1. Update Command in Databricks SQL: The UPDATE statement in Databricks SQL allows you to modify column values for rows that match a specified predicate.
Here’s the syntax for the UPDATE statement:
UPDATE table_name [table_alias]
SET { { column_name | field_name } = [ expr | DEFAULT } [, ...] }
[WHERE clause]
table_name: Identifies the table to be updated (must be a Delta Lake table).table_alias: Optionally define an alias for the table.column_name or field_name: References to columns or fields within a column (for STRUCT types).expr: An arbitrary expression (you can reference table columns to represent the row state prior to the update).DEFAULT: Applies when a DEFAULT expression is defined for the column.
2. Your SQL Statement: Let’s break down your specific SQL statement:
UPDATE dbo.td_address
SET
level_col = z01_vm_address_01_t1.level_col,
dim_char_ky_fld = z01_vm_address_01_t1.dim_char_ky_fld,
ss_address_key = z01_vm_address_01_t1.ss_address_key,
drop_point_id = z01_vm_address_01_t1.drop_point_id,
address_line_1 = z01_vm_address_01_t1.address_line_1,
address_line_2 = z01_vm_address_01_t1.address_line_2,
address_line_3 = z01_vm_address_01_t1.address_line_3,
address_line_4 = z01_vm_address_01_t1.address_line_4,
...removed,
audit_timestamp_01 = z01_vm_address_01_t1.audit_timestamp_01
FROM dbo.z01_vm_address_01_t1 z01_vm_address_01_t1
WHERE 1=1
AND dbo.td_address.level_col = z01_vm_address_01_t1.level_col
AND dbo.td_address.dim_char_ky_fld = z01_vm_address_01_t1.dim_char_ky_fld;
- You’re updating the
dbo.td_address table based on data from the z01_vm_address_01_t1 table.
- The
WHERE clause ensures that only matching rows are updated.
3. Compatibility with Databricks SQL: The UPDATE statement you’ve provided is consistent with Databricks SQL and Delta tables. As long as both tables are Delta Lake tables, this format should work as expected.
Remember to verify the table names, column names, and any other specifics relevant to your environment. If you encounter any issues, feel free to ask for further assistance!
