Structural differences and limitations with MongoDB
Required license: Professional or Enterprise
The data structure for MongoDB is different from that of the relational data connectors.
Instead of schemas, tables, and columns, a MongoDB database consists of collections and fields. A field might be an object that is made up of other fields. In other words, a collection can have a tree structure that is similar to a JSON document.
For workspaces that use MongoDB, the Tonic Structural application has the following differences.
No data science mode
MongoDB is only available for data generation workspaces. You cannot use MongoDB in a data science mode workspace.
Terminology changes
On the following Structural views, the term "collection" replaces the term "table".
References to columns are also replaced:
On Privacy Hub, the protection status panels refer to "fields" instead of "columns".
On the Schema Changes view, the change lists refer to "paths" instead of "columns".
Privacy Hub - Latest Collection Scan
For MongoDB workspaces, Structural must scan each collection to determine the fields and data types within that collection. Until a scan is performed, you cannot configure the collection modes and generators.
For MongoDB workspaces, Privacy Hub includes an additional Latest Collection Scan section that shows the most recent time that a scan was performed on each scanned collection.
For more information, go to Performing scans on collections.
No workspace inheritance
MongoDB workspaces do not support workspace inheritance.
Collection View replaces Database View and Table View
For MongoDB workspaces, there is no Database View or Table View. Instead, MongoDB workspaces have a Collection View.
This view allows you to perform the same functions as Table View, but the display is more like Database View. For more information, go to Using Collection View.
Collection mode (table mode) limitations
Collection mode is the term for table mode in MongoDB workspaces.
MongoDB only supports De-Identify, Truncate, and Preserve Destination modes.
Generator limitations
MongoDB workspaces cannot use the following generators:
AI Synthesizer
Algebraic
Alphanumeric Key
Array Character Scramble
Array JSON Mask
Array Regex Mask
Cross-Table Sum
CSV Mask
Event Timestamps
HTML Mask
JSON Mask
SIN
Timestamp Shift
URL
No upsert
MongoDB workspaces do not support upsert.
No output to container artifacts
For MongoDB workspaces, you cannot write the destination data to container artifacts.
No output to an Ephemeral snapshot
For MongoDB workspaces, you cannot write the destination data to an Ephemeral snapshot.
No post-job scripts
For MongoDB workspaces, there is no option to run post-job scripts after a job.
You can create webhooks that are triggered by data generation jobs.
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