DML (Data Manipulation Language) operations in Snowflake are essential commands for modifying data within tables. Understanding these operations is crucial for the SnowPro Core Certification.
**INSERT**: This operation adds new rows to a table. Snowflake supports various INSERT methods including s…DML (Data Manipulation Language) operations in Snowflake are essential commands for modifying data within tables. Understanding these operations is crucial for the SnowPro Core Certification.
**INSERT**: This operation adds new rows to a table. Snowflake supports various INSERT methods including single-row inserts, multi-row inserts, and INSERT from SELECT statements. You can insert data from staged files using COPY INTO or INSERT with stage references. Example: INSERT INTO employees VALUES (1, 'John', 'Sales');
**UPDATE**: This command modifies existing records in a table based on specified conditions. Snowflake supports standard UPDATE syntax with WHERE clauses to target specific rows. You can update multiple columns simultaneously and use subqueries for complex transformations. Example: UPDATE employees SET department = 'Marketing' WHERE emp_id = 1;
**DELETE**: This operation removes rows from a table based on conditions specified in the WHERE clause. Using DELETE requires Time Travel storage for the retention period. Be cautious as DELETE operations on large tables can consume significant resources. Example: DELETE FROM employees WHERE status = 'inactive';
**MERGE**: This powerful operation combines INSERT, UPDATE, and DELETE into a single statement. MERGE compares source and target tables, then performs different actions based on whether rows match. It follows the WHEN MATCHED and WHEN NOT MATCHED clauses to determine appropriate actions. This is particularly useful for slowly changing dimensions and incremental data loading scenarios. Example: MERGE INTO target USING source ON target.id = source.id WHEN MATCHED THEN UPDATE SET target.value = source.value WHEN NOT MATCHED THEN INSERT (id, value) VALUES (source.id, source.value);
All DML operations in Snowflake are atomic and support transactions. They leverage Snowflake's micro-partition architecture, creating new micro-partitions rather than modifying existing ones, which enables Time Travel and zero-copy cloning capabilities.
DML Operations in Snowflake: INSERT, UPDATE, DELETE, and MERGE
Why DML Operations Are Important
Data Manipulation Language (DML) operations are fundamental to managing data within Snowflake. Understanding these operations is essential for the SnowPro Core certification because they form the backbone of data transformation workflows. DML operations allow you to add new records, modify existing data, remove unwanted entries, and perform complex upsert operations efficiently.
What Are DML Operations?
DML operations in Snowflake include four primary commands:
INSERT - Adds new rows to a table UPDATE - Modifies existing rows in a table DELETE - Removes rows from a table MERGE - Combines INSERT, UPDATE, and DELETE operations in a single statement
How Each Operation Works
INSERT: - Supports single-row and multi-row inserts - Can insert from SELECT statements (INSERT INTO ... SELECT) - Supports INSERT OVERWRITE to replace all existing data - Works with VALUES clause for explicit data insertion
UPDATE: - Modifies column values based on WHERE conditions - Can reference other tables using FROM clause - Supports subqueries in SET clause - All updates within a statement are atomic
DELETE: - Removes rows matching WHERE conditions - Deleting all rows (no WHERE clause) is different from TRUNCATE - Can use USING clause to reference other tables - Deleted data can be recovered using Time Travel
MERGE: - Performs conditional INSERT, UPDATE, or DELETE in one statement - Uses MATCHED and NOT MATCHED clauses - Ideal for slowly changing dimensions (SCD) and upsert scenarios - More efficient than separate DML statements for complex operations
Key Snowflake-Specific Behaviors
- DML operations are transactional and ACID-compliant - Multi-table inserts are supported - Time Travel allows recovery of deleted or modified data - DML on tables with clustering keys may trigger automatic reclustering - Streams can capture DML changes for CDC (Change Data Capture)
Exam Tips: Answering Questions on DML Operations
1. Know the MERGE syntax: Exam questions frequently test understanding of WHEN MATCHED and WHEN NOT MATCHED clauses. Remember that you can have multiple MATCHED conditions.
2. Understand INSERT OVERWRITE: This replaces all data in a table while maintaining table structure and is different from TRUNCATE followed by INSERT.
3. Time Travel recovery: Be aware that DML operations can be undone using Time Travel within the retention period. This is a common exam topic.
4. DELETE vs TRUNCATE: Know that DELETE is logged and supports Time Travel, while TRUNCATE is faster but also supports Time Travel in Snowflake.
5. Multi-table operations: UPDATE and DELETE can reference multiple tables using FROM and USING clauses respectively.
6. Streams and DML: Understand that Streams capture DML changes and are consumed when queried within a DML transaction.
7. Transaction behavior: All DML statements are auto-committed by default unless within an explicit transaction block.
8. Performance considerations: MERGE is typically more efficient than running separate INSERT, UPDATE, and DELETE statements for the same logical operation.
9. Practice scenarios: Focus on upsert patterns using MERGE and understanding when to use each DML operation for optimal performance.