SQL Server Integration Services (SSIS) Data Flow

The Data Flow task in SQL Server Integration Services (SSIS) is the core component responsible for extracting, transforming, and loading data. It provides a robust and flexible environment for defining complex data pipelines.

Introduction to Data Flows

A Data Flow task within an SSIS package is where data manipulation happens. It consists of sources (where data originates), transformations (where data is modified), and destinations (where data is loaded). These components are visually connected in the SSIS Designer to represent the flow of data.

Key Data Flow Components

The Data Flow pane in SSIS Designer hosts several types of components:

  • Data Flow Sources: Components that extract data from various sources like databases, flat files, Excel files, XML files, and more. Examples include OLE DB Source, Flat File Source, Excel Source.
  • Transformations: Components that modify, clean, aggregate, or reroute data. This is where the bulk of data manipulation occurs. Common transformations include:
    • Derived Column
    • Data Conversion
    • Aggregate
    • Sort
    • Lookup
    • Conditional Split
    • Script Component (as Transformation)
  • Destinations: Components that load transformed data into a target system. Examples include OLE DB Destination, Flat File Destination, Excel Destination.
  • Event Handlers: While not directly part of the data flow path, event handlers can be triggered by data flow events to perform actions.

Designing Data Flows

Designing an efficient data flow involves several steps:

  1. Define Requirements: Clearly understand the source, the necessary transformations, and the target schema.
  2. Select Components: Choose appropriate source, transformation, and destination components.
  3. Configure Components: Set properties for each component, such as connection managers, table or query for sources, columns to transform, and error handling options.
  4. Connect Components: Draw precedence constraints (blue lines) to link components, defining the data path.
  5. Handle Errors: Configure error outputs for components to redirect rows that fail during processing to a separate path for logging or further analysis.
Tip: Always plan your data flow design carefully. Consider potential data quality issues and how to address them during the transformation phase.

Scripting in Data Flows

For custom logic that cannot be achieved with built-in transformations, the Script Component is invaluable. It allows you to write C# or VB.NET code to:

  • Perform complex data manipulation.
  • Read from or write to custom data sources/destinations.
  • Implement custom validation logic.
  • Create complex error handling routines.

When configuring a Script Component, you specify whether it will act as a Source, Destination, or Transformation, and then write your code within the script editor.

Data Flow Performance Tuning

Optimizing data flow performance is critical for large datasets:

  • Use Appropriate Data Types: Ensure data types are consistent between components to avoid unnecessary conversions.
  • Minimize Data Read: Fetch only the columns you need from the source.
  • Buffer Size: SSIS uses buffers to hold data in memory. Tuning buffer size can impact performance.
  • Parallelism: Design your package to allow for parallel processing where possible.
  • Avoid Row-by-Row Processing: Use set-based operations whenever possible. Transformations like Aggregate, Sort, and Merge Join are optimized for this.
  • Error Output Management: Efficiently handle errors without significantly impacting the main data path.

Example: Derived Column Transformation

Let's say you need to create a new column called FullName by concatenating FirstName and LastName. You would use a Derived Column transformation:


-- Source data
FirstName | LastName
-------------------
John      | Doe
Jane      | Smith

-- Derived Column Transformation
-- Expression: "[FirstName] + " " + [LastName]"
-- New Column Name: FullName

-- Result
FirstName | LastName | FullName
----------------------------------
John      | Doe      | John Doe
Jane      | Smith    | Jane Smith

Example: Conditional Split

To route data based on certain criteria, like separating customers by country:


-- Input Column: Country

-- Output 1: US Customers
-- Condition: Country == "USA"

-- Output 2: Canada Customers
-- Condition: Country == "Canada"

-- Output 3: Other Countries
-- Condition: ELSE