sail

The framework addresses the operational fragmentation that occurs when data teams run separate systems for batch ETL (Spark), stream processing (Flink or Kafka Streams), and ML trainingrequiring data to be moved between systems and teams to maintain expertise across multiple frameworks.

SAIL aims to provide a single programming model that handles all three workload types efficiently.

The framework's unified execution model allows data pipelines to transition between batch and streaming processing modes without code rewrites, and integrates ML training and inference as native pipeline operations rather than requiring separate framework calls.

This integration is particularly valuable for online learning applications where models need to update continuously from streaming data, and for feature engineering pipelines that serve both batch model training and real-time feature serving from the same computation definition.

Data platform engineers at organizations frustrated with the complexity and operational overhead of running separate batch, streaming, and ML infrastructure use SAIL to reduce the number of systems they maintain and the context switching between frameworks.

Organizations building real-time ML applicationsfraud detection, personalization, anomaly detectionwhere the boundary between data processing and ML inference is blurred find a unified framework reduces the engineering complexity of keeping processing logic consistent across the batch training and streaming inference paths.