Introduction to data modeling part 1

Common Questions That many people may ask !

Why can’t everything be stored in a giant Excel spreadsheet?

There are limitations to the amount of data that can be stored in an Excel sheet. So, a database helps organize the elements into tables, rows, and columns, etc. Also reading and writing operations on a large scale is not possible with an Excel sheet, so it’s better to use a database to handle most business functions.

Does data modeling happen before you create a database, or is it an iterative process?

It’s an iterative process. Data engineers continually reorganize, restructure, and optimize data models to fit the need of the organization.

How is data modeling different from machine learning modeling?

Machine learning includes a lot of data wrangling to create the inputs for machine learning models, but data modeling is more about how to structure data to be used by different people within an organization. You can think of data modeling as the process of designing data and making it available to machine learning engineers, data scientist, business analytics, etc. So, they can make use of it easily.

What is data modelling?

Data Modeling is the process of creating a simplified diagram of a software system and the data elements it contains, using text and symbols to represent the data and how it flows. Data models provide a blueprint for designing a new database or reengineering a legacy application. Overall, data modeling helps an organization use its data effectively to meet business needs for information. A data model can be thought of as a flowchart that illustrates data entities, their attributes, and the relationships between requirements for applications and identify errors in development plans before any code is written.

Why is data modeling done?

Data modeling is a core data management discipline. By providing a visual representation of data sets and their business context, it helps pinpoint information needs for different business processes. It then specifies the characteristics of the data elements that will be included in applications and in the database or file system structures used to process, store, and manage the data. Data modeling Can also help establish common data definitions and internal data standards, often in connection with data governance (DG is the process of managing the availability, usability, integrity, and security of the data) programs. In addition, it plays a big role in data architecture processes (DAP is a discipline that documents an organization’s data assets, maps how data flows through IT systems and create a conceptual data management framework). Data Models are a key data architecture component, along with data flow diagrams (DFD is a graphical or visual representation using a standardized), architectural blueprints, a unified data vocabulary and other artifacts.

What are the different types of data models?

Data modelers use three types of models to separately represent business concepts and workflows, relevant data entities and their attributes and relationships, and technical structures for managing the data.

The models Typically are created in a progression as organizations plan new applications and databases. These are the different types of data models and what they include:·

1- Conceptual Data Model:

This is a high-level visualization of the business of analytics processes that a system will support. It maps out the kinds of data that are needed, how different business entities interrelate and associated business rules. Business executives are the main audience for conceptual data models, to help them see how a system will work and ensure that it meets business needs. CDM aren’t tied to specific database or application technologies.

2-Logical Data Model:

Once a conceptual data model is finished, it can be used to create a less-abstract logical one. It shows how data entities are related and describe the data from a technical perspective. Ex, they define data structure and provide details on attributes, keys, data types and other characteristics. The technical side of an organization uses logical models to help understand required application and database designs. But like CDM, they aren’t connected to a particular technology platform.

3-Physical Data Models:

A logical Models serves as the basis for the creation of a physical data model. Physical models are specific to the database management system (DBMS) or application software that will be implemented. They define the structures that the database or file system will use to store and manage the data. That includes tables, columns, fields, indexes, constraints, triggers, and other DBMS elements. Database designers use physical data models to create designs and generate schema for databases.

So, the correct order of the data modeling is Doing

1- Conceptual Data Model Design

2- Logical Data Model Design

3- Physical Data model Design