Whether your next milestone as a CTO is to migrate your enterprise data to the cloud or launch a full-scale data warehousing operation, creating an integration model that standardizes the data stored in the source systems can simplify data management. Ultimately determining how the data stored will present the information, and how well that info will be used by the decision makers.
Data Modeling and What It Entails
The logical structure of a database, complete with its constraints and relationships, is known as a data model. The structure determines how the data is stored and how the system will access it. When conducting the 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, enterprises must have complete confidence in the input accuracy and reliability of their data, as an error during data input would naturally mean corrupted output as well as redundancies across the database.
Each instance of a database is 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 into one of the following three distinct layers, each with specific placement and function. Let’s delve into each layer separately:
Conceptual 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.
Characteristics
- Provides organization-wide coverage of the business concepts
- They cater to a specific business audience.
- The conceptual layer is built independent of any hardware specs, storage capacity, or software limitations. The focus is to represent the data as viewed in the real world.
Logical 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 physical model.
Characteristics
- Logical model lists the project’s requirements but depending on the scope it could also integrate with other data models.
- It is designed and developed independently from DBMS.
- The datatypes of the data elements have precise length.
Physical Model
The physical layer illustrates how to implement data model in a database management system. It streamlines the implementation methodology in the form of tables, indexes, partitioning, etc. The physical data model helps visualize the entire structure of the database.
Characteristics
- 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, technology used for the project, required data storage, and location.
- All columns for the sake of exact representation of the data must have precise datatype, default values, and length.
- Columns should have exact datatypes, lengths, and default values.
- Primary keys and foreign keys, access profiles, indexes, and authorization are pre-defined.
Approaches to Data Modeling
There are a variety of ways to approach the specific data modeling needs for your business, however the underlying concept of dividing the data into conceptual, logical, and physical layers remains similar for all types of data models. Let’s take a closer look at the most popularly used data models:
- Relational Modeling
- Hierarchical Modeling
- Network Modeling
- Object-Oriented Modeling
- Entity-Relationship Modeling
- The Object-Relational Modeling
Relational Modeling
Perhaps the most common approach used is the relational 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 table have rows and columns, upon which attributes can be set, for 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 in another table for creating link or for easy access, at this point it becomes a foreign key.
Hierarchical Modeling
This approach is perfect for information that deals in hierarchical data, it deals with creating efficiency and identifying redundancies in either the structure of your organization, your 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 into physical database. Primarily used by the IBM’s IMS (Information Management Systems) in the late 60s and early 70s, they’ve seem to become quite popular nowadays due to its few operational inefficiencies.
Network Modeling
In many ways the network model builds upon the hierarchical model by providing many-to-many relationships among linked records, which implies there can be multiple parents for a specific record. With the underlying concept of mathematical set theory, this model is built with complete sets of records that’re related.
Object-Oriented Modeling
There are two types of Object-oriented databases:
First, the multimedia database is one which incorporates media, it could be images, etc., that otherwise cannot be inserted in the relational database.
Second, 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 post-relational model, as it inserts into tables, but not limited as tables. Such models are called hybrid database models.
Entity-Relationship Modeling
Similar to the network data model, the entity-relationship model clearly captures relationships among real-world objects and entities. However, it isn’t physically tied to the structure of the database. Instead, it’s used for conceptually designing databases.
Object-Relational Modeling
This particular model sports 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.
Now let’s take a closer look at some of the software that’ll allow you to create anyone of the above database models.
Speed up Data Migration with Data Modeling Tools
The data modeling tools we’ll discuss in this section are versatile and feature-rich. Unique in one way or another these tools can perform an array of tasks to create complex and innovative data models. Peruse the list and select a software that best suits your organizational needs.
ER/Studio
Built primarily for database and data architecture design, the built-in features of this technology allow modelers to automate mind-numbing tasks and focus more on the accuracy of the data model. ER/Studio is best suited for desinging physical and logical layer of the model. Created by Embarcadero Technology, the tool also auto-generates code for a range of databases.
Software Features
- It supports scripting and code automation.
- ER/Studio ensures consistency across all databases and models.
- Supported formats include PNG, HTML, RTF, JPEG, Schema, XML, and DTD.
- For every change you make at the database level, this tool conducts an impact analysis.
PowerDesigner
The industry’s most powerful modeling tool to hit the market is arguably PowerDesigner. This particular software is the go-to solution for handling complex data. In addition, it offers powerful modeling tool, metadata management, link-and-sync technology, impact analysis, and web-based reporting.
Software Features
- Conducts impact analysis for integrated models enterprise-wide or on project.
- Enterprise architects and developers can share data via secure metadata repository.
- Creates a wizard-driven documentation for the system.
- Supports open software development environment.
- It has the ability to form connections between the data models, business languages, and requirements by utilizing the link-and-sync technology.
Sparx Systems Enterprise Architect
This tool allows you to create, share ideas, verify, create charts, workflows as well as data models . Designed for the Microsoft Windows OS, this tool is even compatible with on Linux OS through the Wine compatibility layer and as Mac CrossOver on Mac OS. The tool is extremely useful for project management as well.
Software Features
- The requirement management capability is built-in, so if you create too heavy a data model for your physical system specification to handle, it will warn you.
- The tool provides complete traceability from requirement to deployment.
- Supports multiple developmental languages and has a WYSIWYG editor.
- You can auto generate documentation of the model.
- In project management, you can use this tool to assign resources to their elements, measure efforts & risks, estimate size of project, and perform a plethora of other tasks.
- Conduct reverse engineering, create model-driven architecture, and dynamic model simulation.
SQL Developer Data Modeler
This particular data modeler is a free-to-use graphical tool by Oracle. It allows you to browse, create, and update different types of data models. Sporting reverse and forward engineering capabilities, this data modeler could be operated both on-premise and on the cloud.
Software Features:
- It works with 5 out of the 6 types of data modeling, namely Relational, Hierarchical, Network, Object-Oriented, Entity-Relationship, and the Object-Relational model .
- Conduct reverse engineering and impact analysis.
- Nest your diagrams by linking model diagrams from separate models together.
- Auto-generates reports.
Astera Centerprise
Astera Centerprise is a next-generation data integration platform that’s designed to support and sustain complex, high-volume data integrations needs of small and large organizations. Learn more here.
Software Features
- Astera Centerprise offers a unified and streamlined interface, supports most used data modeling capabilities.
- Offers ETL (Extract, load, Transformation) capabilities.
- Profile the data you collect and store them logically.
- 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.
Benefits of Modeling Data
Well designed data models tend to compound with other technologies such as fintech to offer great deal 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. Your organization can experience perks in the following areas
- Performance
- Cost
- Efficiency
- Quality
To conclude this discussion on data modeling tools, we can confidently say that these are among the best tool for data modeling in terms of consistency, supporting large number of databases. While also working with large and complex data, all in all they’re all great tools with greater functionalities to offer to businesses of all sizes.
The higher the complexity of the data would mean higher 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.
Originally published at https://www.astera.com on August 29, 2019.
