ORC vs CSV
Developers should use ORC when working with Hadoop-based data lakes or data warehouses, as it significantly reduces storage costs and improves query performance for analytical queries compared to row-based formats meets developers should learn and use csv for handling lightweight data import/export tasks, such as migrating data between systems, generating reports, or processing datasets in analytics. Here's our take.
ORC
Developers should use ORC when working with Hadoop-based data lakes or data warehouses, as it significantly reduces storage costs and improves query performance for analytical queries compared to row-based formats
ORC
Nice PickDevelopers should use ORC when working with Hadoop-based data lakes or data warehouses, as it significantly reduces storage costs and improves query performance for analytical queries compared to row-based formats
Pros
- +It is especially beneficial in Apache Hive, Apache Spark, or Presto environments where columnar pruning and predicate pushdown can skip irrelevant data during scans
- +Related to: apache-hive, apache-spark
Cons
- -Specific tradeoffs depend on your use case
CSV
Developers should learn and use CSV for handling lightweight data import/export tasks, such as migrating data between systems, generating reports, or processing datasets in analytics
Pros
- +It is particularly useful in scenarios requiring interoperability with tools like Excel, data pipelines, or when working with structured data in a human-readable format without complex dependencies
- +Related to: data-import, data-export
Cons
- -Specific tradeoffs depend on your use case
The Verdict
These tools serve different purposes. ORC is a database while CSV is a format. We picked ORC based on overall popularity, but your choice depends on what you're building.
Based on overall popularity. ORC is more widely used, but CSV excels in its own space.
Disagree with our pick? nice@nicepick.dev