Summary and Highlights总结与要点

In this lesson, you have learned:
在本课中，您已学习到：

Big Data refers to the vast amounts of data that is being produced each moment of every day, by people, tools, and machines. The sheer velocity, volume, and variety of data challenged the tools and systems used for conventional data, leading to the emergence of processing tools and platforms designed specifically for Big Data. Big Data processing technologies help derive value from big data. These include NoSQL databases and Data Lakes and open-source technologies such as Apache Hadoop, Apache Hive, and Apache Spark.
大数据指的是由人类、工具和机器每时每刻产生的海量数据。其惊人的速度、体量和多样性对传统数据处理工具与系统提出了挑战，这促使专门针对大数据设计的处理工具与平台应运而生。 大数据处理技术有助于从大数据中提取价值。这些技术包括 NoSQL 数据库、数据湖，以及 Apache Hadoop、Apache Hive 和 Apache Spark 等开源技术。

• Hadoop provides distributed storage and processing of large datasets across clusters of computers. One of its main components, the Hadoop File Distribution System, or HDFS, is a storage system for big data.
  Hadoop 为跨计算机集群的大型数据集提供分布式存储与处理能力。其核心组件之一——Hadoop 文件分布式系统（HDFS）就是专为大数据设计的存储系统。
• Hive is a data warehouse software for reading, writing, and managing large datasets.
  Hive 是一款用于读取、写入及管理大规模数据集的数据仓库软件。
• Spark is a general-purpose data processing engine designed to extract and process large volumes of data.
  Spark 是一种通用型数据处理引擎，专为提取和处理海量数据而设计。