Intermediate Data Transformations

This tutorial delves into more advanced data transformation techniques within SQL Server Integration Services (SSIS). We will explore common scenarios and the SSIS components that facilitate efficient data manipulation.

Key Transformations

SSIS provides a rich set of transformations to clean, reshape, and enrich your data. Here are some of the most commonly used:

1. Derived Column Transformation

The Derived Column transformation allows you to create new columns or modify existing ones by applying expressions. These expressions can involve string manipulation, date functions, arithmetic operations, and conditional logic.

• Use Cases: Concatenating names, calculating ages from dates, creating unique identifiers, categorizing data based on conditions.
• Example Expression: UPPER(FirstName) + " " + UPPER(LastName) to create a full name in uppercase.

2. Conditional Split Transformation

The Conditional Split transformation routes rows of data to different output paths based on specified conditions. This is crucial for data cleansing, error handling, and routing data to different destinations.

• You define a set of conditions, and rows that meet a condition are directed to the corresponding output.
• An "default" output is available for rows that do not meet any defined conditions.

3. Aggregate Transformation

The Aggregate transformation performs aggregate calculations on input data, such as SUM, COUNT, MIN, MAX, and AVG. It typically requires grouping data based on one or more columns.

• Use Case: Calculating total sales per region, counting the number of orders per customer.

-- Example Scenario: Calculate total sales per country
-- Input Data:
-- Country, Sales
-- USA, 100
-- Canada, 150
-- USA, 200
-- Canada, 50

-- Using Aggregate Transformation (GROUP BY Country, SUM(Sales))

-- Output Data:
-- Country, TotalSales
-- USA, 300
-- Canada, 200

4. Look Up Transformation

The Look Up transformation is used to join data from your data flow with data from a reference dataset (e.g., a dimension table in a data warehouse). It allows you to retrieve related information or validate data.

• Use Case: Replacing product IDs with product names, enriching customer data with geographical information.
• Can be configured to handle cache modes (full, partial, no cache) for performance optimization.

5. Sort Transformation

The Sort transformation sorts input data based on specified columns and sort orders (ascending or descending). It's often a prerequisite for other transformations like Aggregate or Merge Join.

• Note: Sorting can be memory-intensive, especially for large datasets.

Data Cleansing Patterns

Transformations are fundamental to data cleansing. Consider these common patterns:

The Conditional Split can be used to isolate records with invalid data (e.g., null values in required fields, values outside expected ranges) and route them to an error handling table or log them.

Performance Considerations

When working with complex transformations, especially on large datasets, performance is key:

• Utilize caching options effectively in transformations like Look Up.
• Perform transformations as late as possible in the data flow to minimize processing on intermediate datasets.
• Consider using more efficient transformations or custom script components when built-in transformations don't meet specific performance needs.

Next Steps

In the next tutorial, we will explore advanced debugging techniques to troubleshoot common issues in your SSIS packages.