A workspace serves several purposes and can be used as
You can create a Workspace on the Transformations page:
Select a workspace type; not all workspace types are supported in all project workspaces. Workspace types match available transformation types. For scripting languages (Python, R, and Julia), we provide JupyterLab with the matching kernel. For SQL workspaces, you can either use Snowflake workspace or a database client of your liking (for example, Dbeaver).
Name and optionally describe the workspace:
Creating the workspace takes a few seconds, so it will take a while before it appears:
Once the workspace is ready, you’ll see it in the workspace list:
In the workspace detail, you can
To connect to a JupyterLab workspace with the associated kernel (Python, R, Julia), use the URL and the password provided in the Credentials link. Use the Connect button to directly open the JupyterLab interface:
To connect to a Snowflake workspace,
To connect to a Synapse and Redshift workspaces, you have to use your database client (we like DBeaver) and use the database options provided in the Credentials view. If the database client does not have Synapse Driver, look for Azure SQL Server.
When a workspace is created, it enters the Active state and can be used.
Workspace termination can be done manually or it is done automatically after 1 hour period of inactivity (if auto sleep feature is supported and enabled).
Inactivity is measured from the last save of any notebook in JupyterLab. When a workspace is terminated, it is switched off
and consumes no credits. A terminated workspace can be resumed. Resuming a workspace means that we restore the
last saved version of all notebooks in the home directory (
/data). We also load the current data from Input Mapping.
On a restored workspace, you’ll get
Terminating and restoring a workspace means that you’ll loose
Note: When you terminate and restore the workspace, its password changes.
When a workspace is terminated and you return to the JupyterLab interface (e.g., when you put your computer to sleep), you’ll see the following error:
Server Connection Error A connection to the Jupyter server could not be established. JupyterLab will continue trying to reconnect. Check your network connection or Jupyter server configuration.
If you see this error, please go to the list of workspaces in Keboola Connection, resume the workspace and reconnect from there.
When loading data into a workspace, you can specify entire buckets, which can be especially useful when you are not sure what tables you’ll need in your work. You can also take advantage of alias tables and prepare buckets with the tables you’ll need.
Note: You must be using new transformations to see this feature.
The workspace also supports read-only input mappings, as described in the mapping section. For each workspace or Snowflake writer (data destination) configuration, users can choose whether to use a read-only input mapping.
Note that there are security implications. If you enable a read-only input mapping for a workspace, then it has access to all the data in the project. You may not want to share workspace credentials with other people unless it’s acceptable for them to see all the data in the project. If limited access is required, do not enable read-only input mappings for the workspace.
With read-only input mappings disabled, only tables listed in the input mapping are accessible.
So, if we enable this feature when we create a workspace, we can access individual tables in the workspace,
without needing to define any tables in the input mapping. However, a read-only input mapping cannot access alias tables, because technically it is just a reference to an existing schema.
This also applies to linked buckets. *Note that buckets and tables belong to another project, so you must access, for example, the database of another project depending on the backend.
For example, say your bucket
in.c-customers is linked from bucket
in.c-crm-extractor in project 123. You then need to reference the tables in the transformation like this:
"KEBOOLA_123"."in.c-crm-extractor"."my-table". When developing transformation code, it’s easiest to create a workspace with read-only input mappings enabled and look directly in the database to find the correct database and schema names.
Unloading data is useful, for example, when your ad-hoc analysis leads to valuable results, or when you trained a new model which you’d like to use in transformations.
When this feature is enabled in a project, your data in workspaces can be kept. This way you can, when you return, start where you left off without losing data or time by importing the data again or executing scripts to get to the right stage.
Once this feature is activated, we will automatically back up all the data in newly created workspaces up to the limits defined by your selected workspace size. More specifically:
Nothing changes for workspaces created before the feature was activated. Your data will be lost upon leaving the workspace. Workspaces created following the feature’s deactivation will not keep their data when you leave. However, if a workspace is created while the feature is activated, it will keep its data even after the feature is deactivated. Your data will be kept until the workspace is deleted.
Provisioning of persistent storage takes some time, usually 2–3 minutes after the feature is activated. To prevent workspaces that are begun in the meantime from becoming broken, we block their initiation until the storage is ready to be used.
Pricing: Curently for FREE in public beta. When it is generally available, additional charges will apply.
Workspaces are highly useful for developing transformations. When you configure mappings and develop a script in JupyterLab, you can use the Create Transformation button to deploy the notebook into a transformation. Please note that only input/output mapping without the actual script is copied when creating transformation from a workspace
Enter the name of the new transformation:
You can also create workspaces from transformations.
Apart from developing transformations, you can use workspaces to perform ad-hoc analysis of production data of your choice. A workspace provides you with a safe and isolated environment where you can experiment. The input mapping isolation also means that you can work on live production projects without data in the workspace constantly changing — you update them on demand by loading data into the workspace.