Module 01 · What Is Big Data?
Module 1 Glossary: What Is Big Data?模块 1 词汇表：什么是大数据？
Spark and Hadoop for Big Data Analytics Spark 和 Hadoop 在大数据分析中的应用
Welcome! This alphabetized glossary contains many of the terms in this course. This comprehensive glossary also includes additional industry-recognized terms not used in course videos. These terms are essential for you to recognize when working in the industry, participating in user groups, and in other professional certificate programs.
欢迎！这个按字母顺序排列的词汇表包含了本课程中的许多术语。这个全面的词汇表还包括了课程视频中没有使用的行业认可术语。这些术语对于您在行业内工作、参与用户组以及在其他专业证书课程中都是必不可少的。
Estimated reading time: 12 minutes
预计阅读时间：12 分钟
| Term 术语 | Definition 定义 |
|---|---|
| Apache Spark | An open-source, in-memory application framework used for distributed data processing and iterative analysis of large data sets. 一个用于分布式数据处理和大规模数据集迭代分析的开放式内存应用程序框架。 |
| Apache HBase | A robust NoSQL datastore that efficiently manages storage and computation resources independently of the Hadoop ecosystem. 一个强大的 NoSQL 数据存储，能够独立于 Hadoop 生态系统高效管理存储和计算资源。 |
| Business intelligence (BI) 商业智能（BI） |
Encompasses various tools and methodologies designed to convert data into actionable insights efficiently. 包含各种工具和方法，旨在高效地将数据转化为可操作的洞察。 |
| Big data 大数据 | Data sets whose volume, velocity, or variety exceeds the capacity of conventional relational databases to effectively manage, capture, and process with minimal latency. Key characteristics of big data include substantial volume, high velocity, and diverse variety. 数据集的规模、速度或种类超过了传统关系型数据库有效管理、捕获和处理的能力，且延迟极低。大数据的关键特征包括巨大的规模、高速度和多样化的种类。 |
| Big data analytics 大数据分析 | Uses advanced analytic techniques against large, diverse big data sets that include structured, semi-structured, and unstructured data from different sources and sizes, from terabytes to zettabytes. It helps companies gain insights from the data collected by IoT devices. 使用先进的分析技术对大型、多样化的数据集进行分析，这些数据集包括来自不同来源和规模的结构化、半结构化和非结构化数据，从 TB 级到 ZB 级。它帮助公司从物联网设备收集的数据中获得洞察。 |
| Big data programming tools 大数据编程工具 |
Programming tools are the final component of big data commercial tools. These programming tools perform large-scale analytical tasks and operationalize big data. They also provide all necessary functions for data collection, cleaning, exploration, modeling, and visualization. Some popular tools you can use for programming include R, Python, SQL, Scala, and Julia. 编程工具是大数据商业工具的最终组成部分。这些编程工具执行大规模分析任务，并使大数据实现业务化。它们还提供数据收集、清洗、探索、建模和可视化所需的所有必要功能。一些流行的编程工具包括 R、Python、SQL、Scala 和 Julia。 |
| Committer 提交者 | Most open-source projects have formal processes for contributing code and include various levels of influence and obligation to the project: Committer, contributor, user, and user group. Typically, committers can modify the code directly. 大多数开源项目都有正式的代码贡献流程，并包括对项目具有不同影响力和义务的多个层级：提交者、贡献者、用户和用户组。通常，提交者可以直接修改代码。 |
| Cloud computing 云计算 | Allows customers to access infrastructure and applications over the internet without needing on-premises installation and maintenance. By leveraging cloud computing, companies can utilize server capacity on-demand and rapidly scale up to handle the extensive computational requirements of processing large data sets and executing complex mathematical models. 允许客户通过互联网访问基础设施和应用程序，无需本地安装和维护。通过利用云计算，企业可以按需使用服务器容量，并快速扩展以处理大型数据集的复杂计算需求和执行复杂的数学模型。 |
| Cloud providers 云服务提供商 | Offer essential infrastructure and support, providing shared computing resources encompassing computing power, storage, networking, and analytical software. These providers also offer software as a service model featuring specific solutions, enabling enterprises to gather, process, and visualize data efficiently. Prominent examples of cloud service providers include AWS, IBM, GCP, and Oracle. 提供基本的设施和支持，提供涵盖计算能力、存储、网络和分析软件的共享计算资源。这些提供商还提供软件即服务（SaaS）模式，具有特定的解决方案，使企业能够高效地收集、处理和可视化数据。云服务提供商的突出例子包括 AWS、IBM、GCP 和 Oracle。 |
| Extract, transform, and load (ETL) process 提取、转换和加载（ETL）过程 |
A systematic approach that involves extracting data from various sources, transforming it to meet specific requirements, and loading it into a data warehouse or another centralized data repository. 一种系统化方法，涉及从各种来源提取数据，将其转换为满足特定要求，并加载到数据仓库或另一个集中式数据存储库中。 |
| Hadoop | An open-source software framework that provides dependable distributed processing for large data sets through the utilization of simplified programming models. 一个开源软件框架，通过利用简化的编程模型为大型数据集提供可靠的分布式处理。 |
| Hadoop Distributed File System (HDFS) Hadoop 分布式文件系统（HDFS） |
A file system distributed on multiple file servers, allowing programmers to access or store files from any network or computer. It is the storage layer of Hadoop. It works by splitting the files into blocks, creating replicas of the blocks, and storing them on different machines. It is built to access streaming data seamlessly. It uses a command-line interface to interact with Hadoop. 一个分布在多个文件服务器上的文件系统，允许程序员从任何网络或计算机访问或存储文件。它是 Hadoop 的存储层。它通过将文件分割成块、创建块的副本并将它们存储在不同的机器上来工作。它是为无缝访问流数据而构建的。它使用命令行界面与 Hadoop 进行交互。 |
| Hive | A data warehouse infrastructure employed for data querying and analysis, featuring an SQL-like interface. It facilitates report generation and utilizes a declarative programming language, enabling users to specify the data they want to retrieve. 一个用于数据查询和分析的数据仓库基础设施，具有类似 SQL 的界面。它促进报表生成，并利用声明式编程语言，使用户能够指定他们想要检索的数据。 |
| Internet of Things (IoT) 物联网（IoT） | A system of physical objects connected through the internet. A thing or device can include a smart device in our homes or a personal communication device such as a smartphone or computer. These collect and transfer massive amounts of data over the internet without manual intervention by using embedded technologies. 一个通过互联网连接的物理对象系统。一个事物或设备可以包括我们家里的智能设备，或个人通信设备，如智能手机或计算机。这些设备通过嵌入式技术收集和传输大量数据，无需人工干预。 |
| Machine data 机器数据 | Refers to information generated by various sources, including the Internet of Things (IoT) sensors embedded in industrial equipment, as well as weblogs that capture user behavior and interactions. 指由各种来源生成的信息，包括嵌入在工业设备中的物联网（IoT）传感器，以及捕捉用户行为和交互的博客。 |
| Map 地图 | MapReduce converts a set of data into another set of data, and the elements are fragmented into tuples (key or value pairs). MapReduce 将一组数据转换为另一组数据，并将元素分割为元组（键值对）。 |
| MapReduce | A program model and processing technique used in distributed computing based on Java. It splits the data into smaller units and processes big data. It is the first method used to query data stored in HDFS. It allows massive scalability across hundreds or thousands of servers in a Hadoop cluster. 一种基于 Java 的分布式计算中使用的程序模型和处理技术。它将数据分割成更小的单元，并处理大数据。它是查询存储在 HDFS 中数据的第一种方法。它允许在 Hadoop 集群中的数百或数千台服务器上实现大规模扩展。 |
| NoSQL databases NoSQL 数据库 | NoSQL databases are built from the ground up to store and process vast amounts of data at scale and support a growing number of modern businesses. NoSQL databases store data in documents rather than relational tables. Types of NoSQL databases include pure document databases, key-value stores, wide-column databases, and graph databases such as MongoDB, CouchDB, Cassandra, and Redis. NoSQL 数据库是为了在规模上存储和处理大量数据，并支持日益增多的现代企业而设计的。NoSQL 数据库以文档形式存储数据，而不是关系表。NoSQL 数据库的类型包括纯文档数据库、键值存储、宽列数据库以及图数据库，如 MongoDB、CouchDB、Cassandra 和 Redis。 |
| Open-source software 开源软件 | Not only is the runnable version of the code free, but the source code is also completely open, meaning that every line of code is available for people to view, use, and reuse as needed. 不仅可执行版本的代码是免费的，源代码也是完全开放的，这意味着每一行代码都可以供人们查看、使用和按需重用。 |
| Price analytics 价格分析 | Helps understand market segmentation, identify the best price points for a product line, and perform margin analysis for maximum profitability. 有助于理解市场细分，确定产品线的最佳价格点，并执行利润率分析以实现最大利润。 |
| Relational databases 关系型数据库 | Data is structured in the form of tables, with rows and columns, collectively forming a relational database. These tables are interconnected using primary and foreign keys to establish relationships across the data set. 数据以表格形式结构化，包含行和列，共同构成关系数据库。这些表格通过主键和外键相互连接，以在数据集中建立关系。 |
| Sentiment analysis 情感分析 | Utilizes social media conversations to gain insights into consumer opinions about a product. It is used to develop effective marketing strategies and establish customer connections based on their sentiments and preferences. 利用社交媒体对话来获取消费者对产品的意见洞察。它用于制定有效的营销策略，并根据其情感和偏好建立客户关系。 |
| Social data 社交数据 | Comes from the likes, tweets and retweets, comments, video uploads, and general media that are uploaded and shared via the world's favorite social media platforms. Machine-generated data and business-generated data are data that organizations generate within their own operations. 来源于全球最受欢迎的社交媒体平台上上传和分享的点赞、推文、转发、评论、视频上传以及一般媒体。机器生成数据和企业生成数据是组织在其自身运营中生成数据。 |
| Transactional data 交易数据 | Generated from all the daily transactions that take place both online and offline, such as invoices, payment orders, storage records, and delivery receipts. 由线上和线下发生的所有日常交易生成，例如发票、付款订单、存储记录和交货收据。 |
| Velocity 速度 | The speed at which data arrives. Velocity is one of the four main components used to describe the dimensions of big data. 数据到达的速度。速度是用于描述大数据维度的四个主要组成部分之一。 |
| Volume 容量 | The increase in the amount of data stored over time. Volume is one of the four main components used to describe the dimensions of big data. 随着时间推移，存储数据量的增加。容量是描述大数据维度的四个主要组成部分之一。 |
| Variety 多样性 | The diversity of data or the various data forms that need to be stored. Variety is one of the four main components used to describe the dimensions of big data. 数据的多样性或需要存储的各种数据形式。多样性是描述大数据维度的四个主要组成部分之一。 |
| Veracity 真实性 | The certainty of data, as with a large amount of data available, makes it difficult to determine if the data collected is accurate. Veracity is one of the four main components used to describe the dimensions of big data. 数据的确定性，随着大量数据的可用性，使得难以确定所收集的数据是否准确。真实性是描述大数据维度的四个主要组成部分之一。 |
| Yet Another Resource Negotiator (YARN) | Serves as the resource manager bundled with Hadoop and is typically the default resource manager for numerous big data applications, such as HIVE and Spark. While it remains a robust resource manager, it's important to note that more contemporary container-based resource managers, such as Kubernetes, are gradually emerging as the new standard practices in the field. 作为与 Hadoop 捆绑在一起的资源管理器，它是许多大数据应用（如 HIVE 和 Spark）的默认资源管理器。虽然它仍然是一个强大的资源管理器，但需要注意的是，更现代的基于容器的资源管理器（如 Kubernetes）正在逐渐成为该领域的新的标准实践。 |