[{"_1":2,"_46":-5,"_47":-5},"loaderData",{"_3":4,"_14":15,"_44":-5,"_45":-5},"root",{"_5":-5,"_6":-5,"_7":-5,"_8":9,"_10":11},"user","shoppingCartId","toast","locale","en","constants",{"_12":13},"paypalClientId","AenrHU49peF-ZUH-Yq7AXLLNZYGUGlml474U16NCjB4ZznmQCEjiliN3whyuVZmkb7VxEzmTALgtKEKE","routes/_store+/_readers+/email_author.$slug/route",{"_16":17,"_43":-5},"book",{"_18":19,"_20":21,"_22":23,"_24":25,"_26":27,"_28":29},"id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNjgzNDA","title","Grokking Data Analytics and Machine Learning","state","published","contactAuthorMethod","email","aboutText","

Grokking Data Analytics and Machine Learning is your comprehensive journey to mastering the craft of designing scalable data platforms and production ML systems. It focuses on data architecture patterns, ML system design trade-offs, and end-to-end implementation — giving you both the expert-level reasoning to think like a data architect AND the production skills to build systems that process petabytes of data and serve millions of predictions daily.

What makes this guide unique is its dual approach: you'll learn data and ML concepts (Why use lakehouse over data warehouse? When to apply Lambda vs. Kappa architecture?) alongside production implementation (How do I design a feature store with real-time computation? How do I architect model serving infrastructure for low-latency inference?). This bridges the gap that data engineers and ML practitioners face — understanding data warehousing theory or ML algorithms but struggling to implement production-grade data platforms and ML systems at scale.

While foundational data engineering teaches why ETL and batch processing matter, this guide tackles how to handle real-time streaming with late-arriving data, implement feature stores with training-serving consistency, architect MLOps pipelines with continuous training, detect and handle model drift in production, and build observable data systems at petabyte scale — through real-world scenarios, sophisticated trade-off analysis, and hands-on architecture implementation.

This guide is your data and ML architecture proving ground: design data lakehouses, implement streaming pipelines, architect feature stores, deploy ML serving infrastructure, reason about data quality and governance, build MLOps platforms, and most importantly, develop the expertise of architecting and operating data and ML systems in production.

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