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In this blog post, we will discuss a critical aspect of ETL (Extract, Transform, Load) process: the incremental load logic in T-SQL. Incremental load refers to the process of updating a data warehouse with new data from operational databases. The goal is to keep the data warehouse up-to-date without the need to perform a complete reload of the entire database. Incremental load is essential in maintaining the performance and efficiency of a data warehouse, especially when dealing with large volumes of data.

The first step in building an incremental load logic is to identify the new records that need to be loaded into the data warehouse. This can be achieved using a ‘last updated timestamp’ field in the source table, which gets updated every time a record is modified. Here is a T-SQL example on SQL Server 2012/2014:

SELECT * FROM SourceTable WHERE LastUpdated > (SELECT MAX(LastUpdated) FROM DestinationTable)

The above T-SQL code will select all the records from the source table that have a 'LastUpdated' timestamp greater than the maximum 'LastUpdated' timestamp in the destination table. However, this approach assumes that the ‘LastUpdated’ field is properly maintained in the source table.

Now let's see how we can build incremental load logic in MySQL 5.6. We can use the same approach as we did in T-SQL, with some minor syntactical differences:

SELECT * FROM SourceTable WHERE LastUpdated > (SELECT MAX(LastUpdated) FROM DestinationTable);

The next step in the incremental load process is to merge the new data with the existing data in the data warehouse. This can be accomplished using the MERGE statement in T-SQL. The MERGE statement matches the source with the target based on a specified condition, and then allows you to perform different actions like insert, update or delete based on whether the match was found or not.

Let's now shift our focus to DB2 10.5. In DB2, the equivalent of a MERGE statement is the MERGE INTO statement. Here's how you can use it to implement incremental load:

MERGE INTO DestinationTable USING (SELECT * FROM SourceTable WHERE LastUpdated > (SELECT MAX(LastUpdated) FROM DestinationTable)) AS src
ON DestinationTable.ID = src.ID
WHEN MATCHED THEN UPDATE SET DestinationTable.field1 = src.field1, DestinationTable.field2 = src.field2
WHEN NOT MATCHED THEN INSERT (ID, field1, field2) VALUES (src.ID, src.field1, src.field2);

Finally, let's discuss Azure SQL. Azure SQL is a fully-managed cloud service that is compatible with SQL Server 2012 and 2014. Therefore, the T-SQL code used earlier for SQL Server 2012/2014 can be used here as well. It's worth mentioning that Azure SQL includes a feature called Incremental Service Updates, which delivers regular updates and improvements to the platform without downtime or impact on performance.

Building an efficient incremental load logic is crucial for maintaining the performance and efficiency of your data warehouse. It reduces the load on your operational databases and the network, and ensures that your data warehouse is always up-to-date. We hope this blog post helps you in implementing incremental load logic using T-SQL in various platforms.