What Is a Database? An In-Depth Guide

A database is a systematically organized collection of information, typically in rows and columns, that you can sort through, search, and update. It holds all sorts of data, including text like words and numbers, photos, videos, and files.

To process and retrieve a database, you use software called a database management system (DBMS), with a programming language, like structured query language (SQL), in charge of performing queries.

In today’s data-driven world, databases facilitate reliable and accurate data retrieval and storage. As such, they have a key role in the day-to-day business operations and decision-making.

In this section, we’ll talk about how databases work. The process taking place under the hood is rather interesting — at least for us tech geeks.

Historical Background

If we’re going strictly by the book, databases go way back to information written down in journals stored in libraries and filing cabinets. As for their electronic form, the birth of the computer in the 60s changed everything.

The first computerized databases were navigational. Ironically, these were difficult to navigate, as you had to go through their entirety to find specific information.

The two main models of the navigational database were the hierarchical model and the network model — both superseded by the relational database in the 1970s.

In the 1980s, the now-ubiquitous SQL became the standard query language, followed by the rise of object-oriented databases in the 1990s. The advent of the Internet fueled the rapid database growth, supplemented with automated software a decade or so later.

These days, not only do modern databases store humongous amounts of data in the cloud, but they also leverage advances in machine learning and deep neural network models to make analytics and query understanding more robust.

Components

If you think a database consists of tables, records, fields, and whatnot, you’d be half right.

These are essential parts of relational databases (the most widely used type of database), but they are not universally found in every database. The specific structure depends on the database type and how it’s designed to manage data (we’ll get to this in a couple of minutes).

Instead, the consensus is that key database components include:

1. Data: Raw information that includes all the records and details stored in the database.
2. Hardware: All of the physical devices, such as storage drives, servers, and computers used to run and manage the database.
3. Software: All of the programs managing the databases, like the DBMS, operating system, and application programs used to access data.
4. Procedures: Instructions and rules that govern the design and use of the database.
5. Database access language: Programming languages employed to retrieve, insert, and delete data from the database.

There’s a point to be made that people — database admins, developers, and end users — are a key component, too, as we control and manage the database and perform various operations on it.

Perhaps the easiest way to understand databases is to think of them as super-spreadsheets since there are a lot of similarities.

That being said, keep in mind that data is not one-dimensional and limited to facts like words and numbers since numerous kinds of files are also considered data.

Basic Operation

First, data is gathered and formatted, then stored via a DBMS based on the desired database model — in tables with rows and columns or documents, graphs, or key-value pairs.

To query data, users employ a database access language, with the DBMS processing the query using indexes (specific data structures) to speed up data retrieval.

Once the requested data is located, the DBMS retrieves and delivers it to the user. Through user authentication and authorization, the system makes sure that only permitted individuals can view, modify, or delete data.

Database Management Systems (DBMSes)

As you can imagine by now, data can get pretty complicated (especially big data) to organize, access, and keep secure.

That’s why a DBMS is a key cog in the entire database machine — it acts as the linchpin between the database and its end users, allowing streamlined retrieval, updating, and management of how the information is organized and optimized.

But there’s more. A DBMS also facilitates oversight and control of databases through administrative operations like performance monitoring and backup and recovery. It helps manage data that is used across several applications or resides in multiple locations.

Naturally, different systems vary in how you can organize, apply, and scale them. The one you opt for will primarily depend on the location of your data, the database’s architecture, and your approach to scaling.

Popular DBMSs include MySQL, Oracle, PostgreSQL, Microsoft SQL Server, MongoDB, and Redis, to name a few.

Query Languages

No conversation about databases and queries can go without SQL, the granddaddy of database programming languages. Even though it was developed in the 70s, it’s still heavily used in relational databases to query, edit, and control access to data.

A lot of its appeal lies in the fact that SQL easily integrates with different programming languages like Java, C#, Python, and PHP.

Plus, it’s easy to learn as it uses common English keywords in its statements, making it easy to create SQL-based, high-performing data processing apps.

Though by far the most widely used language, SQL has alternatives that are largely developed for specific domains and use cases. These include IBM DB2, PRQL, Amazon RDS, DMX, GraphQL, N1QL, and others.

Transactions

In a database context, a transaction is a group of read-and-write operations that is treated as a single unit of work. It either completes fully or doesn’t, leaving the database in a consistent state.

The principle is the same as with a standard transaction: when you pay for something, the money either leaves your account or not, and there is no in-between state.

Why is this important to mention? A database is a shared resource, which means it’s common for many users to tinker around at the same time. To maintain data integrity, each translation has to have distinct properties colorfully labeled ACID:

• Atomicity: Every operation (such as reading, writing, updating, or deleting data) within a transaction is considered an indivisible unit. This characteristic safeguards against data corruption or loss.
• Consistency: Guarantees that transactions alter the database in a predefined and expected manner, thus ensuring that any data corruption or errors don’t compromise the integrity of your database.
• Isolation: Sees to it that concurrent transactions don’t influence or disrupt each other. Each operation appears to be happening sequentially despite their simultaneous execution.
• Durability: This guarantees that once a transaction is successfully executed, any changes made to data will be permanent, even in the event of a system failure.

Together, these ACID properties make certain that transactions leave the database in a valid state even if unexpected errors occur.

Though database management systems perform the same fundamental tasks, how they accomplish them varies due to different database structures. For the most part, databases are categorized per their organizational approach.

Relational Databases

A relational database is a type of database that stores and provides access to data points related to one another. Here, data is organized into tables containing information about each entity.

Each table represents a predefined category through rows and columns that hold attributes of the data. As every record typically has a value for every attribute, it’s easy to establish relationships between data points.

Each row in the table is a record with a unique ID called the key. For access to specific data, all you have to do is enter the key for the data that has been programmed to be related to that key.

This format of structured data is both efficient and flexible for processing information and formulating queries.

The top relational database systems are Oracle, MySQL, PostgreSQL, and Microsoft SQL Server.

NoSQL Databases

Commonly known as non-relational databases, the NoSQL (not only SQL) database type stores data in whatever format is best for that data.

If that sounds a bit vague, it’s because NoSQL databases often hold unstructured and semi-structured data, so it’s better to store an email as an email rather than dividing it into smaller elements, for example.

This dynamic and flexible approach is well-suited for large-scale web applications and instances where faster and more efficient data retrieval and processing are a priority.

There are four models of NoSQL databases:

• Key-value stores: Data is assigned and fetched by a unique identifier, which can be a string of numbers. Each key has an associated value, which can be either the data itself or its location within the database.
• Document stores: These store data within flexible JSON-like documents that usually carry one object and its related metadata. This allows for more advanced queries about a document’s content. It’s worth noting that documents are a superset of all other data models, so developers can structure data according to their applications.
• Column-family stores: A concept called a keyspace is at the center, containing all the column families (similar to tables in the relational model), which contain rows. The rows contain columns with data that can be accessed and identified via keys.
• Graph databases: Store data in nodes and edges (which a graph is made of) as a network of entities and relationships. As a result, graph databases tend to deliver high performance and flexibility as their structure fits better for modeling real-world scenarios.

Examples of non-relational databases include MongoDB, Cassandra, Redis, Couchbase, and Neo4j.

NewSQL Databases

As the name somewhat implies, the NewSQL database combines the best of what SQL and NoSQL offer: consistency and scalability.

It employs several techniques, most notably partitioning/sharding data (sometimes horizontally) and distributing it across multiple nodes in a cluster. Doing so makes parallel processing possible and enhances performance as the workload is distributed across the nodes.

NewSQL databases also engage in replication, which copies data for fault tolerance and load balancing. They deliver a more attuned solution to modern business processes where handling large volumes of data in real time is an everyday occurrence.

ClustrixDB, CockroachDB, NuoDB, Pivotal GemFire XD, and Apache Trafodion are some of the most popular NewSQL Databases.

In-Memory Databases

Adding a bit of twist to the story, in-memory databases store all the data in a host’s RAM.

The advantage is direct and, by proxy, quicker data access compared to a traditional database using disk operations because internal memory (positioned near the processor on the motherboard) is a lot faster than its disk counterpart.

So, in-memory databases are routinely employed for caching and storing temporary data. Such a method creates a copy that loads faster the next time it’s requested.

These databases are also great for frequently accessed information that requires virtually non-existent response time or for high-volume data where data loss wouldn’t be the worst thing in the world.

Examples include Redis, Memcached, Apache Ignite, SQLite, and MongoDB In-Memory Storage Engine.

Cloud Databases

A spin on the traditional relational database, a cloud version is built, deployed, and accessed in a cloud environment.

The core principle and functions are the same, but cloud computing offers the added benefits of speed, on-demand scalability, high availability, agility, and minimized costs, as businesses using it are charged based on how much storage or bandwidth they use.

Cloud databases can be offered as a managed database-as-a-service (DBaaS) or deployed on a cloud-based virtual machine managed by an in-house IT team.

Just like a traditional on-premises variant, a cloud database comes in the form of a relational and non-relational database.

Notable cloud computing platforms that offer this type of database include AWS, Oracle Database, Microsoft Azure, Google Cloud, IBM DB2, and MongoDB Atlas.

Transforming data into useful information doesn’t just happen by default. It requires a well-designed data model to deliver tangible value, which is where database design and data modeling come into the spotlight.

Data Modeling

The first step in the database design process, data modeling ensures that data is organized logically and systematically by crafting a visual representation of it.

In a nutshell, data modeling is about painting a picture of the type of data you’re dealing with: what data you have, how it’s related, how you can group it, and what it looks like. It’s how you define your data entities’ structure, attributes, and relationships.

The importance of data modeling in database design can’t be overstated. Since it supports business processes and IT strategy, it’s the focal point of every data engineering solution, from data architecture to data processing and governance.

On a database level, data modeling is key for understanding the correlations between different data elements for better organization and management of data. If designed properly, a data model can also boost query performance and lower data redundancy.

Normalization

As basic rules in relational databases go, normalization takes the cake. Think of each record as a row in a table, and all the rows are like peas in a pod. They all have the same structure, meaning they all have the same number of fields or columns.

Each table represents a single entity or concept. So, by breaking down data into smaller, logically related tables, normalization helps reduce data duplication and inconsistencies.

It keeps data tidy by arranging it into tables and columns in a way that cuts down on repeats and dependencies.

Every column in a table is like a specific detail or piece of info, and every row is a unique record with its own set of values for each detail. No piece of information is stored twice, thus improving the accuracy and reliability of data.

Plus, it does wonders for data management, both in terms of time and effort spent.

Entity-Relationship (ER) Diagrams

The most common data modeling methodology, the entity-relationship diagrams graphically explain the connection between entities stored in a database. They are created with three elements in mind: entities, attributes, and relationships.

Entities are objects or concepts relevant to the system — your customers, products, and orders. Attributes are an entity’s traits or properties, such as name, price, and so on. An ER diagram portrays a relation between entities, like a user buying a product.

When you want to conceptualize the database or gain a better understanding of the stored information, ER diagrams are invaluable tools.

They offer a clear overview of the data’s structure and act as a framework, decreasing the level of complexity and allowing database designers a quicker turnaround.

Since visuals are usually a lot simpler to grasp than a bunch of words, ERDs are quite handy when you need to explain tricky database designs to project managers or business partners. No matter how tech-savvy they are, they’ll get the gist of it.

At this point, creating one’s own database should be a no-brainer, but it doesn’t hurt to highlight specific areas where doing so will benefit you the most.

With such a broad spectrum of advantages, you’ll find databases in use in virtually every industry.

• Business applications: Saying that databases improve business processes doesn’t fully depict the scope of use. Think CRM and ERP systems (customer information, sales transactions, inventory management), financial records like reports and invoices, and everything in between.
• Web development: Databases are crucial in powering websites and web applications. Just think where eCommerce and user-heavy online platforms would be if they couldn’t store a ton of data used for user authentication, content management, online transactions, and more.
• Scientific research: Providing structure by storing information in a well-organized manner. Scientists and researchers can use queries to search for specific data points within the database and perform complex calculations and analyses to derive actionable insights.
• Healthcare: Storing personal health data to inform and advance patient care, diagnosis, and treatment. Thus, databases have a vital role in electronic health records, patient management, and medical research.

It’s safe to say that databases are unsung heroes in all manners of business and life — and these are just a select few examples!

Despite being omnipresent, databases come with certain hurdles, the major one being data privacy. Having data readily accessible while keeping it safe from unwanted eyeballs is a perpetual balancing act between accessibility and comprehensive security measures.

It doesn’t help that the volume of data being generated is on a constant upward trajectory, particularly big data. Those stuck on more traditional databases will have to adopt new approaches to stay afloat in the ever-expanding oceans of information.

Add the growing complexity of databases, and things can get pretty hectic even for the more experienced database administrators.

However, advances in database technology, such as distributed and blockchain databases and machine learning integration, are alleviating some of the pain. For starters, distributed databases share the burden of storing and managing data, leading to faster processing and retrieval.

Throwing machine learning into the mix will take everything to another level. It will automate tasks like anomaly detection and data cleansing, optimize querying, and uncover trends and patterns within the data.

Blockchain databases will arguably have the biggest impact on data security thanks to the technology’s tamper-proof nature. Additionally, they can add more transparency to how data has been accessed and modified by creating an immutable audit trail.

It’s a fact: databases rule the digital world.

Because data is any information that can be linked to any object, it’s stored in diverse shapes and sizes. Hence, a database simplifies data management by structuring it for easier retrieval.

As if that weren’t enough, databases are becoming more robust and powerful than ever before. So, get ready to unlock the true potential of digital information — exciting possibilities in data management and analysis await!