Apache ORC vs Avro

Developers should learn ORC when working with big data platforms like Apache Hive, Spark, or Presto to optimize storage and query performance for analytical workloads 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.

Apache ORC

Nice Pick

Developers should learn ORC when working with big data platforms like Apache Hive, Spark, or Presto to optimize storage and query performance for analytical workloads

Pros

• It is particularly useful for scenarios involving large-scale data processing, such as log analysis, business intelligence, and data lake implementations, due to its efficient compression and predicate pushdown capabilities
• 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. Apache ORC is a database while Avro is a tool. We picked Apache ORC based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Apache ORC wins

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