Data Solutions: Informatica Cloud - Incremental Load (Data Ingestion)

Today we will learn how to create incremental data load (ingestion) in Informatica cloud data integration

Select source connection (point to the newly created source connection in the Informatica administration)
Select source type as query
In the query window, enter the query to get the maximum timestamp from your target table
The timestamp should come from the target database table here. This max timestamp will act as a filter to fetch latest records from the source

Write query to fetch records from the actual source table
use a where condition (where timestamp > ?<timestamp_field>? )
In the advance section, set "max output row count" = 0. This will fetch all the latest rows from the source (otherwise it will fetch only 500 rows set by default).
Go to Output Fields and create all the fields that you have listed in your query

Select the target table where you want to insert the records
Do the field mapping
Additional: In the advance section of the target, call your stored procedure (that will run some business logic after inserting all the records) in the Post SQL

Mapping is complete
Run the Mapping via Informatica job. You will see all the records getting inserted
Run the job again to test incremental records
After running the mapping again, there will be no records inserted in the target. this is because, in the source there are not new records present at the moment.

Anonymous31 August 2026 at 11:58
Incremental data loading is a useful approach for handling large datasets because it avoids processing the entire source repeatedly. The use of timestamps, SQL queries, filtering, and source-to-target mappings provides a practical way to identify only newly added records and move them into the target system. This makes the overall ingestion workflow more efficient and easier to manage.

For learners working with databases and ETL workflows, understanding how records are selected, transformed, filtered, and loaded is an important part of Data Analytics Online Course topics. These concepts are particularly useful when dealing with recurring data pipelines where new information needs to be incorporated without unnecessarily reprocessing existing records.
Anonymous31 August 2026 at 11:58
The final validation step is also valuable because running the mapping again demonstrates whether the incremental logic correctly detects only new records. Although the article is primarily about ingestion rather than charts or dashboards, Data Visualization Online Course concepts can complement analytics workflows when the loaded data is eventually prepared for reporting and visual analysis.