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Dynamic Table SQL

Once a data source has been scanned at the Schema Analysis tier, users can use the Dynamic Table Generation page to select that datasource and configure the additional properties for each attribute, which is required to generate the SQL statements to extract and flatten that JSON source into Dynamic Tables.

Users will be asked to validate, specify, and/or configure the following:

  • Snowflake data type

  • Precision/Scale

  • Datetime format

These have default values based on inference from the scan, but the user may want to adjust them based on their understanding of the data. Users will also select and configure the attributes which will be carried down to each nested array. This allows for the Dynamic Tables to be represented as relational streams.

Example: You are dealing with a JSON with retail sales information with an object that contains an orders array. You can select the customer_id column from the root level to include in the flattened our orders array. This will allow you to join the two Dynamic Tables based on the customer_id.

Last updated 11 months ago