|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/dwdesign
课程评论：没有评论
课程名称：数据仓库概念、设计和数据集成 课程概述：本课程是“商业智能数据仓库”专项中的第二门课程，建议按顺序学习。课程将教授设计数据仓库和创建数据集成工作流的有趣概念和技能，这些对于数据仓库开发人员和管理员来说是基本技能。课程包括实践经验，利用开源产品处理数据透视表和创建数据集成工作流。在数据集成作业中，可以使用Oracle、MySQL或PostgreSQL数据库。课程还将深入探讨成熟度模型、架构、多维模型和管理实践，为数据仓库开发提供组织视角。适合希望成为数据仓库设计师或管理员的商业或信息技术专业人士。 学习目标： - 评估组织的数据仓库成熟度和商业架构对齐情况； - 创建数据仓库设计，反思不同设计方法和目标； - 使用知名开源软件创建数据集成工作流； - 反思变更数据、刷新约束、刷新频率权衡和数据质量目标在数据集成过程设计中的作用； - 使用知名开源软件对数据透视表进行操作，以满足典型的商业分析请求。 课程大纲： 1. 数据仓库概念与架构：介绍课程和数据仓库技术的发展背景、商业架构及就业机会等，为后续模块打下基础。 2. 多维数据表示与操作：学习数据仓库的多维表示，使用WebPivotTable工具进行数据透视表的操作练习。 3. 数据仓库设计实践与方法：学习关系数据库的数据仓库设计，掌握设计模式和schema集成等概念，并通过案例学习应用这些知识。 4. 数据集成概念、过程与技术：了解数据集成处理和技术，包括SQL语句的应用，为后续的实践软件技能打下基础。 5. 数据集成工具的架构、特性与细节：学习开源数据集成工具的架构和特点，通过Pentaho Data Integration进行引导教程和作业练习。 通过本课程，您将获得数据仓库设计和实施的实践经验，为成功开发数据仓库项目做好准备。
Name:Data Warehouse Concepts and Architectures
Description:Module 1 introduces the course and covers concepts that provide a context for the remainder of this course. In the first two lessons, you’ll understand the objectives for the course and know what topics and assignments to expect. In the remaining lessons, you will learn about historical reasons for development of data warehouse technology, learning effects, business architectures, maturity models, project management issues, market trends, and employment opportunities. This informational module will ensure that you have the background for success in later modules that emphasize details and hands-on skills.You should also read about the software requirements in the lesson at the end of module 1. I recommend that you try to install the software this week before assignments begin in week 2.
Name:Multidimensional Data Representation and Manipulation
Description:Now that you have conceptual background for data warehouse development, you’ll start using data warehouse tools. In module 2, you will learn about the multidimensional representation of a data warehouse used by business analysts. You’ll apply what you’ve learned in practice and graded problems using WebPivotTable, a web-based tool for manipulating pivot tables. At the end of this module, you will have solid background to communicate and assist business analysts who use a multidimensional representation of a data warehouse. To complete this module, you should proceed to the assignment and quiz involving WebPivotTable.
Name:Data Warehouse Design Practices and Methodologies
Description:This module emphasizes data warehouse design skills. Now that you understand the multidimensional representation used by business analysts, you are ready to learn about data warehouse design using a relational database. In practice, the multidimensional representation used by business analysts must be derived from a data warehouse design using a relational DBMS. You will learn about design patterns, summarizability problems, transformations for schema integration, and design methodologies. You will apply these concepts to mini case studies about data warehouse design. At the end of the module, you will have created data warehouse designs based on data sources and business needs of hypothetical organizations.
Name:Data Integration Concepts, Processes, and Techniques
Description:Module 4 extends your background about data warehouse development. After learning about schema design concepts and practices, you are ready to learn about data integration processing to populate and refresh a data warehouse. The informational background in module 4 covers concepts about data sources, data integration processes, and techniques for pattern matching and inexact matching of text. Module 4 provides detailed material about SQL statements for data integration with examples and an assignment for both Oracle Cloud and PostgreSQL. Module 4 provides a context for the software skills that you will learn in module 5.
Name:Architectures, Features, and Details of Data Integration Tools
Description:Module 5 extends your background about data integration from module 4. Module 5 covers architectures, features, and details about data integration tools to complement the conceptual background in module 4. You will learn about the features of two open source data integration tools, Talend Open Studio and Pentaho Data Integration. You will use Pentaho Data Integration in a guided tutorial in preparation for a graded assignment involving Pentaho Data Integration. For the tutorial and assignment, you need to connect to a database server, Oracle Cloud or PostgreSQL. If you have time, I recommend completing the data integration assignment using both Oracle Cloud and PostgreSQL.
This is the second course in the Data Warehousing for Business Intelligence specialization. Ideally, the courses should be taken in sequence. In this course, you will learn exciting concepts and skills for designing data warehouses and creating data integration workflows. These are fundamental skills for data warehouse developers and administrators. You will have hands-on experience for data warehouse design and use open source products for manipulating pivot tables and creating data integration workflows. In the data integration assignment, you can use either Oracle, MySQL, or PostgreSQL databases. You will also gain conceptual background about maturity models, architectures, multidimensional models, and management practices, providing an organizational perspective about data warehouse development. If you are currently a business or information technology professional and want to become a data warehouse designer or administrator, this course will give you the knowledge and skills to do that. By the end of the course, you will have the design experience, software background, and organizational context that prepares you to succeed with data warehouse development projects. In this course, you will create data warehouse designs and data integration workflows that satisfy the business intelligence needs of organizations. When you’re done with this course, you’ll be able to: * Evaluate an organization for data warehouse maturity and business architecture alignment; * Create a data warehouse design and reflect on alternative design methodologies and design goals; * Create data integration workflows using prominent open source software; * Reflect on the role of change data, refresh constraints, refresh frequency trade-offs, and data quality goals in data integration process design; and * Perform operations on pivot tables to satisfy typical business analysis requests using prominent open source software
