No Clocks

A data workflow is the series of steps that turn raw data into something meaningful — think downloading, cleaning, analyzing and visualizing. You might already do this in R with a mix of scripts and notebooks. Some steps in your data workflow may also be manual and require no coding, such as data processing in Excel or uploading model output data to OneDrive. A data pipeline, on the other hand, is an automated version of that workflow. It ensures that every step happens in order, only the necessary steps are rerun when data changes, and guarantees the results are reproducible every time. A well-structured pipeline ensures that anyone revisiting the analysis — including your future self — can rerun, verify and build on the work without extra effort or missing pieces.

The following illustrates a foreign key constraint syntax: [CONSTRAINT fk_name] FOREIGN KEY(fk_columns) REFERENCES parent_table(parent_key_columns) [ON DELETE delete_action] [ON UPDATE update_action]

In this syntax: First, specify the name for the foreign key constraint after the CONSTRAINT keyword. The CONSTRAINT clause is optional. If you omit it, PostgreSQL will assign an auto-generated name. Second, specify one or more foreign key columns in parentheses after the FOREIGN KEY keywords. Third, specify the parent table and parent key columns referenced by the foreign key columns in the REFERENCES clause. Finally, specify the desired delete and update actions in the ON DELETE and ON UPDATE clauses.

Since the primary key is rarely updated, the ON UPDATE action is infrequently used in practice. We’ll focus on the ON DELETE action.

PostgreSQL supports the following actions: SET NULL SET DEFAULT RESTRICT NO ACTION CASCADE

To copy a table completely, including both table structure and data, you use the following statement: CREATE TABLE new_table AS TABLE existing_table;