Difference Between Entities vs Attributes in a Data Model

The entity-relationship model is composed of different elements. The most important types of elements are the entities and their attributes. In this article, we go into detail about the distinction between them, the role they play in a data model, and the steps to create them in Vertabelo.

What is Data Modeling?

Data modeling is carried out by a database architect or database modeler. It is the process of creating a diagram that lists the set of data structures that are going to be the data backbone of a software application. These data structures are the entities and attributes in a data model.

SOMETIMES, IT IS AN EASY PROCESS, and you come up with the ideal solution quite quickly. Other times, it is a difficult process involving numerous hours of designing and discussing with the stakeholders and business analysts. You want to make sure the data model can support all of the business processes that need modeling.

Thinking about your data and how it moves through your relational database before starting to build your application helps you spot places for improvement. This is a key BENEFIT OF DATA MODELING and one of the aspects I LIKE ABOUT IT. It offers a big advantage by laying out all of the details of the data that goes into your application. It helps you avoid rework in the future when new information about the business is added.

Building a RELATIONAL DATA MODEL OF A DATABASE, sometimes called the entity-relationship model, is done by going through a set of predefined phases of the data model. By doing so, you are almost guaranteed to build a resilient data model that fits your needs.

The final relational data model is simply a set of tables with relationships and constraints among them. Data is stored in their respective tables called entities, so that it is organized and easily accessible by using the least amount of resource.

As mentioned before, building the entity-relationship model is done by going through three major design phases that build THREE INTERMEDIATE DATA MODELS: the conceptual data model, the logical data model, and the physical data model.

All of these intermediate data models are VARIATIONS OF AN ENTITY-RELATIONSHIP MODEL, each with the same information but at different levels of detail.

Conceptual Data Model

We always start the data model design process with a conceptual data model. The goal of this phase is to sketch out a simplified version of the entity-relationship model, composed of only the entities and their relationships.

Designing a conceptual data model for a store in VERTABELO may be represented as we have below. In this model, we have identified the main entities and the relationships among them.

entity relationship model

Logical Data Model

Once we define our conceptual data model by representing the entities, we add more information about these entities in the form of attributes. We also identify which attributes can represent the primary identifier for each row, marked by "PI". Once we have completed these steps and expanded our entity-relationship model, we have essentially built the LOGICAL DATA MODEL.

entity relationship model

Physical Data Model

The final iteration of an entity-relationship model is the physical data model. This is a representation of the final form of the model, applied to the target database where the model is deployed.

In this step, we create a diagram very similar to the one in the logical model, but all of the entities and attributes in a data model are converted into their final form of tables and columns, respectively. We also convert the data types from the logical model into the corresponding data types of the target database.

The coolest thing: VERTABELO MODELER CAN AUTOMATICALLY GENERATE THIS PHYSICAL DATA MODEL with just a few clicks! It automatically chooses the right data types that are compatible with the target database engine.

entity relationship model

Entities and Attributes in a Data Model

When designing any data model, the main focus is on identifying entities and attributes. Building any entity-relationship model involves identifying what the entities are and what the attributes are, all from the requirements.

The distinction between entities and attributes is very important, and there needs to be a balance between the number of entities and the number of attributes in your data model. It is possible to create an entity-relationship model with only entities or one with just a single entity and multiple attributes. But this is generally not the case, since it’s best to APPLY THE APPROPRIATE LEVEL OF NORMALIZATION whenever creating an entity-relationship model.

Building out your database requires you to understand your problem domain very well. Once you have a solid understanding of that, you can correctly identify the entities and the attributes that will go into the design of the entity-relationship model.

What Is an Entity?

In your entity-relationship model, AN ENTITY is a representation of either a physical object from the real world or a singular concept that is generally very well defined and delimited.

In the conceptual data model, we aim to identify the main entities in a problem domain and represent them. In our previous conceptual model diagram, we had three entities identified, all highlighted with different colors in the image below. These represent the main entities that interact in the context of a store that sells different products.

entity relationship model

What Is an Attribute?

That said, defining every detail of a problem as an entity is not a good practice. This is why we define an additional set of details that are specific to each entity and are inherited by each individual of the same entity type.

These additional details are called attributes. They define the real entity or concept much better. Expanding the diagram of just the entities as we have above, the attributes complete the final form of an entity. When we identify the entities and their attributes as well as the most important relationships between the entities, we have built a minimal entity-relationship model.

entity relationship model

Once the attributes are defined inside an entity, we can now translate them to their representation in a relational database. You see in the diagram above that we have a physical data model of the store sales scenario, with entities and attributes.

This is where the entities are mapped to tables and their attributes become columns in the tables. The attributes also get appropriate data types and associated constraints to represent the entity-relationship model correctly.

How to Define Entities and Attributes in Vertabelo

Most of the diagrams shown above have been created in Vertabelo Modeler. We are going to quickly show you how you recreate this entities-and-attributes example in an entity-relationship model in VERTABELO MODELER.

After logging into Vertabelo Modeler, start by creating a logical data model on your Documents page. If this is your first time working with Vertabelo, we have a TUTORIAL SHOWING YOU HOW TO CREATE A LOGICAL MODEL.

Once you have the document created, the first step is to add a new entity. This is done by pressing this button and then clicking anywhere on the design canvas.

entity relationship model

This automatically creates a new entity, usually called entity_1 by default.

entity relationship model

To rename it, select the entity on the canvas. Then look at the right side of your screen, where the entity properties panel is situated. You can add the new name here as well as add any attributes to this entity by clicking on “+ Add attribute”.

entity relationship model

For recreating the Product entity in our new diagram, the configuration for the entity and the attributes looks like this:

entity relationship model

As you see, you also need to add the data type for an attribute whenever defining a new one for an entity. By pressing the small settings button next to each Data type, you see all the available data types for an attribute. Please note that these are general logical data types for the attributes since this is a logical data model. They are not the final data types of the columns in a physical data model.

entity relationship model

Also, it’s a good practice to choose whether that attribute is mandatory when new instances of data are inserted for that type of entity. This is denoted by the column of checkboxes with "M" in the header.

Even more importantly, in general, every entity that eventually becomes a table needs to have a primary identifier. This is the next column of checkboxes with "PI" in the header.

Whenever designing your entity-relationship model, remember to define THE NAMING CONVENTION you are going to use throughout your model. It’s important to keep consistency in the naming of multiple entities and attributes. As time goes on and the model grows in size, you need to be able to identify what type of information is stored there from the name quickly. Also, it makes querying much easier when entities and attributes follow a similar naming pattern.

A Finished Entity-Relationship Model

With all of this information, you can now confidently build your entity-relationship model using Vertabelo Modeler. Not only does it have all the features you need to create the entities, attributes, and relationships, but it also has MANY OTHER FEATURES that allow you to build a data model down to the finest detail possible.

If you want to explore all of the features the user interface provides, we have an article explaining all of them HERE. We also have SOME TIPS FOR YOU BEFORE YOU START your data modeling journey. So, rest assured you’re on the right path when you use them.

Original article source at: https://www.vertabelo.com/

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Difference Between Entities vs Attributes in a Data Model
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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Gerhard Brink


Data Science vs Big Data: Difference Between Data Science & Big Data

In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines.

When working with all terminology related to data, it is essential to have a clear understanding of the different scope of work related to it. In this article, we’ll discuss the differences between Big Data and Data Science. Though these terms are interlinked and often used interchangeably, there’s a vast underlying difference between them in all aspects.

Let us begin by defining the two terms.

Big Data is a standard way to define it is as an assortment of data which is too large to be stored or processed using the traditional database systems within a given period. A common misconception while referring to it is when the term is used to refer to data whose size of the volume is of the order of terabytes or more. However, it is a purely contextual term. For example, even a file of 250MB is Big Data in the context of an email attachment.

Data exhibits key attributes that must be taken into consideration when processing a dataset. They are most commonly known as the 5 Vs. Each of the Vs has specific implications in terms of handling them, but, when all of them are seen in combination, they present even bigger challenges.

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How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

  • Building/collecting data
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  • Organizing data

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Gerhard  Brink

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

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Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
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The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.

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