For large datasets, optimized file formats like Parquet, ORC, and Avro are generally superior to row-based formats (CSV, JSON). These modern formats store data column-wise, meaning values from the same column are grouped together on disk. This columnar organization drastically improves performance for analytical queries that typically scan specific columns, not entire rows. Additionally, they incorporate features like efficient compression to reduce storage footprint, schema evolution to handle changing data structures, and built-in splitting capabilities enabling parallel processing across distributed systems like Hadoop or Spark.

Parquet is widely used with Apache Spark for big data processing across industries like finance for risk modeling and e-commerce for user behavior analysis. Amazon Redshift leverages it for cloud data warehousing. Optimized Row Columnar (ORC) is heavily used within Apache Hive for large-scale data warehousing tasks. Avro's row-based storage with a schema excels in data serialization for streaming pipelines, such as feeding data into Apache Kafka from sensor networks or application logs.

Key advantages include significant query speedups (due to columnar reads and predicate pushdown), reduced storage costs (via strong compression), and inherent support for distributed computing. Limitations can include increased complexity for simple tasks compared to CSV, potential cross-tool compatibility snags, and specialized knowledge requirements for optimization. Continuous developments focus on tighter cloud storage integration (like Delta Lake on object stores), supporting richer analytics (nested data), and enhancing format interoperability.