Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts.
Este curso faz parte do Programa de cursos integrados Armazenagem de dados e inteligência de negócios Armazenagem de Dados e Inteligência de Negócios
Suporte de bancos de dados relacionais para Data Warehouses
oferecido por
Programa de cursos integrados Armazenagem de dados e inteligência de negócios Armazenagem de Dados e Inteligência de NegóciosSistema de Universidades do ColoradoUniversidade do Colorado
Informações sobre o curso
4.6
484 classificações
Habilidades que você terá
Data WarehousingMaterialized ViewData WarehouseSQL
Programa - O que você aprenderá com este curso
Semana
14 horas para concluir
DBMS Extensions and Example Data Warehouses
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 DBMS extensions, a review of schema patterns, data warehouses used in practice problems and assignments, and examples of data warehouses in education and health care. 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 Oracle software this week before assignments begin in week 2. If you have taken other courses in the specialization, you may already have installed the Oracle software.
7 vídeos (total de (Total 44 mín.) min), 14 leituras, 1 teste
7 videos
Course objectives video lecture4min
Course topics and assignments video lecture5min
DBMS extensions video lecture8min
Relational database schema patterns video lecture8min
Colorado Education Data Warehouse video lecture7min
Data warehouse standards in health care video lecture7min
14 leituras
Powerpoint lecture notes for lesson 110min
Powerpoint lecture notes for lesson 210min
Optional textbook10min
Powerpoint lecture notes for lesson 310min
Powerpoint lecture notes for lesson 410min
Powerpoint lecture notes for lesson 510min
Powerpoint lecture notes for lesson 610min
Overview of software requirements10min
Overview of database software installation10min
Oracle installation notes10min
Making connections to a local Oracle database10min
SQL statements for Store Sales tables10min
SQL statements for Inventory DW tables10min
Optional textbook reading material10min
1 exercício prático
Module 1 quiz30min
Semana
27 horas para concluir
SQL Subtotal Operators
Now that you have the informational context for relational database support of data warehouses, you’ll start using relational databases to write business intelligence queries! In module 2, you will learn an important extension of the SQL SELECT statement for subtotal operators. You’ll apply what you’ve learned in practice and graded problems using Oracle SQL for problems involving the CUBE, ROLLUP, and GROUPING SETS operators. Because the subtotal operators are part of the SQL standard, your learning will readily apply to other enterprise DBMSs. At the end of this module, you will have solid background to write queries using the SQL subtotal operators as a data warehouse analyst.
5 vídeos (total de (Total 39 mín.) min), 12 leituras, 3 testes
5 videos
SQL CUBE operator video lecture7min
SQL ROLLUP operator video lecture7min
SQL GROUPING SETS operator video lecture7min
Variations of subtotal operators video lecture9min
12 leituras
Powerpoint lecture notes for lesson 110min
Additional problems for lesson 110min
Powerpoint lecture notes for lesson 210min
Additional problems for lesson 210min
Powerpoint lecture notes for lesson 310min
Additional problems for lesson 310min
Powerpoint lecture notes for lesson 410min
Additional problems for lesson 410min
Powerpoint lecture notes for lesson 510min
Additional problems for lesson 510min
Optional textbook reading material10min
Assignment notes10min
2 exercícios práticos
Module 2 quiz20min
Quiz for module 2 assignment4min
Semana
37 horas para concluir
SQL Analytic Functions
After your experience using the SQL subtotal operators, you are ready to learn another important SQL extension for business intelligence applications. In module 3, you will learn about an extended processing model for SQL analytic functions that support common analysis in business intelligence applications. You’ll apply what you’ve learned in practice and graded problems using Oracle SQL for problems involving qualitative ranking of business units, window comparisons showing relationships of business units over time, and quantitative contributions showing performance thresholds and contributions of individual business units to a whole business. Because analytic functions are part of the SQL standard, your learning will apply to other enterprise DBMSs. At the end of this module, you will have solid background to write queries using the SQL analytic functions as a data warehouse analyst.
5 vídeos (total de (Total 47 mín.) min), 12 leituras, 3 testes
5 videos
Extended Syntax and Ranking Functions video lecture7min
Window Comparison I video lecture8min
Window Comparisons II video lecture10min
Functions for Ratio Comparisons video lecture10min
12 leituras
Powerpoint lecture notes for lesson 110min
Additional problems for lesson 110min
Powerpoint lecture notes for lesson 210min
Additional problems for lesson 210min
Powerpoint lecture notes for lesson 310min
Additional problems for lesson 310min
Powerpoint lecture notes for lesson 410min
Additional problems for lesson 410min
Powerpoint lecture notes for lesson 510min
Additional problems for lesson 510min
Optional reading material10min
Assignment notes10min
2 exercícios práticos
Module 3 quiz20min
Quiz for module 3 assignment38min
Semana
46 horas para concluir
Materialized View Processing and Design
After acquiring query formulation skills for development of business intelligence applications, you are ready to learn about DBMS extensions for efficient query execution. Business intelligence queries can use lots of resources so materialized view processing and design has become an important extension of DBMSs. In module 4, you will learn about an SQL statement for creating materialized views, processing requirements for materialized views, and rules for rewriting queries using materialized views. To gain insight about the complexity of query rewriting, you will practice rewriting queries using materialized views. To provide closure about relational database support for data warehouses, you will learn about about Oracle tools for data integration, the Oracle Data Integrator, along with two SQL statements useful for specific data integration tasks. After this module, you will have a solid background to use materialized views to improve query performance and deploy the Extraction, Loading, and Transformation approach for data integration as a data warehouse administrator or analyst.
5 vídeos (total de (Total 54 mín.) min), 11 leituras, 3 testes
5 videos
Materialized view definition and processing video lecture9min
Query Rewriting Rules video lecture8min
Query Rewriting Examples video lecture10min
Oracle Tools for Data Integration video lecture14min
11 leituras
Powerpoint lecture notes for lesson 110min
Additional problems for lesson 110min
Powerpoint lecture notes for lesson 210min
Additional problems for lesson 210min
Powerpoint lecture notes for lesson 310min
Powerpoint lecture notes for lesson 410min
Additional problems for lesson 410min
Powerpoint lecture notes for lesson 510min
Additional problems for lesson 510min
Optional textbook reading material10min
Assignment notes10min
2 exercícios práticos
Module 4 quiz30min
Quiz for module 4 assignment38min
33%
comecei uma nova carreira após concluir estes cursos
20%
consegui um benefício significativo de carreira com este curso
25%
recebi um aumento ou promoção
Melhores avaliações
Awesome content ,very well drafted and explained course content. I am thankful to the coursera team, who has discovered such kind of really good course and specialization.
Excellent Contents for concepts explained with sample queries and assignments. It is really amazing to go thru all the new concepts within such a short time frame.
Instrutores
Michael Mannino
Associate ProfessorBusiness School, University of Colorado Denver
Sobre Sistema de Universidades do ColoradoUniversidade do Colorado
The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond.
Sobre o Programa de cursos integrados Armazenagem de dados e inteligência de negócios Armazenagem de Dados e Inteligência de Negócios
Evaluate business needs, design a data warehouse, and integrate and visualize data using dashboards and visual analytics.
This Specialization covers data architecture skills that are increasingly critical across a broad range of technology fields. You’ll learn the basics of structured data modeling, gain practical SQL coding experience, and develop an in-depth understanding of data warehouse design and data manipulation. You’ll have the opportunity to work with large data sets in a data warehouse environment to create dashboards and Visual Analytics. You will use of MicroStrategy, a leading BI tool, OLAP (online analytical processing) and Visual Insights capabilities to create dashboards and Visual Analytics. In the final Capstone Project, you’ll apply your skills to build a small, basic data warehouse, populate it with data, and create dashboards and other visualizations to analyze and communicate the data to a broad audience.
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