Upsert in Databricks Using the MERGE INTO Command
Syntax:
SQL
MERGE INTO target_table AS target
USING source_table AS source
ON target.id = source.id
WHEN MATCHED THEN
UPDATE SET target.column1 = source.column1,
target.column2 = source.column2
WHEN NOT MATCHED THEN
INSERT (id, column1, column2)
VALUES (source.id, source.column1, source.column2)
WHEN NOT MATCHED BY SOURCE THEN
DELETE;
Advantages:
- Efficiency:
Streamline complex data operations with a single command.
- Flexibility:
Handle different scenarios with MATCHED and NOT MATCHED clauses.
- Consistency:
Ensure data integrity and synchronization between source and target tables.
- Scalability:
Perform operations on large datasets seamlessly with Databricks’ optimized performance.
Incorporating `MERGE INTO` into your data workflows can significantly enhance your data management capabilities.
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Databricks DataEngineering SQL DataManagement BigData DataIntegration
USING source_table AS source
ON target.id = source.id
WHEN MATCHED THEN
UPDATE SET target.column1 = source.column1,
target.column2 = source.column2
WHEN NOT MATCHED THEN
INSERT (id, column1, column2)
VALUES (source.id, source.column1, source.column2)
WHEN NOT MATCHED BY SOURCE THEN
DELETE;
Advantages:
- Efficiency:
Streamline complex data operations with a single command.
- Flexibility:
Handle different scenarios with MATCHED and NOT MATCHED clauses.
- Consistency:
Ensure data integrity and synchronization between source and target tables.
- Scalability:
Perform operations on large datasets seamlessly with Databricks’ optimized performance.
Incorporating `MERGE INTO` into your data workflows can significantly enhance your data management capabilities.
If you find this useful, please repost! 🌟
I
follow Satish Mandale for more such contents
Databricks DataEngineering SQL DataManagement BigData DataIntegration
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