Data Modeling Basics | #Tableau Course #32

Summary

The video explains the fundamentals of data modeling, a process of organizing and representing data for clear understanding. It covers three types of data models: conceptual, logical, and physical. The speaker focuses on business intelligence data models, specifically the star and snowflake schemas, used in tools like Tableau and Power BI. The star schema, ideal for small datasets, features a central fact table and surrounding dimension tables. The snowflake schema is more complex but optimizes storage. The video uses a star schema to demonstrate its simplicity, with facts and dimensions key to understanding real-world analytics.

Takeaways

• 📊 Data modeling organizes and represents data clearly for decision-making and business performance improvement.
• 🧱 A data model includes entities like customers or products, with attributes like names, and shows relationships between entities.
• 🔍 There are three types of data models: conceptual (big picture), logical (detailed blueprint), and physical (implementation details).
• 📈 Conceptual data models are used for high-level communication with stakeholders, focusing on entities and their relationships.
• 🛠️ Logical data models define structure and constraints, used by designers and developers as blueprints for databases.
• 💾 Physical data models represent the actual implementation of data, with details like data types, keys, and indexes.
• 🌟 Star schema is a simple data model with a central fact table surrounded by dimension tables, commonly used for small to medium datasets.
• ❄️ Snowflake schema is more complex, normalizing dimensions into smaller sub-dimensions, ideal for large datasets to avoid duplication.
• 📦 Dimension tables describe entities like customers or products, while fact tables contain events or transactions, linking dimensions together.
• 🚀 Star schema is crucial in business intelligence (BI) and analytics, frequently used in tools like Tableau or Power BI.

Q & A

What is data modeling?

Data modeling is the process of organizing and representing data in a clear and understandable way. It defines entities, attributes, and the relationships between entities to help both people and programs understand the data more easily.

What are entities and attributes in a data model?

Entities are objects or events, such as customers or orders, while attributes are the information related to those entities, like a customer's first and last name.

What are the three types of data models, and how do they differ?

The three types of data models are: 1) Conceptual data model: a high-level representation to show the big picture, mainly for stakeholders and business analysts. 2) Logical data model: more detailed, defining entity structures and relationships, used by database designers as a blueprint. 3) Physical data model: includes technical details like data types, used by developers to implement the database.

What are fact and dimension tables in a data model?

Fact tables store events and transactions, such as sales orders, and often include dates and measures like quantities and profits. Dimension tables describe entities, such as customers or products, and contain descriptive information like names or categories.

How do star and snowflake schemas differ?

In a star schema, there is a central fact table surrounded by dimension tables, forming a star-like shape. In a snowflake schema, the dimensions are further normalized into sub-dimensions, which reduces redundancy but increases complexity.

When should a star schema be used, and when is a snowflake schema more appropriate?

A star schema is typically used for smaller or medium-sized datasets as it is simple and easy to understand. A snowflake schema is used for larger datasets where normalized tables reduce data duplication and optimize storage.

What key elements are present in fact tables?

Fact tables typically contain foreign keys that link to dimension tables, dates for tracking events, and numeric measures such as sales, quantities, or profits.

How do you determine if a table is a fact or a dimension table?

If the table contains information about a physical person or object, like customers or products, it is a dimension table. If the table contains events or transactions with associated dates and measures, it is a fact table.

Why is data modeling important for business intelligence and analytics?

Data modeling helps organize data in a way that makes it easier to query, analyze, and visualize, which is crucial for making informed business decisions and improving performance.

Does Tableau use all types of data models?

Tableau adopts both the logical and physical data models in its data sources but does not include conceptual data models.

Outlines

00:00

🧠 Introduction to Data Modeling and Visualization Tools

In data projects, data is typically stored across many tables within data warehouses or lakes. Visualization tools like Tableau or Power BI require connecting and combining these tables into a unified data model. Data modeling organizes and represents data in a clear way, making it easier for both people and programs to understand, thus improving decision-making and business performance. Key concepts include entities (such as customers, products, or events) and their attributes (like first name, last name), as well as the relationships between entities.

05:01

🔍 The Three Levels of Data Models

There are three types of data models, each representing different levels of abstraction. The first is the conceptual data model, a high-level overview used to explain key entities and relationships, mainly to business stakeholders. The second is the logical data model, which provides more detail about data structure, constraints, and relationships, often serving as a blueprint for developers. Finally, the physical data model shows the actual implementation details, including data types, keys, and indexes, and is used by developers to build and manage databases.

⭐ Introduction to Star and Snowflake Schemas

For analytics and business intelligence, special data models are needed to optimize for queries and analysis. The star schema has a central fact table connected to dimensional tables that hold descriptive data. It’s simple and easy to use, making it ideal for small to medium datasets. The snowflake schema is a more complex model where dimensions are broken into sub-dimensions, reducing duplication and saving storage. This structure is more suitable for large datasets.

📊 Star Schema Overview: Facts and Dimensions

In the star schema, tables are classified into two types: dimensions and facts. Dimension tables, like 'customers' and 'products,' hold descriptive data about people or objects. Fact tables, like 'orders,' store events or transactions and include numeric data such as sales or quantities. Fact tables link to dimensions and contain time-related data, forming the core of the data model. Dimensions are smaller, while fact tables are typically larger due to the amount of transactional data they contain.

Keywords

💡Data Modeling

Data modeling is the process of organizing and representing data in a structured, clear, and understandable way. It involves creating entities, attributes, and relationships that describe how data elements are connected. In the video, data modeling is highlighted as a foundational step for visualizing and analyzing data using tools like Tableau and Power BI.

💡Entities

Entities are the key components or objects in a data model, representing real-world items like customers, products, or events. In the video, examples of entities include customers and orders. Each entity holds attributes that describe it, such as the first name and last name of a customer.

💡Attributes

Attributes are the specific details or characteristics of an entity. For example, the first name, last name, and country of a customer are attributes in the customer entity. The video explains how attributes provide additional information about each entity in a data model.

💡Relationships

Relationships define how different entities in a data model are connected. For instance, a customer entity might be related to an order entity through a customer ID. In the video, relationships are critical for creating a visual data model that helps in understanding and analyzing data.

💡Conceptual Data Model

A conceptual data model is a high-level representation of the data that outlines the most important entities and their relationships without going into technical details. In the video, this model is used to help business analysts and stakeholders understand the 'big picture' of how data is organized.

💡Logical Data Model

A logical data model provides more detail about how data is structured, including the attributes of each entity and the constraints on relationships. This model serves as a blueprint for database designers and developers. The video highlights it as an intermediate step between the conceptual and physical models.

💡Physical Data Model

The physical data model is the most detailed level, showing how data is actually stored in a database, including data types, keys, and indexes. In the video, this model is crucial for developers who are responsible for implementing the database.

💡Star Schema

A star schema is a data model that consists of a central fact table surrounded by dimension tables, forming a star-like shape. The fact table stores events (e.g., sales) while the dimension tables store descriptive information (e.g., customers, products). The video uses the star schema as an example of a simple and easy-to-understand model, often used in smaller datasets.

💡Snowflake Schema

A snowflake schema is a more complex data model in which dimension tables are normalized, breaking them into smaller sub-dimensions to reduce redundancy and storage space. The video contrasts the snowflake schema with the star schema, explaining that it is often used with large datasets.

💡Fact Table

A fact table in a data model stores quantitative data, such as sales amounts or transaction quantities, and connects to multiple dimension tables. The video uses an example of a fact table containing sales, quantities, and profits, illustrating its role in linking various dimensions like customers and products.

Highlights

Data modeling organizes and represents data in a clear way, with entities like customers, products, and events, each having attributes like first name and last name.

Entities in data models are connected through relationships, helping both people and programs understand data for better business decisions.

Three types of data models: Conceptual (high-level overview), Logical (detailed structure with constraints), and Physical (implementation details).

Conceptual data models help business analysts and stakeholders understand the overall structure, showing entities and relationships without implementation details.

Logical data models define the attributes and relationships between entities in more detail, used as a blueprint by database designers.

Physical data models focus on technical implementation, specifying details like data types, primary and foreign keys, and indexes for developers.

Star schema is a popular data model for analytics, consisting of a central fact table surrounded by dimension tables forming a star shape.

In star schema, fact tables contain events and measures like sales, while dimension tables hold descriptive information about entities like customers and products.

Snowflake schema is a more complex variation of the star schema, where dimensions are normalized into sub-dimensions, reducing data duplication.

Star schema is easier to understand and works well for small to medium datasets, while snowflake schema is more efficient for large datasets by reducing storage space.

Fact tables in star schema connect dimension tables and contain key attributes, dates, and numeric measures, representing business events like orders or transactions.

Dimension tables store information about people or objects, such as customers or products, and are usually smaller in size compared to fact tables.

Fact tables are typically large and represent events or transactions, making them central to connecting dimensions in the data model.

The data model used in the transcript is based on the star schema, with customer and product dimensions and an order fact table.

Understanding star and snowflake schemas is crucial for business intelligence and analytics, especially when working with tools like Tableau and Power BI.

Transcripts

in real projects your data can be stored

typically in data warehouses or data

leaks inside many many different tables

and the first step in any visualization

tools like Tableau or power bi is to

connect those tables and combine them in

one big data model

so let's start with the question what is

data moduling data modulink is the

process of organizing and representing

data in a clear and understandable way

each data model has entities entities

could be things like customers and

products or events like orders and

inside those entities we have

informations and we call them attributes

like the first name and the last name

inside the entity customers and we

describe in the data model how those

entities are connected or related to

each other's and we call it

relationships this data model this

visual representation of the data makes

it easier for us and for programs to

understand the data which is really

important for making decisions and

improving performance of the business

foreign

so we have three different types of data

models at different levels of

abstraction first we have the conceptual

data model this type is high level

representation of the data model without

going in details on how the data model

is implemented it's like a map that

shows the important entities and the

relationships and we usually use this

type to explain the data models to

business analysts and stakeholders to

understand the big picture of the data

the second type is The Logical data

model in this data model we go more in

details on how the data is structured

and organized we Define in this model

that builds of each entity and it

includes as well constraints and more

details about the relationships between

the entities this data model is usually

used by database designers and

developers as a blueprint for the

implementations and the third type is

the physical data model this type

represents the actual implementations of

the data model it includes all the

technical details about how to store the

data like the data types of the

attributes the primary and foreign Keys

indexes and so on this data model is

used by developers to create and manage

the databases alright so let's summarize

the conceptual data model shows the big

picture of the data The Logical data

model provide a blueprint for the

implementations and the physical data

model shows how the data is implemented

in the databases and Tableau did adopt

both the logical and physical data

models in the data sources but we don't

have conceptual data model in Tableau

don't worry about it I will show you

more detail later

alright so now for analytics especially

for data warehousing and business

intelligence we need special data models

that are optimized for queries and for

analytics it should be flexible and easy

to understand and for that we have two

special data models first one is the

star schema star scheme has a central

fact table and surrounded by dimensional

tables the fact tables contains events

and the dimensions holds descriptive

information the relationship between the

fact and the dimension tables form a

star shape and that's why we call it a

star schema and the other data model we

call it snowflake schema it is very

similar to Star schema but the

dimensions here are breaking down into

sub Dimensions normalize tables or

Dimensions means that those tables are

broken down into small pieces to avoid

having big tables or big Dimensions

which leads to many data duplications

and slow performance the shape of these

data models look likes a snowflake

so star schema is a simple and easy to

understand data model and we usually use

it if our data set is a small or medium

in the other hand the snowflake schema

is more complex but it eliminates the

duplicates and reduces the storage

spaces and we usually use it if we have

a large data sets alright so the data

sets that I've prepared for this Tableau

course are using the star schema data

model just to keep it simple and easy to

follow

foreign

so our data model has a name and we call

it star schema if you're gonna work on

read projects you're gonna hear about

the star schema a lot so star schema has

mainly two types of tables facts and

dimensions for example we have the table

customers it describes each customers by

their first name last name country and

so on so customers is a dimension table

and we have another dimension table in

our data model it is the products so

product stable describes as well each

products by their name and category so

it is as well a dimension alright so now

let's talk about the second type of

tables in the star schema we have the

facts for example let's have a look at

the big table in the middle we can see

three things you can see first a lot of

keys to the other dimensions we have the

order ID customer ID product ID and we

can see dates so we have the order date

the shipping date and the third thing we

can see a lot of numbers so we have

sales quantities profits we call them as

well measures so if you see those three

things that means we have an event or

fact table so facts Connect dimensions

together it has dates and as well

measures okay so to summarize how do we

decide if a table is dimension or fact

if you have a table that contains

informations about a physical person or

an object like employee customers

products then this table is a dimension

and usually they are small tables and in

the other hand if you have a table that

contains events for example we have

sales orders logs ATM transactions so

any tables that has events transactions

and has time in it we call it facts and

usually they are really huge tables okay

so in our data model in the data sets we

have two Dimensions we have the

customers and products and in the middle

we have our fact the orders alright so

now if you're here in your project

someone talking about star schemas and

so on you know exactly what they mean

it's very important Concept in analytics

and biworld if you are using Tableau or

power bi alright so with that you have

learned some important Concepts in data

moduling next we will learn the Tableau

Data model and the two layers physical

and logical layers and if you like my

content and you want to support the

channel then I really appreciate it if

you support like and comment this really

gonna help the YouTube algorithm thank

you so much for watching and I will see

you in the next video bye

[Music]