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The What, Why, and How of Data Modeling Tools

By |2022-04-26T10:36:36+00:00August 29th, 2019|

Whether your next objective as a CTO is to migrate your enterprise data to the cloud or launch a full-scale data warehousing operation, creating an integration model will definitely speed things up for you. Ultimately determining how the data stored will present the information and how well the decision-makers will use that info. This is where data modeling management tools come in. In this article, we will see:

What is Data Modeling and What Does it Entail?

Let’s begin by understanding the most widely agreed-upon data model definition.

A data model is basically a visual representation that describes the connections between different data points and structures stored in an information system. The structure determines how the data is stored and how the system will access it. Efficient data modeling software is needed for an appropriate structure of the data. When conducting due diligence about leveraging your data to fuel business decisions, the integrity of said data is a critical prerequisite.

Prior to computing their data for valuable insights, business analysts must have complete confidence in the input accuracy and reliability of their data. Any errors during data input would naturally mean corrupted output as well as redundancies across the database. There are various open-source or free data modeling tools available as well. Some prominent data model examples that are open-source include DB designer, Archi, and Oracle SQL Developer.

Each instance of a database is more or less identical. Relationships and rules are designed and programmed into the database by a modeler. An ideal programmer for this job must grasp this and formulate a plan to carry out the task accurately and efficiently.


The Three Layers of a Data Model

So, what do you plan to do with every spec of organizational data your company has accumulated over the years? Do you aim to migrate to a new system or maybe upgrade an existing one, or perhaps create an insight-yielding data warehouse? Either way, your data is going to be structured using a data warehouse modeling tool into one of the following three distinct layers, each with specific placement and function. Let’s delve into each layer separately:

Conceptual Data Model

This is the primary level of the model that defines the structure of the data according to the business requirements. It focuses on business-oriented attributes, entries, and relationships.


  • Provides organization-wide coverage of the business concepts
  • They cater to a specific business audience.
  • The conceptual layer is built independently of any hardware specs, storage capacity, or software limitations. The focus is to represent the data as viewed in the real world.

Logical Data Model

This layer is more complex and structured than the conceptual layer. It contains information about how the model should be implemented by defining the structure and relationships of the data elements. The main advantage of the logical model is that it provides a firm foundation for the logical and physical data model.


  • The logical model lists the project’s requirements, but it could also integrate with other data models depending on the scope.
  • It is designed and developed independently from DBMS.
  • The data types of the data elements have a precise length.

Physical Data Model

The physical layer illustrates how to implement a data model in a database management system. It streamlines the implementation methodology in the form of tables, indexes, partitioning, etc. The physical data model diagram helps visualize the entire structure of the database.


  • The physical model lists the needs of a single project, but depending on the scope of the project, it could also integrate with other physical models.
  • This model contains the table of relationships between the data, addressing the nullability and cardinality of said relationships.
  • It is designed and developed specifically for a specific DBMS version, the technology used for the project, required data storage, and location.
  • For the sake of exact representation of the data, all columns must have a precise data type, default values, and length.
  • Columns should have exact data types, lengths, and default values.
  • Primary keys and foreign keys, access profiles, indexes, and authorization are pre-defined.

Data Modeling Techniques to Boost Your Business | Blog | Whatagraph

Data Modeling Techniques (Source: Whatagraph)

Approaches to Data Modeling

There are a variety of ways to approach the specific modeling needs for your business with an efficient database modeling tool. However, the underlying concept of dividing the data into conceptual, logical, and physical data model layers remains similar for all types of data models. Let’s take a closer look at the most popularly used types of database models:

  • Relational Modeling
  • Hierarchical Data Modeling
  • Network Data Modeling
  • Object-Oriented Modeling
  • Entity-Relationship Modeling
  • The Object-Relational Modeling

Relational Modeling

Perhaps the most common approach used in data modeling is the relational data model, introduced in the 70s; it was integrated with SQL (Structured Query Language). This model uses a collection of relations to represent the database by sorting the data on the tables based on the relationships defined.

Each of these tables has rows and columns, depending on which attributes can be set, e.g., birth date, zip, or prices. A specific attribute or even a combination of them can be selected as the primary key. The primary key can be used or referred to in another table for creating links or for easy access; at this point, it becomes a foreign key.

Hierarchical Modeling

This approach is perfect for information that deals with hierarchical data; it creates efficiency and identifies redundancies in your organization’s structure, logistical, and more; the applications are endless. Here every entry of a record has but a single root or parent.

These records are entered in a specific order; the same order is then used for storing the data in the physical database. Primarily used by IBM’s IMS (Information Management Systems) in the late 60s and early 70s, they’ve seemed to become quite popular nowadays due to their few operational inefficiencies.

Network Data Modeling

In many ways, the network data model builds upon the hierarchical model by providing many-to-many relationships among linked records, implying multiple parents for a specific record. With the underlying concept of mathematical set theory, this model is built with complete sets of records that are related.

Object-Oriented Modeling

There are two types of Object-oriented databases:

First, the multimedia database is one that incorporates media; it could be images, etc., that otherwise cannot be inserted in the relational database.

Second, a hypertext database is one that allows any object to be linked to another object. It’s a lifesaver for organizing data that’s disparate; however, this isn’t for performing numerical analysis.

The object-oriented database model is perhaps best known for the post-relational model, as it inserts into tables but is not limited to tables. Such models are called hybrid database models.

Entity-Relationship Modeling

Similar to the network data model, the entity-relationship model clearly captures relationships between entities and objects in the database. However, it isn’t physically tied to the structure of the database. Instead, it is used for conceptually understanding database design and distinguishing entity types.

Object-Relational Modeling

This particular model displays advanced functionalities of its own along with the simplicity of the relational model, making this variation the object-relational model. This type of data model is considered to be a hybrid model that allows modelers to incorporate new objects in a familiarly structured environment.

Benefits of Modeling Data

The right data modeling software will result in well-designed data models that tend to compound with other technologies such as fintech to offer plenty of insights that make your organization competitive. Enabling data-driven insights about your employees, customers, competition as well as stockholders is what data modeling is all about. When using an efficient data modeling management tool, your organization can experience perks in the following areas:


  • A well-designed data model operates really fast and makes database tuning easier.


  • Due to data modeling, oversights and errors are caught early on, protecting you from expensive setbacks in the future.


  • A poorly built data model is equivalent to an intersection with no traffic lights. In contrast, a well-designed model allows data to flow smoothly and swiftly while Catering to analysis requests and performing calculations on-demand and in real-time.


  • Make your data more statistically consistent and significantly reduce computing errors.

To conclude this discussion on data modeling tools, we can confidently say that these are among the best data warehouse modeling tools in terms of consistency, supporting a large number of databases. While also working with large and complex data models, all in all, they’re all great modeling tools with greater functionalities to offer to businesses of all sizes.

The higher the complexity of the data, the higher the cost to set up and maintain it. A data model with an optimally constructed data structure, on the other hand, will help you get rid of excess tables of data, significantly reducing the cost incurred, saving resources for other endeavors. It also aids in documenting the data map for the ETL process.

Automated Data Warehousing Tool – ADWB

Astera DW Builder is a next-generation data warehousing platform that’s designed to support and sustain complex, high-volume data warehousing needs of small and large organizations. With ADWB, you can create data models from scratch or reverse engineer them.

Software Features

  • ADWB offers a unified and streamlined interface, supports the most used data modeling capabilities.
  • Offers ETL (Extract, Load, Transformation) capabilities.
  • Supports intelligent data mapping.
  • Workflow orchestration features with the ability to create branches and dependencies.
  • Schedule specific jobs for a later date to be completed automatically, or simply create a trigger protocol.