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Answer: Store data in a columnar format like Parquet, leveraging its built-in compression mechanisms for null values.
Storing data in a columnar format like Parquet is highly effective for sparse datasets due to its ability to only store present values in each column, significantly reducing the space null values occupy. Parquet's built-in compression mechanisms further enhance storage efficiency by optimizing for columnar storage, which is particularly beneficial for datasets with numerous null values. Additionally, this approach improves query performance by allowing analytical queries to access only the necessary columns without scanning irrelevant rows, ensuring a balance between storage efficiency and query performance. This makes Parquet an ideal choice for lakehouses handling highly sparse datasets.
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When dealing with highly sparse datasets, such as user interaction matrices filled with null values, which storage format or technique optimizes both query performance and storage efficiency?
A
Normalize the dataset into multiple tables to separate dense columns from sparse ones, reducing storage overhead.
B
Use Delta Lake‘s binary storage format with custom compression algorithms tailored to sparse data.
C
Store data in a columnar format like Parquet, leveraging its built-in compression mechanisms for null values.
D
Implement a custom sparse matrix storage format as a UDF that compresses null values and decompresses them during queries.
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