Parquet vs ORC
Developers should learn Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by reading only relevant columns meets 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. Here's our take.
Parquet
Developers should learn Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by reading only relevant columns
Parquet
Nice PickDevelopers should learn Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by reading only relevant columns
Pros
- +It is essential for use cases involving data lakes, ETL pipelines, and analytical workloads where fast aggregation and filtering are required, such as in financial analysis, log processing, or machine learning data preparation
- +Related to: apache-spark, apache-hive
Cons
- -Specific tradeoffs depend on your use case
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
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
The Verdict
Use Parquet if: You want it is essential for use cases involving data lakes, etl pipelines, and analytical workloads where fast aggregation and filtering are required, such as in financial analysis, log processing, or machine learning data preparation and can live with specific tradeoffs depend on your use case.
Use ORC if: You prioritize it is especially beneficial in apache hive, apache spark, or presto environments where columnar pruning and predicate pushdown can skip irrelevant data during scans over what Parquet offers.
Developers should learn Parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by reading only relevant columns
Disagree with our pick? nice@nicepick.dev