Explore recent improvements in Apache Parquet performance within Apache Spark in this 37-minute talk from Databricks. Learn about vectorized read support for complex types, which can achieve 10x+ improvement when reading Parquet data with complex structures. Discover how Parquet column index support enhances predicate pushdown capabilities, allowing Spark to leverage this feature for more efficient data filtering. Gain insights into the differences between vectorized and non-vectorized Parquet readers, understand the importance of predicate pushdown in optimizing scan performance, and get a glimpse of future work items aimed at further enhancing Parquet read performance in Spark. Delve into technical concepts such as Parquet schema conversion, complex type support, and column index filtering to deepen your understanding of these performance optimizations.