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Answer: Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
Option D is correct because manually setting data types in Databricks ensures that the data conforms to the expected types, providing greater assurance of data quality enforcement. This is particularly important in a production environment where data accuracy and consistency are critical. Options A, B, and C are incorrect for the following reasons: - A: While Tungsten encoding is optimized for processing, it is not specifically optimized for storing string data, and storing JSON as strings is not the most efficient method for querying nested structures. - B: Delta Lake does use Parquet for storage, but schema evolution involves more than just modifying file footer information; it requires writing new data files with the updated schema. - C: Schema inference does not guarantee that the inferred types will match the data types required by downstream systems, which can lead to potential mismatches and errors.
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A junior data engineer is implementing logic for a Lakehouse table called silver_device_recordings. The source data consists of 100 unique fields in a deeply nested JSON structure.
The silver_device_recordings table will serve downstream applications, including multiple production monitoring dashboards and a production model. Currently, 45 out of the 100 fields are utilized in at least one of these applications.
Given the highly nested schema and large number of fields, the data engineer is evaluating the optimal approach for schema declaration.
Which of the following statements about Delta Lake and Databricks provides relevant considerations for their decision-making process?
A
The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
B
Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
C
Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
D
Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.