This technical presentation reviews the historical development of big data platforms, outlines the four generations of cloud data platform architectures, details the modern cloud‑native stack—including unified metadata, scheduling, and integration systems—and showcases a real‑world industrial manufacturing case with a Q&A session.
This article traces 20 years of big‑data evolution, defines data lakes and data warehouses, compares their trade‑offs, and explains how lakehouse solutions—exemplified by Alibaba Cloud MaxCompute—merge flexibility with enterprise‑grade performance to lower total ownership cost.
The talk outlines how to treat raw data like crude oil—using a robust big‑data platform, entity‑resolution techniques, secure data governance, and visualisation tools to transform disparate sources into reusable assets that drive rapid business insights and operational efficiency.
This article provides a comprehensive overview of the Lakehouse paradigm, tracing its origins from traditional data warehouses and data lakes, comparing architectures, detailing core components such as Delta Lake and Iceberg, and illustrating practical cloud implementations and future directions.
