Explains the importance of data storage formats and compression for performance and cost in large-scale data engineering systems.
Explores compression algorithms in Parquet files, comparing Snappy, Gzip, Brotli, Zstandard, and LZO for storage and performance.
Explores how metadata in Parquet files improves data efficiency and query performance, covering file, row group, and column-level metadata.
Explores why Parquet is the ideal columnar file format for optimizing storage and query performance in modern data lake and lakehouse architectures.
Explains the hierarchical structure of Parquet files, detailing how pages, row groups, and columns optimize storage and query performance.
An introduction to Apache Parquet, a columnar storage file format for efficient data processing and analytics.
Explains Parquet's columnar storage model, detailing its efficiency for big data analytics through faster queries, better compression, and optimized aggregation.
Compares columnar vs. row-based data structures, explaining their optimal use in OLAP and OLTP systems for performance and scalability.
