Evolving Dataflow to process massive datasets for machine learning

Google evolved its Dataflow platform to efficiently process massive datasets for machine learning and AI workloads.

• •Liquid sharding dynamically rebalances work units, global compute optimizes resource scheduling, and automatic pipeline fusion reduces I/O overhead
• •Heterogeneous worker pools and TPU-aware autoscaling optimize resource utilization and reduce computational costs
• •Multi-language SDK (Java, Python, Go, C++) supports JAX and LLM-specific optimizations for flexible pipeline development
• •Unified batch and streaming paradigm uses identical code for both historical and real-time data processing
• •Advanced features including sampling, dry-run, pause/resume, and detailed observability improve prototyping and production reliability