Incremental Data Load using Lookup Transformational Tool in SSIS

INTRODUCTION

Implementing incremental data loads in SQL Server using SSIS is a powerful strategy to efficiently manage and synchronize data between systems. In this task, you’ll learn how to design an SSIS package that identifies and processes only new or updated records from a source table, minimizing redundant data movement and optimizing performance. By leveraging the Lookup Transformation to compare source records against the destination table and employing the Conditional Split Transformation to segregate new records from those that require updates, you’ll create a robust, maintainable ETL process. This approach not only ensures data integrity but also reduces processing time, making it ideal for high-volume, dynamic data environments.

Incremental data load is a technique used in ETL (Extract, Transform, Load) processes to efficiently transfer only new or modified records from a source system to a destination database, rather than reloading the entire dataset. This approach enhances performance, reduces processing time, and minimizes the impact on system resources. In SSIS (SQL Server Integration Services), Lookup Transformation plays a crucial role in this process by comparing incoming source records against existing destination data to determine whether a record already exists. Combined with the Conditional Split Transformation, which allows the data to be categorized based on conditions (such as detecting new or changed records), these tools enable an optimized, structured, and automated incremental data loading process. This ensures that only relevant updates are applied, keeping the data warehouse or operational database in sync with minimal overhead.

PREREQUISITES

Before proceeding, it is essential to have the following on your system:

• Microsoft Visual Studio 2019
• KingswaySoft
• Integration Services
• SQL Server
• SQL Server Management Studio
• Microsoft Dynamics 365 CRM Environment

GETTING STARTED

The SSIS package will be created that will fetch the Dynamics 365 Entity as a source and would update the SQL Server database table using Conditional Split and Lookup Transformation tools.

• The Lookup Transformation tool will map the source and destination table columns together
• If the Match Output exists, the Conditional Split will identify the column that is not the same

We are creating a new entity in Dynamics 365 named Staff with the fields as Name (Single Line of Text), Department (Single Line of Text) and Last Modified (DateTime).

Similarly, we have created a new database table Employee with the same columns as illustrated in the source.

Now that the source and destination are configured successfully, it’s time to create a new Integration Service project using Visual Studio 2019. Your SSIS data flow will have the following flow:

Configure the source as Dynamics 365 CRM Source and create the connection with your environment. Make sure that all the columns from the source entity are fetched correctly.

Then, drag the Lookup Transformation tool from the toolbox and configure it accordingly. Create a database destination connection and configure it accordingly.

Navigate to the Columns tab and map the input columns correctly with the available lookup columns (retrieved from the destination table)

Now, drag the Conditional Split transformation tool and connect it with the Match Output flow of the Lookup. Add two conditions in the conditional split (as illustrated in the screenshot)

Now, save the package and run it accordingly. The result output would look like this:

Running the package automatically modified the destination database as per the conditions:

CONCLUSION

Implementing an incremental data load using SSIS with Lookup and Conditional Split transformations ensures efficient data processing by transferring only new or updated records. This approach minimizes redundancy, optimizes system performance, and reduces the load on both source and destination databases. By leveraging the Lookup Transformation to compare existing records and the Conditional Split Transformation to categorize data for insertion or updates, organizations can maintain accurate and up-to-date information with minimal processing time. This method is particularly beneficial for handling large datasets, ensuring data integrity while enhancing the overall efficiency of ETL workflows.

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