A data engineering team is optimizing a large-scale data analytics pipeline that processes terabytes of transactional data. Current queries, which frequently aggregate metrics from a small subset of columns (e.g., total sales, transaction value), are slow due to significant I/O bottlenecks with the existing row-oriented storage format. To improve performance, the team decides to migrate to the Apache Parquet format. Which feature of Parquet is most directly responsible for accelerating these specific analytical queries?
Its support for schema evolution, which enables the addition or removal of columns without rewriting the entire dataset.
Its columnar storage organization, which allows query engines to selectively read only the required columns, minimizing I/O.
Its native support for complex nested data structures, allowing for the efficient representation of hierarchical data.
Its advanced per-column compression algorithms, which reduce the overall storage footprint and data transfer size.
The correct answer focuses on Parquet's columnar storage organization. In the described scenario, queries only need to access a small subset of many available columns. Parquet's columnar nature allows the query engine to read only the data for the required columns, skipping all other columns entirely. This is a form of projection pushdown, which drastically reduces I/O and is the primary reason for the performance increase in this use case.
Per-column compression is a feature of Parquet and does reduce I/O by making files smaller, but the most significant I/O savings in this scenario come from not having to read the unnecessary columns at all.
Support for schema evolution is a key feature for data lifecycle management, allowing for changes to the table structure over time, but it does not directly accelerate query read performance.
Native support for nested data structures is beneficial for representing complex data types but is not the primary feature that accelerates queries on a subset of columns.
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