Start your journey into database design by learning about the two approaches to data processing, OLTP and OLAP. In this first chapter, you'll also get familiar with the different forms data can be stored in and learn the basics of data modeling.

OLTP and OLAP

OLAP vs. OLTP

Which is better?

Storing data

Name that data type!

Ordering ETL Tasks

Recommend a storage solution

Database design

Classifying data models

Deciding fact and dimension tables

Querying the dimensional model

Processing, Storing, and Organizing Data

In this chapter, you will take your data modeling skills to the next level. You'll learn to implement star and snowflake schemas, recognize the importance of normalization and see how to normalize databases to different extents.

Star and snowflake schema

Running from star to snowflake

Adding foreign keys

Extending the book dimension

Normalized and denormalized databases

Querying the star schema

Querying the snowflake schema

Updating countries

Extending the snowflake schema

Normal forms

Converting to 1NF

Converting to 2NF

Converting to 3NF

Database Schemas and Normalization

Get ready to work with views! In this chapter, you will learn how to create and query views. On top of that, you'll master more advanced capabilities to manage them and end by identifying the difference between materialized and non-materialized views.

Database views

Tables vs. views

Viewing views

Creating and querying a view

Managing views

Creating a view from other views

Granting and revoking access

Updatable views

Redefining a view

Materialized views

Materialized versus non-materialized

Creating and refreshing a materialized view

Managing materialized views

Database Views

This final chapter ends with some database management-related topics. You will learn how to grant database access based on user roles, how to partition tables into smaller pieces, what to keep in mind when integrating data, and which DBMS fits your business needs best.

Database roles and access control

Create a role

GRANT privileges and ALTER attributes

Add a user role to a group role

Table partitioning

Reasons to partition

Characteristics

Normalization

Vertical Partitioning

Horizontal Partitioning

Partitioning and normalization

Creating vertical partitions

Creating horizontal partitions

Data integration

Options

False

True

Data integration do's and dont's

Analyzing a data integration plan

Picking a Database Management System (DBMS)

SQL versus NoSQL

DBMSs

NoSQL

Choosing the right DBMS

Database Management

Chicago 311 Service Requests

Pitchfork Reviews

A good database design is crucial for a high-performance application. Just like you wouldn't start building a house without the benefit of a blueprint, you need to think about how your data will be stored beforehand. Taking the time to design a database saves time and frustration later on, and a well-designed database ensures ease of access and retrieval of information. While choosing a design, a lot of considerations have to be accounted for. In this course, you'll learn how to process, store, and organize data in an efficient way. You'll see how to structure data through normalization and present your data with views. Finally, you'll learn how to manage your database and all of this will be done on a variety of datasets from book sales, car rentals, to music reviews.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

Introduction to Relational Databases in SQL

Learn to design databases using star and snowflake schemas, and OLTP and OLAP. You will gain essential skills for creating and managing databases in SQL.

Database Design

Learn to design databases in SQL to process, store, and organize data in a more efficient way.

Yardımcı Veri Mühendisi SQL içinde

Yardımcı Veri Mühendisi Kar Tanesi'nde

Veritabanı Yöneticileri için SQL

SQL Temelleri

SQL Server Geliştiricisi

Veritabanı Yöneticileri için SQL Server

Automated testing and proactive alerts are not needed.

All your data has to be updated in real time in the final view.

My source data can be in different formats and database management systems.

You should be careful choosing a hand-coded solution because of maintenance cost.

Data integration should be business driven, e.g. what combination of data will be useful for the business.

Data in the final view can be updated in different intervals.

Data integration do's and dont's

Database Design

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