Why Traditional ETL Pipelines Become the Bottleneck the Moment You Scale AI Workloads

Traditional ETL was designed for batch analytics on structured data, but AI training needs continuous data flow to prevent GPU starvation, unstructured data support, and framework-native formats. The article recommends GPU-accelerated preprocessing with NVIDIA RAPIDS, S3-compatible object storage for high-throughput parallel reads, and Apache Iceberg for versioning and lineage, distinguishing high-throughput training pipelines from low-latency inference pipelines.