This article compares Apache Spark and Apache Flink for stream processing, highlighting key differences and similarities across common use cases.

• Spark excels in ease of use and high-level APIs; Flink offers superior real-time, low-latency stateful processing
• Flink provides layered APIs (Process Functions, DataStream, Table/SQL) with granular control over time and state
• Spark Structured Streaming offers Dataset and DataFrame APIs, roughly equivalent to Flink's Table/SQL layer
• Data preparation: Flink uses SQL DDL for schema definition; Spark requires explicit schema objects
• JSON flattening: Spark uses select method; Flink uses JSON_VALUE function (v1.14+)
• Deduplication: Spark uses dropDuplicates(); Flink uses ROW_NUMBER() window function
• Windowing: Both support tumbling and sliding windows with similar syntax and watermarking for late data
• Data enrichment: Both support UDFs; Flink provides initialization method; async I/O recommended for external APIs

Both frameworks enable efficient big data stream processing with evolving capabilities; choice depends on specific workload requirements and architectural fit.