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A data engineering team is developing a data pipeline to process JSON data from a new source. The team notices that some records are being processed incorrectly due to mismatched data types, leading to errors in downstream applications. The team is considering using schema inference to automatically detect and apply the correct data types to the JSON fields. However, they are concerned about the accuracy of schema inference, especially since the JSON data contains a mix of data types and some fields are optional. The team must ensure that the solution is cost-effective, scalable, and minimizes manual intervention. Given this scenario, which of the following best describes the concept of schema inference and its application in resolving the data type mismatch issue? (Choose one option)
A
Schema inference is the process of manually defining the data types for each field in a JSON source to ensure accurate data processing, which requires significant upfront effort but guarantees data type accuracy.
B
Schema inference is a technique that converts JSON data into a relational format before processing, which can introduce additional complexity and latency in the data pipeline.
C
Schema inference automatically detects the data types of a JSON source based on the values of the first few records, which may not always be accurate for the entire dataset but reduces manual effort.
D
Schema inference is the process of analyzing the entire JSON dataset to accurately determine the data types for each field, ensuring high accuracy but at the cost of increased processing time and resources.