Relational databases are one of the leading methods of storing data, providing a structured format that pairs well with structured data. Around 60.5% of all databases are relational, compared to only 39.5%, which are NoSQL.
Every day, the world creates billions of new data records. A database collects related data organized and structured for efficient storage and retrieval. With the advancement of data capture, processing, and storage tools, businesses can now use data to an extreme degree. The nature of big data and data-driven decisions in company culture has led many to define this period as one in which data is the most valuable commodity in the world. Out of all the methods for storing data, it is the most popular.
But what exactly are relational databases, what core characteristics define them, and how do businesses use them? In this article, we’ll dive into absolutely everything you need to know about exploring:
- What is a relational database?
- Why are relational databases important?
- What’s the difference between relational and NoSQL databases?
- Introduction to ACID
- Are relational databases and relational database management systems the same thing?
Let’s dive right in.
What is a relational database?
It is a database format that organizes data in a structured manner. Data is stored in tables, rows, and columns in a relational database. One of the major benefits of this highly regulated system is that it is easy to establish relationships between different tables.
Building up a system of relationships between different tables within a relational database. Users can query, extract, find, and manage data rapidly. The term relational database dates back to the 1970s when E.F. Codd published a paper called “A Relational Model of Data for Large Shared Data Banks.”
While Codd initially had a series of rules that describe what relational meant, these have faded out of public use and no longer typically relate to how they are used today. Instead, relational refers to database systems that establish relationships between distinct data tables.
Within each table, each row will have a unique identifier. This identifier allows users to rapidly locate data within a relational database, facilitating querying and the quick extraction of specific pieces of data.
They are now worldwide, forming a core part of the data industry infrastructure.
Why are relational databases important?
Relational databases are an extremely robust method for storing and managing structured data. These databases ensure that data remains consistent and high quality using a series of mechanisms, such as normalization.
Alongside just storing data in an efficient and logical manner. They are also important because they allow users to query the data they store. Using SQL (Structured-Query-Language), users can retrieve the data they’re looking for, which is the foundation of data analysis.
Another way to extract data from databases is by using Lambda SQL, which adds Lambda functions to SQL queries. Lambda SQL allows users to expand what’s possible with SQL, adding additional capabilities like executing complex calculations, transformation, aggregations, and more.
As data analysis and management continue to improve, data analysts are increasingly able to execute complex commands that provide flexibility to data. Relational databases are fundamentally important in storing, managing, and querying structured data.
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What’s the difference between relational and NoSQL databases?
Relational and NoSQL databases are two different databases that allow users to store data. Relational databases use SQL as their query language and primarily will store structured data. On the other hand, NoSQL databases provide much more flexibility, allowing for storing unstructured, semi-structured, and structured data.
NoSQL databases typically focus on horizontal scalability, distributing data across multiple nodes and servers to provide a rapid environment for querying. While both NoSQL and SQL databases are useful, they serve different functions and will typically deal with distinct forms of data.
The storage system you choose will vary depending on the data type you’re using and what you’re doing with that data.
Big data is processed using relational databases, although alternative technologies have emerged to handle the diverse and voluminous nature of big data more effectively.
Which of the following is not a relational database?
The answer above question is MongoDB is not a relational database.
Introduction to ACID
Structured databases use ACID to ensure that data transactions and processing always function as expected. ACID stands for Atomicity, Consistency, Isolation, and Durability, with each working together to ensure that data is reliable and offers data integrity.
Here’s what ACID does in structured databases:
- Atomicity: Atomicity ensures that a transaction either occurs or it doesn’t. It ensures that a transaction is either completely finish or rolled back, ensuring data is never in more than one unit.
- Consistency: Consistency ensures that a relational database is in the same state when someone interacts. This allows for data integrity as you will always interact with the most up-to-date and functional data version.
- Isolation: Isolation ensures that concurrent transactions (those that happen simultaneously) don’t interfere with one another.
- Durability: Durability ensures that data within a relational database is permanently available, even in a system failure.
Relational databases are often use as transactional databases, meaning they execute commands or transactions. To support transactions, ACID is used to make sure these transactions are reliable and process without a hitch.
Are relational databases and relational database management systems the same thing?
Relational databases and relational database management systems (RDBMS) are very similar and have many shared features, but they are different. As we’ve discussed, a relational database organises data into tables, rows, and columns. On the other hand, an RDBMS is software that allows users to store, retrieve, and manipulate data within a relational database.
An RDBMS provides various tools and interfaces that allow users to query and maintain its database, creating and enforcing the relationships between tables. There is a huge array of RDBMS to choose from, popular ones being MySQL, SQL Server, PostgreSQL, and more.
Several cloud services pair with databases and RDBMS to increase their scalability. For example, Google Cloud and AWS are huge contenders in this sphere.
Is big data processed using relational data?
While relational databases have traditionally been a popular choice for processing and managing structure data, it is important to note that big data encompasses a wide range of data types and volumes that may not be ideally suite for traditional systems. Relational is typically designed for structured data with predefine schemas, and they may face challenges when dealing with large volumes of unstructured or semi-structured data.
In big data, alternative technologies have emerged to address the unique requirements of processing and analyzing vast amounts of diverse data. These technologies include:
Distributed file systems:
Big data processing frameworks like Apache Hadoop’s Distributed File System (HDFS) or Apache Hadoop-compatible alternatives such as Amazon S3 or Google Cloud Storage store data across a cluster of machines, enabling parallel processing.
NoSQL databases:
Non-relational data, often called NoSQL (Not only SQL), has gained popularity for handling big data. NoSQL databases, such as Apache Cassandra, MongoDB, or Amazon DynamoDB, are designed to scale horizontally and handle large volumes of data while providing high availability and low-latency access.
Data processing frameworks:
Technologies like Apache Spark or Apache Flink offer distributed computing frameworks that can handle large-scale data processing and analytics. These frameworks provide capabilities for parallel processing, in-memory computing, and support for various data formats.
Data lakes:
A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format. Data lakes, often built on top of distributed file systems, provide a flexible and scalable solution for storing and processing big data.
Streaming platforms:
With the rise of real-time data processing, streaming platforms such as Apache Kafka or Amazon Kinesis have become essential for handling continuous data streams. These platforms enable the processing of high-velocity data in real-time or near real-time.
It’s worth noting that while relational data may still play a role in certain aspects of big data processing, they are often used alongside or integrated with these alternative technologies to create comprehensive data ecosystems that can efficiently handle the challenges posed by big data.
Final Thoughts
Relational databases are one of the fundamental data structures that the world of business, data analytics, and data storage rely on daily. Relativity database is not a term commonly used in the context of databases. Although NoSQL databases offer more flexibility regarding what data they can hold, relational data are the classic choice for structured data.
Especially for those executing transactions, a relational database is an invaluable location to store, interact with, and manage large volumes of data. Relational data are absolutely everywhere and are, without a doubt, one of our most important pieces of data architecture.
