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Efficiency: Incremental loading reduces the amount of data transferred between systems, making the data integration process more efficient. This is particularly important when dealing with large datasets.
Faster Updates: Since only the changed or new records are processed, incremental loading typically results in faster update times compared to reloading the entire dataset.
Reduced Resource Usage: Incremental loading minimizes the impact on system resources, such as network bandwidth, storage, and processing power.
Incremental loading is widely used in data warehousing, business intelligence, and data integration scenarios to keep data up-to-date with minimal impact on resources. The implementation details may vary depending on the tools, databases, and platforms involved in the data integration process.

Incremental loading involves updating a dataset with only the new or changed data since the last load, rather than reloading the entire dataset. The specific approach you use depends on the characteristics of your data and the tools at your disposal.

Here Are Some Common Strategies For Incremental Loading:
Timestamp Or Date-Based Incremental Loading:
Include a timestamp or date column in your data to track when records were last modified.
During each update, retrieve records with timestamps or dates greater than the maximum timestamp or date from the previous load.
SELECT *
FROM your_table
WHERE modification_timestamp > last_load_timestamp;
Change Data Capture (CDC):
Implement a mechanism to capture changes in the source data. This could involve using triggers, database logs, or tracking columns
Identify and load only the changed records during each update.
SELECT *
FROM your_table
WHERE is_modified = true;
Flag-Based Incremental Loading:
Introduce a flag column in your data to mark records that have been added or modified.
During each update, process only the records with specific flag values.
SELECT *
FROM your_table
WHERE incremental_flag = 'Y';
Log -Based Incr