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Use the guidelines below to prepare your CSV and Parquet files for synthetization.
They’ll need to be formatted as a single subject table or a subject table-linked table dataset.

We recommend synthesizing datasets in Parquet format.

If you want to synthesize CSV files, please read the CSV file requirements to learn how they need to be formatted.


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Single subject table
  • We recommend that subject tables have more than 5000 subjects.
    There is no minimum size. However, the more subjects there are available, the better the training algorithm can generalize their features, which results in a decreased privacy risk.

  • The maximum number of subjects and columns is determined by your license.

  • Each subject must refer to a distinct real world entity.

  • Each row describes one subject.

  • Each row can be treated independently.
    The rows' order carries no information, and the contents of one row
    do not affect other rows.

  • Please ensure that the column names do not contain
    any privacy-sensitive information.

    Avoid column names such as vendor_a_purchases, vendor_b_purchases, etc.. Not only would vendor names already appear in the metadata, but they could also slip through rare category protection (e.g., there’s a vendor_a column, but this vendor only appeared five times in the whole dataset). You can solve this problem by simply having a vendor column with the vendor names in it.


A subject is an entity or individual whose privacy you are going to protect. A subject table, therefore, contains records that describe these subjects.

Each row in a subject table describes the profile of a unique subject. They contain fields that tell something about them, such as their name, gender, height, place of residence, or income.

In practice, two or more real-world entities may have identical features when they’re described as subjects in the subject table. Conversely, a customer can make several online purchases using different accounts or without logging in to their account. This results in a subject table that contains multiple records with different identifiers for the same person.

MOSTLY AI delivers the most accurate results if the subject table reflects the real world as closely as possible. If real-world entities share identical properties, then this should be left as such. But if multiple records contain the same contact details, it’s plausible that it’s the same person and could be considered for merging.

Below you’ll find an example of a subject table. You can use it as a guideline to create your own.

example subject table



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Subject table-linked table dataset
  • This structure is ideal for processing lists, sequences, or time-series data.

  • It consists of two tables, a subject table that satisfies the requirements stated above, and a linked table.

  • Each record in the subject table must have a unique ID number (primary key).

  • Each record in the linked table must contain the ID of the subject that it’s linked to.

  • Avoid having a numbers of records per subject that is larger than what you would consume in your downstream application.


MOSTLY AI can process lists, sequences, or time-series data when they’re formatted as subject table-linked table datasets. Here you can think of shopping lists, insurance claims, patient health records, or time-series data, such as online shopping journeys, purchase histories, or financial transactions.

Linked tables contain events, and MOSTLY AI processes these as properties of the subjects in your subject table. Therefore, they cannot exist without subjects, but subjects can have zero events. This relationship guarantees the subjects` privacy during synthesization, which is why these types of data need to be formatted into a subject table and a separate linked table.

The image below shows the columns that these tables must have to make this relationship. Each record in your linked table must have a field that specifies to which subject it belongs.

example subject table

If you’re working with CSV or Parquet files, MOSTLY AI automatically links two tables if the subject table contains a column called id and the second table contains _id in the name of a column (for instance, players_id).

Below you’ll find an example of a basic customer journey dataset with two subjects. Alice Doe made a purchase after visiting the store twice, and Bob Joe was flagged as a churned customer after he no longer showed up for five days.

Subject Table
id        firstName     lastName
1         Alice         Doe
2         Bob           Joe
Linked table
users_id  event_time  event_type
1         2020-04-01  visit
1         2020-04-03  visit
1         2020-04-05  purchase
2         2020-03-13  visit
2         2020-03-18  churn
If you have a single table with event data, please split it into a subject and linked table accordingly.


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General recommendations

We recommend splitting the contents of your fields by their features whenever possible.

For instance, the column street address may contain addresses that all have the same form — 123 example street. In this case, you can split them into the street name and number. MOSTLY AI can then treat street names as a text column and the numbers as numerical variables, which results in improved accuracy of the generated synthetic data.