Incremental Load in Informatica: Step-by-Step Guide with Example

In today’s data-driven world, organizations must process large volumes of data efficiently. Instead of reloading entire datasets daily, modern ETL strategies focus on incremental loading in Informatica to optimize performance and minimize resource usage.

This blog provides a complete walkthrough on what is incremental load in Informatica, how it works, and how to implement it using Informatica connectors.

What is Incremental Load in Informatica?

Incremental load in Informatica refers to the process of loading only new or changed data from source systems into the target data warehouse, rather than performing a full data reload. This method saves time, reduces load on systems, and increases overall efficiency.

Incremental loading is especially valuable for high-volume systems where daily full loads are resource-intensive and unnecessary.

Benefits of Incremental Loading in Informatica

• Faster Processing: Loads only delta records, significantly reducing ETL execution time.
• Efficient Resource Usage: Less CPU, memory, and bandwidth usage.
• Accurate Data Updates: Ensures the target always reflects the latest data changes.
• Scalable Approach: Ideal for growing datasets and enterprise-level data operations.

How Incremental Load Works in Informatica

Incremental loading in Informatica typically involves:

1. Identifying New or Changed Records: Using timestamps, unique keys, or flags.
2. Capturing Changes: Via source filters or Change Data Capture (CDC).
3. Loading Data: Inserting or updating only the modified rows in the target.

Step-by-Step Guide: Implementing Incremental Load in Informatica

Here’s a simplified example using Informatica connectors and a timestamp column (e.g., last_updated_date).

Step 1: Design the Source and Target

• Source: Table with a last_updated_date column
• Target: Data warehouse table where data will be updated

Step 2: Create a Parameter for Last Load Time

• Use a mapping parameter (e.g., $$LastLoadDate) to track the last successful ETL run

Step 3: Filter the Source Data

• Apply a filter in the source qualifier:
  last_updated_date > $$LastLoadDate

Step 4: Use Informatica Connectors

• Use certified Informatica connectors (e.g., for Google Sheets, BigQuery, Snowflake) to fetch only updated rows based on the filter

Step 5: Update the Target Table

• Use the Update Strategy transformation to update or insert rows as needed

Step 6: Refresh Last Load Date

• After successful run, update $$LastLoadDate to the current timestamp

Real-Life Example: Google Sheets to Snowflake

Let’s say your marketing team maintains campaign data in Google Sheets. You want to sync this data daily into Snowflake using Informatica connectors.

• Source: Google Sheets with a modified_date column
• Target: Snowflake table
• Connector: Infometry’s certified Google Sheets Connector

Incremental Load Logic:

• Filter rows where modified_date > $$LastLoadDate
• Use Update Strategy to update/insert into Snowflake
• Update $$LastLoadDate post-successful load

Final Thoughts

Understanding what is incremental load in Informatica and applying it effectively is essential for building modern, scalable ETL pipelines. Using Informatica connectors, especially certified options from providers like Infometry, helps you simplify implementation and boost performance across cloud and on-premise systems.

Looking to implement incremental loads using Informatica connectors? Contact Infometry to explore our 22+ prebuilt certified connectors for Google, HubSpot, Adaptive Insights, and more.