ORC vs Avro

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 avro when working in distributed systems, particularly in big data environments like hadoop, kafka, or spark, where efficient and schema-aware data serialization is critical for performance and interoperability. Here's our take.

ORC

Nice Pick

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

Avro

Developers should learn Avro when working in distributed systems, particularly in big data environments like Hadoop, Kafka, or Spark, where efficient and schema-aware data serialization is critical for performance and interoperability

Pros

• It is ideal for use cases involving data pipelines, log aggregation, and real-time streaming, as its compact format reduces storage and network overhead while supporting backward and forward compatibility through schema evolution
• Related to: apache-hadoop, apache-kafka

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. ORC is a database while Avro is a tool. We picked ORC based on overall popularity, but your choice depends on what you're building.

The Bottom Line

ORC wins

Based on overall popularity. ORC is more widely used, but Avro excels in its own space.