Cat On A Spreadsheet

Moving data from Excel into SQL Server is one of the most common enterprise-grade VBA automation tasks — and it is also one of the most error-sensitive. Users frequently paste or import extremely large transactional datasets into Excel, run a macro, and expect all of those rows to be reliably loaded into SQL Server. Unfortunately, most of the code floating around online is either slow (because it inserts rows one row at a time with a new SQL call each time), or dangerously constructed (because it concatenates raw SQL strings and exposes you to SQL injection), or worse — both.

A robust pattern needs to satisfy three requirements simultaneously: the inserts must be secure, the inserts must be fast, and the inserts must be observable. In this post we implement a design that satisfies all three. We will read from an Excel Table (ListObject) named tblDataLoad, and insert its rows into a SQL Server table called dbo.ImportData. We will use a parameterized command structure so that SQL injection is impossible and types remain stable. We will execute inserts in chunked batches wrapped inside database transactions so that performance remains reasonable even with tens of thousands of rows. Finally, we will integrate with the previous error logging and email notification module so that if anything goes wrong, the failure is logged to a timestamped log file and the log is emailed to VBAdmin@company.com automatically.

The hybrid parameterized and chunked approach is ideal for this kind of controlled, medium-to-large volume load scenario. Each chunk commits its own transaction, so if a single chunk encounters a failure, the damage is isolated. Smaller chunk sizes improve reliability and reduce the cost of rollbacks, while larger chunk sizes improve throughput at the expense of resource load. Reasonable chunk sizes are usually between 200 and 2000 rows depending on environment, server and network IO characteristics, and transaction log settings.

The code below demonstrates the full pattern. The mapping between Excel column headers and SQL column names is declared explicitly, because relying on implicit column ordering is too fragile and cannot safely be maintained long-term. The macro reuses a prepared parameterized command object so that parameter creation costs are not repeated unnecessarily. And when the macro completes successfully with no errors, the success message returns immediately. If there were any errors during any chunk, the ErrorHandler module handles the persistence of those errors, creates a timestamped log file, and emails the contents automatically.

When running the above code for the first time, you will probably want to start with a small chunk size to prove the pipeline is stable, then gradually tune up. Chunk sizes determine the tradeoff point between IO throughput and risk concentration. Large chunks improve raw speed, but if one record in the chunk is malformed the entire chunk rolls back. Very small chunks make error isolation trivial but can destroy performance. Over time you will find the optimal balance for your environment, and that value will often differ between departments or workloads because network constraints are rarely uniform across an organization.

The most powerful aspect of this pattern is that each layer is independently improvable. The connection layer can be upgraded to use ADAL / modern auth without changing the rest. The mapping structure can be expanded automatically from named ranges or metadata sources. The logging module can be extended to automatically dump SQL Server constraint errors into a dedicated diagnostic table for analysis. And the email notification pipeline means that when failures occur, the development team knows immediately, rather than waiting weeks until a user finally mentions something “looked wrong”.

This approach is the difference between VBA code that feels temporary and code that feels like an actual controlled system integration engine.

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Cat On A Spreadsheet