A data analyst needs to update a large customer data warehouse with only the records that have been added or changed since the last update. Which data integration approach should the analyst use to accomplish this efficiently?
A delta load, also known as an incremental load, is the process of loading only the new or changed data since the last time the process was run. This is much more efficient than a full load, which reloads the entire dataset. A snapshot load captures the state of the data at a single point in time, and data blending involves combining data from different sources.
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What is the difference between delta load and full load?
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How is a delta load typically implemented in a data warehouse?
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When would a snapshot load be a better choice than a delta load?