Cloud · AWS Core Services

AWS Glue Streaming versus Managed Service for Apache Flink

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Interview question

How do you evaluate AWS Glue Streaming against Amazon Managed Service for Apache Flink for real-time analytics?

How to answer

The short answer

When architecting real-time streaming pipelines on AWS, data engineers often choose between two fully managed distributed engines: AWS Glue Streaming (powered by Apache Spark Structured Streaming) and Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics).

Selecting the appropriate engine requires analyzing latency requirements, processing semantics, and state management complexity.

Output
Processing Dimension     AWS Glue Streaming           Managed Service for Flink
--------------------------------------------------------------------------------
Engine                   Apache Spark Streaming       Apache Flink
Processing model         Micro-batch (default)        Continuous event-at-a-time
Processing latency       500ms to minutes             Sub-50 milliseconds
State backend            HDFS / S3 checkpoints        RocksDB embedded in memory
Windowing flexibility   Standard tumbling / sliding  Complex session & CEP patterns

AWS Glue Streaming characteristics

Glue Streaming executes Spark Structured Streaming jobs inside serverless Glue Data Processing Units (DPUs).

  • Architecture: Ingestion processes records in configurable micro-batches (typically 1 to 5 seconds).
  • Best fit: Ideal for lakehouse ingestion pipelines where raw Kafka or Kinesis streams need validation, schema enforcement, enrichment against relational reference tables, and incremental writes to Delta Lake or Apache Iceberg tables on S3.
  • Skill reuse: Teams with established PySpark codebases can deploy streaming jobs with minimal engineering refactoring.

Amazon Managed Service for Apache Flink characteristics

Managed Flink runs native Apache Flink applications inside provisioned Kinesis Processing Units (KPUs).

  • Architecture: True continuous stream processing where every incoming record is evaluated individually within milliseconds.
  • Stateful processing: Flink maintains high-performance local state backed by an embedded RocksDB database, enabling complex event processing (CEP), pattern detection across out-of-order streams, and session windows over multi-day periods.
  • Best fit: Real-time fraud detection, financial trading signals, and millisecond alerting platforms.