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The CompTIA IT Fundamentals+ PrepCast is your step-by-step guide to building a rock-solid foundation in IT, covering hardware, software, networking, databases, and security in a way that’s clear and approachable. Designed for beginners and those looking to prepare for more advanced certifications, each episode turns exam objectives into practical lessons you can follow with confidence. Produced by BareMetalCyber.com, this series gives you the knowledge and momentum to pass the exam and launch your IT journey.
In this episode, we introduce non-relational databases, often referred to as NoSQL systems. These databases break away from the traditional structure of rows and columns found in relational models. Instead, they offer more flexible ways to store and retrieve data, making them ideal for fast-paced, evolving environments. You will learn about two of the most common types: key/value stores and document databases. Both models are important for understanding modern storage solutions and are included in Domain Five of the Information Technology Fundamentals Plus exam.
On the exam, you may be asked to identify differences between relational and non-relational systems, recognize use cases for each model, or match a description to the appropriate database type. The focus is on understanding the purpose and structure of these systems—not on installation, configuration, or query writing. You will not be required to set up a database, define JSON syntax, or manage collections. Instead, your goal is to learn how each model organizes data and when it is best used.
A non-relational database, sometimes called a NoSQL database, does not rely on fixed table structures with predefined schemas. Instead, these databases allow data to be stored in more flexible formats, such as key/value pairs or documents. This structure is useful when data types are constantly evolving, when speed is critical, or when scalability is required across large systems. These systems are designed for specific use cases where traditional relational models might be too rigid or slow.
One of the key differences between relational and non-relational databases is the lack of structured tables and relationships. Non-relational models do not require predefined columns or table joins. They store data in more flexible formats, which allows for quicker adjustments and fewer limitations. Relationships between data points are handled differently, often within the same document or through key references, rather than through foreign keys and relational links. This approach supports a broader range of applications.
The key/value database is the simplest type of non-relational model. It stores data as pairs, where a unique key is associated with a specific value. This model is very fast for retrieving individual items when the key is known. A common example might be username equals “jdoe” and password equals “one two three four.” In this case, the system stores the key “username” with the value “jdoe,” and the key “password” with the associated value. This simplicity makes key/value databases well suited for lightweight data retrieval tasks.
Key/value stores are commonly used in applications that require quick lookups, such as caching, session storage, or reading configuration settings. Because they are built for speed, these systems are often deployed in scenarios where performance is more important than complex relationships. Key/value stores are not designed to manage structured or related data, making them unsuitable for tasks like customer order histories or inventory systems. Their power lies in their speed and simplicity.
A document database is another type of non-relational model that stores data as structured documents. These documents often use JSON or XML formats, allowing for flexible, self-contained data records. Each document can contain all the necessary fields and values for a single item, such as a user profile or product listing. Unlike relational databases, document stores do not require all documents to have the same fields. One document might include an address, while another omits it entirely.
The structure of document databases is built around collections instead of tables. Collections contain multiple documents, but those documents do not need to follow the same format. For example, one user profile might contain fields for name, email, and age, while another only includes name and email. This makes document databases highly adaptable, especially for applications where data structures are expected to change over time or vary between users. They are often chosen for their ability to evolve with the application.
Examples of popular non-relational databases include Redis and DynamoDB for key/value storage, and MongoDB and Couchbase for document storage. While brand names may occasionally appear in examples or questions, the Information Technology Fundamentals Plus exam does not require you to memorize them. What matters is understanding the underlying model and its use case. Knowing that a system uses key/value pairs for fast access or documents for flexible structure is far more important than remembering specific software tools.
Non-relational databases are often used when speed, scalability, or flexibility is a higher priority than strict data consistency or relational logic. They are ideal for projects involving unstructured or semi-structured data, such as social media content, user-generated data, or application logs. They also support rapid development cycles, where data models change frequently. In addition, they are commonly used in real-time analytics, content management systems, and mobile app development where quick, schema-free storage is beneficial.
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When comparing relational and non-relational databases, it's important to focus on structure and flexibility. Relational databases use structured tables, fixed schemas, and relationships maintained by primary and foreign keys. In contrast, non-relational databases offer schema-free designs that allow for dynamic and evolving data models. They store data as key/value pairs or documents, which gives developers more flexibility when building systems that need to adapt quickly or handle a wide variety of data types.
One of the major advantages of non-relational databases is their scalability. These models are easier to scale horizontally, meaning new servers or nodes can be added to the system to handle increased demand. This type of scalability is especially useful for large-scale web applications and services that receive unpredictable amounts of traffic. Many cloud platforms support non-relational models specifically because of their ability to grow easily without requiring extensive reconfiguration.
Document databases commonly use JSON, which stands for JavaScript Object Notation. JSON is a human-readable format that can store structured information with nested fields. For example, a user document might include a name, an address object with multiple subfields, and a list of recent purchases. This format makes it possible to capture complex data in a single document without needing to spread it across multiple tables. JSON also supports data types like strings, numbers, arrays, and Boolean values, making it highly versatile.
A key feature of document databases is data independence. Each document is self-contained and does not rely on external tables or strict schema rules. This means that one document can be updated or changed without affecting the structure of others. This independence is valuable in systems where different records might need to store different types of information, such as user-generated content or varying customer preferences. It also simplifies updates and migrations because documents can evolve individually.
Common questions on the Information Technology Fundamentals Plus exam may describe a data structure and ask you to identify the appropriate database model. For instance, if a question says, “Which type of database stores information as JSON documents?” the correct answer would be a document database. If it asks, “Which model organizes data in key/value pairs for fast lookups?” then a key/value store is the best choice. Being able to match each storage model to its function is a key part of your exam readiness.
There are also specific tasks that will not appear on the exam. You will not need to install or configure a non-relational database, nor will you need to use a query language like MongoDB’s query syntax or write JSON. You will not be tested on software versions or performance tuning. Instead, the exam focuses on your ability to recognize how these databases work conceptually and what kind of data structures and use cases they are best suited for.
One major concept to understand is flexible schemas. In a relational database, you must define the structure of a table before entering data. Every row must follow the same format, and every column must be pre-defined. In contrast, non-relational databases, particularly document stores, allow each document to have different fields. This means there is no need to predefine what the structure will be, which is especially useful in environments where data requirements may change rapidly.
These models are increasingly common in business data systems. Key/value databases are frequently used for tasks that require quick access to simple information, such as caching login sessions or reading configuration settings. Document databases are often used for storing more complex information like user profiles, product catalogs, or content articles. Because each document can be tailored to a specific need, developers can work faster and make changes without breaking the entire system.
The reason the Information Technology Fundamentals Plus exam includes non-relational models is that they reflect the direction of modern IT environments. Many applications now run on cloud infrastructure, where scalability, speed, and flexibility are critical. NoSQL models provide the underlying structure for many of these systems. Understanding how they differ from traditional databases gives you a broader perspective and helps prepare you for a wide range of technologies you may encounter in your career.
To summarize the key points, key/value and document databases fall under the category of non-relational or NoSQL models. Key/value stores offer simplicity and speed for retrieving data by unique identifiers. Document databases provide a more structured but flexible model using formats like JSON to store self-contained records. Both types support scenarios where relational models are too rigid or complex. On the Information Technology Fundamentals Plus exam, you should be able to recognize these models, their structures, and their most common use cases.
In conclusion, key/value and document databases represent two widely used types of non-relational data storage. They provide faster performance and greater flexibility than traditional relational models, especially in scalable, cloud-based, or rapidly changing environments. The Information Technology Fundamentals Plus exam will ask you to distinguish these models by structure and purpose. By mastering their characteristics, you strengthen your foundation in modern IT data management and prepare yourself for future learning in database technologies.