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

Introduction

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

This guide explains:

• What is incremental load in Informatica
• How incremental loading works
• Benefits of incremental load
• Step-by-step implementation using Informatica connectors
• Real-world example: Google Sheets to Snowflake

What is Incremental Load in Informatica?

Incremental load in Informatica refers to the process of loading only new or updated data from source systems into the target warehouse, instead of reloading the entire dataset.

This method:

• Saves processing time
• Reduces system load
• Ensures data freshness with fewer resources
  

It is especially valuable in enterprise-scale ETL pipelines where daily full loads are time-consuming and unnecessary.

Benefits of Incremental Loading in Informatica

1. Faster Processing – Only delta records are loaded, reducing ETL execution time.
2. Efficient Resource Usage – Lowers CPU, memory, and network bandwidth consumption.
3. Accurate Data Updates – Keeps the target system always in sync with the latest changes.
4. Scalable Approach – Works seamlessly as datasets grow in volume and complexity.

How Incremental Load Works in Informatica

The incremental loading process typically involves:

1. Identifying New or Changed Records – Using timestamps, unique keys, or change flags.
2. Capturing Data Changes – With filters or Change Data Capture (CDC).
3. Loading Data Efficiently – Updating only modified rows in the target table.

Step-by-Step: Implementing Incremental Load in Informatica

Here’s how you can build incremental loading using Informatica connectors and a last_updated_date column.

Step 1: Define Source and Target

• Source: Table with last_updated_date field
• Target: Data warehouse table for updated data
  

Step 2: Create a Parameter for Last Load Time

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

Step 3: Filter Source Data

In the Source Qualifier transformation, apply:

last_updated_date > $$LastLoadDate

Step 4: Use Informatica Connectors

Leverage certified connectors (e.g., Google Sheets, Snowflake, BigQuery) to fetch only updated rows.

Step 5: Update Target Table

Use Update Strategy Transformation to perform insert/update logic on modified rows.

Step 6: Refresh Last Load Date

After a successful run, update $$LastLoadDate with the current timestamp.

Real-Life Example: Google Sheets to Snowflake

Imagine your marketing team maintains campaign data in Google Sheets, and you need to sync it daily into Snowflake.

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

Incremental Load Logic:

1. Filter rows where modified_date > $$LastLoadDate
2. Use Update Strategy to insert/update in Snowflake
3. Refresh $$LastLoadDate after load completion

This ensures only the changed campaign data flows to Snowflake—saving time and resources.

Final Thoughts

Mastering incremental load in Informatica is essential for building scalable, high-performance ETL pipelines. By leveraging Informatica connectors—especially certified solutions from providers like Infometry—you can simplify implementation and accelerate performance across both cloud and on-premises systems.

Looking to implement incremental loads in your data ecosystem?

Infometry offers 22+ certified connectors for Google, HubSpot, Adaptive Insights, Snowflake, and more. Contact Infometry to get started.