSQL client can connect to an AWS Redshift cluster. We have tested Navicat and DBeaver (free), and they work fine. You can use both a Redshift driver and a PostgreSQL driver.

If using the PostgreSQL driver, do not forget to change the connection port to 5439. For an SSL secure connection, follow this guide. To establish a secure connection to a Redshift sandbox, follow the official instructions from Amazon.

Direct Access to Storage Tables

In a Redshift sandbox, you have native access to all Redshift buckets in the project. You can easily access a table in Storage using schema/bucket namespacing. For example: SELECT * FROM "in.c-main"."mytable". Use double quotes, as the schema (= bucket) name always contains a dot.

We do not recommend working with Storage tables directly in your SQL code. Always use the input mapping to bring tables to your schema. This adds another level of security and features to your transformation.


A Redshift sandbox always uses the latest Redshift version available on the cluster.

RStudio Sandbox

Important: Currently, this feature is in beta.

The RStudio sandbox is available only as a plain sandbox:

Screenshot - Plain Sandbox

Click on the Create sandbox button,

Screenshot - RStudio Sandbox

and select the tables you want to load into the sandbox.

Screenshot - RStudio Sandbox

Depending on the size of the input tables, the sandbox creation may take some time. You can review its progress in Jobs. When the sandbox is created and you connect to it, you will be taken to a web version of RStudio. The loaded tables will be available at the same locations as in R Transformations. The R version is also the same as in R Transformations.

Screenshot - RStudio Sandbox

Note: Although it is possible to upload files directly into the RStudio sandbox, we highly recommend that you use only input mapping to load data into the sandbox. It is a more reliable and traceable method of loading data.

The RStudio Sandbox has the following limitations:

  • Sandbox disk space is limited to 10GB.
  • Memory is limited to 8GB.
  • The UI only allows tables to be loaded to Sandbox. Loading input files and transformation script is supported only by the API.
  • Sandboxes will be deleted after 5 days unless extended.
  • Adding data to existing sandboxes is not supported yet.

Jupyter Notebook Sandbox

Important: Currently, this feature is in beta.

The Jupyter Notebook sandbox is available only as a plain sandbox. It is created the same way as the RStudio Sandbox and the exact same limitations apply to it.

Screenshot - Jupyter Sandbox

The Python environment for Jupyter Notebook is the same as in Python Transformations. Jupyter Notebook does not have a built-in file browser, but the loaded tables are kept at the same locations as in Python Transformations. To list the available table data files, use scripts such as this one:

from os import listdir
from os.path import isfile, join

mypath = '/data/in/tables'
onlyfiles = [f for f in listdir(mypath)]

The Jupyter Notebook Sandbox has the following limitations:

  • Sandbox disk space is limited to 10GB.
  • Memory is limited to 8GB.
  • The UI only allows tables to be loaded to Sandbox. Loading input files and transformation script is supported only by the API.
  • Sandboxes will be deleted after 5 days unless extended.
  • Adding data to existing sandboxes is not supported yet.