Understanding How SSIS Excel Data Source Task Determines When to Stop Reading Rows

Discover how the SSIS Excel Data Source Task identifies the end of data in Excel files, and learn effective strategies for managing blank rows.
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Introduction

If you're working with SQL Server Integration Services (SSIS) and trying to transfer data from Excel to a database, you might encounter a puzzling situation: your SSIS Excel Data Source Task keeps reading beyond the last row containing data — even when there are blank rows present. For example, you may have an Excel file with 500 rows, yet the task continues to read until row 600. This raises the question: how does the SSIS Excel Data Source Task know when to stop?

In this guide, we’ll explore the underlying mechanics of how SSIS identifies the endpoint of data in Excel files, as well as some practical solutions for managing blank rows.

How Does SSIS Determine the Endpoint?

The exact logic behind how SSIS determines where the data ends is often not well-documented. However, here’s a simplified understanding of the process:

Excel Behavior: When you open an Excel spreadsheet and press Ctrl + End, Excel takes you to the last cell that contains data or formatting. This could lead you to the impression that there is more data than there actually is. In our example, while there are 500 rows of data, there could be remnants, formatting, or invisible markers extending the range to 600.

Resetting the End of Data Marker:

If you notice that the Excel Source is reading beyond the expected rows, you can adjust this by highlighting the extra empty rows (e.g., from 500 to 600) and deleting them. This action updates the end-of-data marker back to where it should be.

Handling Extra Blank Rows in SSIS

To tackle the issue of blank rows being read, you can implement the following steps within your SSIS package:

Step 1: Define What Constitutes a Valid Row

To begin, it’s essential to identify the criteria for a valid row of data. This could include:

An ID column that is populated

A Social Security Number (SSN)

A Sales Date

Essentially, any column that you expect to have data can be used to define what a valid row looks like.

Step 2: Add a Derived Column

Create a new derived column called IsInvalidRow, using a Boolean data type (DT_BOOL). The expression you can apply might look like this:

ISNULL(MyCol)

ISNULL(MyCol) || MyCol==""

Replace MyCol with the column name you are assessing to determine whether the row is valid.

Step 3: Implement a Conditional Split

Use a Conditional Split transformation to route rows based on the IsInvalidRow column. The condition might simply be:

IsInvalidRow == TRUE

Any rows identified as invalid can be discarded (routed to nowhere), while valid rows will continue through your data flow.

Why Add an Extra Column?

You might wonder why it’s beneficial to add a column for invalid rows. There are several reasons:

Debugging: This column allows you to visualize which rows are failing validation. You'll gain insights into the evaluation process of your expressions.

Maintainability: In complex environments, separating logic into smaller components makes it easier to modify and troubleshoot in the future.

Conclusion

Understanding how the SSIS Excel Data Source Task recognizes the end of data and how you can manage blank rows is key to ensuring accurate data transfer. By defining valid rows, employing a derived column, and utilizing conditional splits, you can enhance the reliability of your data flow processes in SSIS.

Implementing these strategies not only aids in cleaner data extraction but also significantly improves your SSIS package's maintainability. If you encounter similar challenges, these techniques will help you effectively handle them.