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You can configure your data diffs with the following parameters and options:

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Dataset

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Data connection

Pick your data connection(s).

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Diff type

Choose how you want to compare your data:
  • Table: Select this to compare data directly from database tables
  • Query: Use this to compare results from specific SQL queries
Datafold can also diff views, materialized views, and dynamic tables (Snowflake-only) across both options too.

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Dataset

Choose the dataset you want to compare, Main and Test. This can be a table or a view in your relational database.

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Time travel point

If your database supports time travel, like Snowflake, you can query data at a specified timestamp. This is useful for tracking changes over time, conducting audits, or correcting mistakes from accidental data modifications. You can adjust the database’s session parameters as needed for your query. Supported time travel expressions: Timestamp examples:
  • 2024-01-01
  • 2024-01-01 10:04:23
  • 2024-01-01 10:04:23-09:00
  • 2024-07-16T10:04:23+05:00
Negative offset examples (in seconds):
  • 130
  • 3600

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Filter

Insert your filter clause after the WHERE keyword to refine your dataset. For example: created_at >'2000-01-01 will only include data created after January 1, 2000.

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Column remapping

When columns are the same data type but are named differently, column remapping allows you to align and compare them. This is useful when datasets have semantically identical columns with different names, such as userID and user_id. Datafold will surface any differences under the column name used in the Main dataset.

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General parameters

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Primary key

The primary key is one or more columns used to uniquely identify a row in the dataset during diffing. The primary key (or keys) does not need to be formally defined in the database or elsewhere as it is used for unique row identification during diffing. Multiple columns support compound primary key definitions.

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Time-series dimension column

If a time-series dimension is selected, this produces a Timeline plot of diff results over time to identify any time-based patterns. This is useful for identifying trends or anomalies when a given column does not match between tables in a certain date range. By selecting a time-based column, you can visualize differences and patterns across time, measured as column match rates.