Why Building Real-Time Data Pipelines Is So Hard

Building real-time data pipelines for machine learning is challenging due to the need for fast access to feature data, maintaining standing infrastructure, and handling fresh features from multiple sources. The process typically starts with batch feature engineering using tools like data warehouses, data modeling tools, and schedulers, but online inference adds complexity by requiring precomputed features stored in a fast database like Redis. Fresh features multiply the amount of infrastructure needed to manage, and training/serving skew can occur when features are computed in two distinct places. Feature platforms like Tecton provide tools to centrally build and manage diverse data pipelines for machine learning models, helping teams avoid these challenges and simplify the development of real-time data pipelines.

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