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 parquet when working with big data analytics, as it significantly reduces storage costs and improves query performance by reading only relevant columns. 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 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
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 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 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