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You are designing an Azure Data Lake Storage solution that will transform raw JSON files for use in an analytical workload. You need to recommend a format for the transformed files. The solution must meet the following requirements: * Contain information about the data types of each column in the files. * Support querying a subset of columns in the files. * Support read-heavy analytical workloads. * Minimize the file size. What should you recommend?
Correct Answer: D
Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar format. Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance. It is especially good for queries that read particular columns from a "wide" (with many columns) table since only needed columns are read, and IO is minimized. Reference: https://www.clairvoyant.ai/blog/big-data-file-formats