What would a stored procedure look like that reads data from a delta lake table, compares it to existing historic data to find any new, unique items that need inserting into the historic data? Use sample investment "price" tables that contain date, symbol and closing_price. The unique identifier is date-symbol combination.

                    
                        CREATE PROCEDURE sp_UpdateHistoricPrices (
                            @deltaLakeTable VARCHAR(255),
                            @historicTable VARCHAR(255)
                        )
                        AS
                        BEGIN
                            -- Define temporary tables for staging new data
                            DECLARE @newPrices TABLE (
                                date DATE NOT NULL,
                                symbol VARCHAR(10) NOT NULL,
                                closing_price DECIMAL(5,2) NOT NULL
                            );

                            -- Read new data from Delta Lake table using external table
                            CREATE EXTERNAL TABLE #newPrices (
                                date DATE,
                                symbol VARCHAR(10),
                                closing_price DECIMAL(5,2)
                            )
                            WITH (
                                LOCATION = @deltaLakeTable,
                                FORMAT = 'DELTA'
                            );

                            INSERT INTO @newPrices
                            SELECT date, symbol, closing_price
                            FROM #newPrices;

                            -- Drop temporary external table
                            DROP EXTERNAL TABLE #newPrices;

                            -- Identify new data that's not in the historic table
                            INSERT INTO @historicTable (date, symbol, closing_price)
                            SELECT np.date, np.symbol, np.closing_price
                            FROM @newPrices np
                            LEFT JOIN @historicTable hp
                            ON np.date = hp.date AND np.symbol = hp.symbol
                            WHERE hp.date IS NULL;

                            -- Alternatively, you can update existing records if needed
                            -- UPDATE @historicTable
                            -- SET closing_price = np.closing_price
                            -- FROM @newPrices np
                            -- INNER JOIN @historicTable hp
                            -- ON np.date = hp.date AND np.symbol = hp.symbol;

                            -- Log the number of new records inserted
                            PRINT 'Inserted ' + CAST(@@ROWCOUNT AS VARCHAR(10)) + ' new price records.';

                        END
                        GO
                    
                

Explanation:

• Parameters: The procedure takes two parameters:
  • @deltaLakeTable: The path to the Delta Lake table containing new price data.
  • @historicTable: The name of the existing historic price table in Synapse Analytics.
• Temporary Tables: We define a temporary table @newPrices to store the read data from the Delta Lake table.
• Read Data from Delta Lake: We create an external table #newPrices pointing to the Delta Lake location and format. Then, we insert the data into the temporary table @newPrices.
• Identify New Data: We perform a LEFT JOIN between @newPrices and @historicTable. If a date-symbol combination doesn't exist in the historic table, it's considered new and inserted into the historic table.
• Optionally Update Existing Records: You can modify the script to update existing records instead of inserting new ones. Modify the UPDATE statement to achieve this.
• Log Insertion Count: The script prints the number of new records inserted into the historic table.

Note:

• This script assumes your price tables have date, symbol, and closing_price columns. Adapt the column names and table structures if yours differ.
• Remember to grant appropriate permissions for the Synapse Analytics service to access the Delta Lake storage location.
• Consider using try-catch blocks and error handling for robust operation in a production environment.

This provides a basic framework for comparing and inserting new data from a Delta Lake table into a historic price table using T-SQL stored procedures in Azure Synapse Analytics. Adapt and extend it based on your specific needs and data schema.