Development

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;

When to use temporary tables Isolation of data: Since the temporary tables are session-specific, different sessions or transactions can use the same table name for temporary tables without causing a conflict. This allows you to isolate data for a specific task or session. Intermediate storage: Temporary tables can be useful for storing the intermediate results of a complex query. For example, you can break down a complex query into multiple simple ones and use temporary tables as the intermediate storage for storing the partial results. Transaction scope: Temporary tables can be also useful if you want to store intermediate results within a transaction. In this case, the temporary tables will be visible only to that transaction

In PostgreSQL, a generated column is a special type of column whose values are automatically calculated based on expressions or values from other columns. A generated column is referred to as a computed column in the SQL Server or a virtual column in Oracle .

There are two kinds of generated columns: Stored: A stored generated column is calculated when it is inserted or updated and occupies storage space. Virtual: A virtual generated column is computed when it is read and does not occupy storage space.

A virtual generated column is like a view, whereas a stored generated column is similar to a materialized view. Unlike a material view, PostgreSQL automatically updates data for stored generated columns.

PostgreSQL currently implements only stored generated columns.

In PostgreSQL, a sequence is a database object that allows you to generate a sequence of unique integers. Typically, you use a sequence to generate a unique identifier for a primary key in a table. Additionally, you can use a sequence to generate unique numbers across tables. To create a new sequence, you use the CREATE SEQUENCE statement.

Listing all sequences in a database To list all sequences in the current database, you use the following query: SELECT relname sequence_name FROM pg_class WHERE relkind = 'S';

PostgreSQL version 10 introduced a new constraint GENERATED AS IDENTITY that allows you to automatically assign a unique number to a column.

The GENERATED AS IDENTITY constraint is the SQL standard-conforming variant of the good old SERIAL column.

The following illustrates the syntax of the GENERATED AS IDENTITY constraint: column_name type GENERATED { ALWAYS | BY DEFAULT } AS IDENTITY[ ( sequence_option ) ]

In this syntax: The type can be SMALLINT, INT, or BIGINT. The GENERATED ALWAYS instructs PostgreSQL to always generate a value for the identity column. If you attempt to insert (or update) values into the GENERATED ALWAYS AS IDENTITY column, PostgreSQL will issue an error. The GENERATED BY DEFAULT instructs PostgreSQL to generate a value for the identity column. However, if you supply a value for insert or update, PostgreSQL will use that value to insert into the identity column instead of using the system-generated value.

PostgreSQL allows a table to have more than one identity column. Like the SERIAL, the GENERATED AS IDENTITY constraint also uses the SEQUENCE object internally.

To fix the error, you can use the OVERRIDING SYSTEM VALUE clause as follows: INSERT INTO color (color_id, color_name) OVERRIDING SYSTEM VALUE VALUES(2, 'Green');

Alternatively, you can use GENERATED BY DEFAULT AS IDENTITY instead.

Because the GENERATED AS IDENTITY constraint uses the SEQUENCE object, you can specify the sequence options for the system-generated values.

For example, you can specify the starting value and the increment as follows: DROP TABLE color; CREATE TABLE color ( color_id INT GENERATED BY DEFAULT AS IDENTITY (START WITH 10 INCREMENT BY 10), color_name VARCHAR NOT NULL );

In this example, the system-generated value for the color_id column starts with 10 and the increment value is also 10.