ORC vs Parquet
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 parquet when working with large-scale analytical data processing, as it significantly reduces storage costs and improves query performance through columnar compression and predicate pushdown. 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
Parquet
Developers should learn and use Parquet when working with large-scale analytical data processing, as it significantly reduces storage costs and improves query performance through columnar compression and predicate pushdown
Pros
- +It is ideal for use cases such as data warehousing, log analysis, and machine learning pipelines where read-heavy operations dominate, and it integrates seamlessly with modern data ecosystems like cloud storage (e
- +Related to: apache-spark, apache-hadoop
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use ORC if: You want it is especially beneficial in apache hive, apache spark, or presto environments where columnar pruning and predicate pushdown can skip irrelevant data during scans and can live with specific tradeoffs depend on your use case.
Use Parquet if: You prioritize it is ideal for use cases such as data warehousing, log analysis, and machine learning pipelines where read-heavy operations dominate, and it integrates seamlessly with modern data ecosystems like cloud storage (e over what ORC offers.
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
Disagree with our pick? nice@nicepick.dev