Building upon the architectural patterns discussed previously, this chapter concentrates on the generation and manipulation of features within the store. You will learn to implement complex transformation pipelines, process streaming data, and store features derived from unstructured data or embeddings. Techniques for performing time-window aggregations efficiently at scale will be addressed, along with the considerations for choosing between batch, streaming, and on-demand computation approaches. By the end of this chapter, you will understand how to manage advanced feature engineering tasks and computation logic directly within your feature store infrastructure.
2.1 Feature Transformation Pipelines
2.2 Handling Streaming Features
2.3 Managing Embeddings and Unstructured Data
2.4 Time-Window Aggregations at Scale
2.5 On-Demand Feature Computation
2.6 Batch vs. Real-time Computation Trade-offs
2.7 Hands-on Practical: Implementing Complex Transformations
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