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How To Implement Incremental Loading In Snowflake Using Stream And Merge?
Incremental loading refers to the process of selectively loading and updating only the new or changed data since the last load, rather than reloading the entire dataset. This approach is commonly used in data integration scenarios where you have a large dataset, and you want to minimize the amount of data transferred and processed during each data update.

The main advantages of incremental loading include:

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.