This document discusses data science, big data, and big data architecture. It begins by defining data science and describing what data scientists do, including extracting insights from both structured and unstructured data using techniques like statistics, programming, and data analysis. It then outlines the cycle of big data management and functional requirements. The document goes on to describe key aspects of big data architecture, including interfaces, redundant physical infrastructure, security, operational data sources, performance considerations, and organizing data services and tools. It provides examples of MapReduce, Hadoop, and BigTable - technologies that enabled processing and analyzing massive amounts of data.