Parquet on disk, Arrow in memory: Why Spark pipelines get faster

Matt Topol explains that the most efficient data pipelines use Apache Parquet for storage and Apache Arrow for in-memory compute. By standardizing the in-memory format within Apache Spark, we remove the constant need to "decode" data as it moves between nodes and different language runtimes (like Python and the JVM).

Standardizing on Arrow ensures that Apache Spark spends its time processing data, not just translating it.

Видео Parquet on disk, Arrow in memory: Why Spark pipelines get faster канала Apache Spark