Database Structures | CompTIA Tech+ FC0-U71 | 5.3

In this post, we’re going to explore the world of databases and discuss the differences between structured, semistructured, and non-structured databases.  We’ll also compare relational and non-relational databases, delving into the details of schema, tables, keys, and constraints for relational databases, as well as key/value stores and document databases for non-relational databases.

Understanding how different types of databases work is essential for managing, storing, and retrieving data efficiently in various computing environments.

Structured, Semistructured, & Non-Structured Databases

To begin, let’s define and compare the three main types of database structures:  structured, semistructured, and non-structured.

Structured Data

Structured data is the most organized form of data, and it fits into predefined models, often with rows and columns, like a spreadsheet or a relational database.

• Highly Organized:  Structured data follows a strict schema, meaning every data point fits a specific format, making it easy to query, manipulate, and analyze.
• Examples:  Customer records, sales data, financial data – all of these can be stored in tables with predefined fields like name, age, and address.
• Storage:  Structured data is often stored in relational databases, such as MySQL, PostgreSQL, or Microsoft SQL Server.

In structured data, because every piece of data has a clear, predefined model, tasks like querying or joining tables become highly efficient.

Semistructured Data

Semistructured data falls between structured and non-structured data.  It doesn’t adhere to a strict table format, but it still has some organizational properties that make it easier to analyze than non-structured data.

• Flexible Organization:  Semistructured data may have tags or keys that define certain data elements, but the structure isn’t rigid.  For example, some fields might exist in one record but not in another.
• Examples:  JSON (JavaScript Object Notation) and XML (Extensible Markup Language) documents are common examples of semistructured data.  In these formats, data can have varying attributes but still maintain a certain level of organization.
• Storage:  This data type is often stored in document databases like MongoDB or Couchbase.

Semistructured data is particularly useful for applications that require flexibility, such as web apps that handle dynamic content.

Non-Structured Data

Non-structured data, on the other hand, doesn’t have any predefined structure.  This type of data is more challenging to analyze and store in a traditional relational database.

• Non Fixed Schema:  Non-structured data is unorganized and can take any form.  It doesn’t fit neatly into rows or columns.
• Examples:  Examples include multimedia files (images, video, audio), social media posts, and sensor data.
• Storage:  Non-structured data is typically stored in specialized databases or file storage systems like NoSQL databases, Hadoop, or even cloud-based systems like AWS S3.

Non-structured data has become increasingly important as businesses deal with vast amounts of multimedia, user-generated content, and data from sensors or IoT devices.

Relational Databases

Now that we understand the different types of data, let’s move on to relational databases, which are a popular choice for storing structured data.

What is a Relational Database?

A relational database is a type of database that stores data in a structured format, using rows and columns.  Data is organized into tables, and the relationships between the tables are defined using keys.

The most common relational database management systems (RDBMS) include:

• MySQL
• PostgreSQL
• Oracle Database
• Microsoft SQL Server

Relational databases excel in situations where data consistency and relationships between data points are critical.  Let’s explore some key concepts that define relational databases.

Schema

A schema defines the structure of a relational database, specifying how data is organized into tables, fields, and relationships.  It acts as a blueprint for the database, determining what data can be stored and how it can be accessed.

• Tables:  The core of a relational database.  Each table contains records (rows) and fields (columns).
• Constraints:  Rules that ensure the integrity of the data.  For example, a constraint may enforce that a particular field cannot be left blank or that a field must contain unique values.

Schemas are important for maintaining data consistency and ensuring that all entries conform to the same rules.

Tables, Rows, and Columns

Relational databases organize data into tables, which consist of rows (also known as records) and columns (also known as fields).

• Rows:  Each row in a table represents a single record or entry.  For example, in a customer table, each row would represent a different customer.
• Columns:  Each column represents a specific attribute of the data, such as a customer’s name, address, or phone number.

This structure makes it easy to run complex queries and retrieve specific data efficiently.

Primary Key & Foreign Key

Two important types of keys in relational databases are the primary key and the foreign key.

• Primary Key:  A primary key is a unique identifier for each record in a table.  It ensures that no two records in the table are identical.  For example, a customer ID might be the primary key in a customer table.
• Foreign Key:  A foreign key is a field in one table that links to the primary key in another table.  It creates a relationship between the two tables.  For instance, an order table might have a foreign key linking it to the customer table to identify who placed each order.

By linking tables through foreign keys, relational databases can efficiently manage relationships between data.

Constraints

Constraints in a relational database help ensure data integrity by imposing rules on the data.

Common constraints include:

• NOT NULL:  Ensures that a column cannot have a null value.
• UNIQUE:  Ensures that all values in a column are unique.
• CHECK:  Ensures that all values in a column meet a specified condition.
• FOREIGN KEY:  Enforces the link between tables.

Constraints play a critical role in maintaining the accuracy and reliability of data in relational databases.

Non-Relational Databases (NoSQL)

Now, let’s switch gears and discuss non-relational databases, often referred to as NoSQL databases.  These databases are designed to handle different types of data, especially semistructured or non-structured data.

What is a Non-Relational Database?

Unlike relational databases, non-relational databases don’t rely on tables, rows, and columns.  Instead, they use more flexible data models.  This flexibility makes them ideal for storing large volumes of unstructured or semistructured data.

Common non-relational database types include:

• Key/Value Databases
• Document Databases

Non-relational databases are highly scalable and perform well in distributed systems.

Key / Value Databases

A key/value database is the simplest type of NoSQL database, where data is stored as a collection of key/value pairs.

• Key:  A unique identifier for the data.
• Value:  The data associated with the key, which can be any type of information such as text, numbers, or binary objects.

Key/value databases are often used for storing session data, configuration settings, or user profiles.  Popular key/value databases include Redis and DynamoDB.

Document Databases

A document database stores data in the form of documents, typically in JSON or BSON (Binary JSON) format.

• Flexible Schema:  Unlike relational databases, document databases don’t require a fixed schema, which makes them ideal for handling semistructured data.
• Nested Documents:  Documents can contain nested fields and arrays, allowing for a more complex and hierarchical data structure.
• Query Flexibility:  Document databases allow for rich querying, making them powerful for applications that need to search and filter based on various fields.

Popular document databases include MongoDB and CouchDB.

Comparison:  Relational vs. Non-Relational Databases

Now, let’s compare relational and non-relational databases.

Relational Databases

• Structured Data:  Relational databases are ideal for storing structured data with predefined relationships.
• ACID Compliance:  These databases are known for their ACID properties (Atomicity, Consistency, Isolation, Durability), which ensure data integrity.
• Fixed Schema:  A strict schema is used, which may require careful planning and upfront design.
• Use Cases:  Relational databases are used in situations where data consistency is critical, such as in financial systems or inventory management.

Non-Relational Databases

• Flexible Data Models:  Non-relational databases are better suited for handling semistructured or non-structured data.
• Schema-Less:  No predefined schema is required, allowing for more flexibility in handling evolving data.
• Scalability:  Non-relational databases are highly scalable, making them ideal for applications with large amounts of data or high traffic, such as social media platforms or content management systems.
• Use Cases:  Non-relational databases excel in environments where the data structure is constantly changing, or large-scale horizontal scalability is required.

Conclusion

In conclusion, we’ve explored the differences between structured, semistructured, and non-structured data, as well as compared relational and non-relational databases.

• Relational databases offer structured data storage with a fixed schema, keys, and constraints, making them ideal for applications that require data integrity and relationships between records.
• Non-relational databases, on the other hand, offer more flexibility for handling semistructured and non-structured data, making them perfect for modern applications that require scalability and flexibility.

Understanding these database structures is essential for the CompTIA Tech+ exam and for working effectively with different types of data in the real world.