What is a Relational Database?

Relational Database Meaning

A relational database is a type of database that stores and provides access to data points that are related to one another.

Relational databases are based on the relational model, an intuitive, straightforward way of representing data in tables. In a relational database, each row represents a set of related data, and every row in the table has the same structure. The columns represent categories of data (such as name or email), and each entry in the column has the same type of data (such as text or integer). Together, the columns and rows create a table.

The relational model is so important because it forms the basis for SQL (pronounced "sequel"), which stands for structured query language. SQL is a computer language that lets you perform powerful queries and operations on data stored in databases. If you want to read from or write to a database, you have to use SQL. It's not really possible to use a relational database without using SQL, which gives you an idea just how important this data model is.

How does it work?

A relational database is a digital database based on the relational model of data, as proposed by E. F. Codd in 1970. The various software systems used to maintain relational databases are known as a relational database management system (RDBMS). Virtually all relational database systems use Structured Query Language (SQL) for querying and maintaining the database.

Relational databases have replaced legacy hierarchical databases and network databases because they are easier to understand and use. Relational databases are organized into tables, which consist of rows and columns, with a unique key identifying each row. Each entry in the table has its own unique key, or primary key. It is common practice to give each table a name that reflects its contents.

How Relational Databases are structured

The columns of the table represent different attributes or fields of the object described by the table, such as customer name or telephone number. Each row in the table represents an instance of an object, such as a particular customer or telephone number.

Each item of data is stored as a row in a table, and all rows with a common feature are stored together in the same table. One or more columns of the table may be designated as identifiers; these have unique values for each row and are used to establish relations between different tables.

For example, in a table that contains employee data such as names and salaries, employees' names might be designated as identifiers.

A related table containing departments might use names to identify each department — Sales, Marketing, etc. A query can then show employees' names alongside their department names by matching the identifier values in both tables.

Relational Databases and ACID Properties

Relational database transactions are characterized by four properties: atomicity, consistency, isolation, and durability, also known as ACID properties. As per oracale.com:

• Atomicity defines all the elements that make up a complete database transaction.
• Consistency defines the rules for maintaining data points in a correct state after a transaction.
• Isolation keeps the effect of a transaction invisible to others until it is committed, to avoid confusion.
• Durability ensures that data changes become permanent once the transaction is committed.

Examples

There are several examples of relational databases that data engineers commonly use, including:

1. MySQL: MySQL is an open-source relational database management system that is widely used for web applications and other online services. It is known for its speed, scalability, and reliability.
2. PostgreSQL: PostgreSQL is an open-source object-relational database management system that is known for its powerful features and support for advanced data types. It is commonly used for data warehousing, data analytics, and web applications.
3. Oracle Database: Oracle Database is a commercial relational database management system that is known for its scalability, security, and high availability. It is commonly used by large enterprises for mission-critical applications and data warehousing.
4. Microsoft SQL Server: Microsoft SQL Server is a commercial relational database management system that is commonly used for business intelligence, data analytics, and web applications. It is known for its integration with other Microsoft technologies, such as Excel and Power BI.
5. Amazon Aurora: Amazon Aurora is a cloud-based relational database service that is known for its scalability, performance, and availability. It is compatible with MySQL and PostgreSQL and is commonly used for web applications, gaming, and e-commerce.

Overall, these relational databases are widely used by data engineers to manage and process large volumes of structured data. They provide powerful features for data modeling, querying, and management, making them essential tools for any modern data team.

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As the volume of data an organization collects grows, it becomes increasingly important to use a data catalog. Here are some reasons why:

1. Data discovery: A data catalog makes it easier to find the data you need by providing a searchable and organized inventory of all the data assets in the organization. As the volume of data grows, it becomes more difficult to keep track of all the data sources and understand the relationships between them. A data catalog can help to mitigate this problem by providing a centralized location for data discovery.
2. Data lineage: A data catalog can help to maintain data lineage information, which is important for ensuring data quality and regulatory compliance. As the volume of data grows, it becomes more difficult to track the origin and history of each data element. A data catalog can help to maintain lineage information by tracking the flow of data through the organization and documenting its transformations.
3. Collaboration: As the volume of data grows, it becomes more important to facilitate collaboration among data stakeholders. A data catalog can help to achieve this by providing a common vocabulary and a shared understanding of data elements. This can lead to better communication, increased productivity, and more effective data-driven decision-making.
4. Governance: A data catalog can help to ensure data governance by providing visibility into all the data assets in the organization. As the volume of data grows, it becomes more difficult to maintain data quality, enforce data policies, and ensure regulatory compliance. A data catalog can help to address these challenges by providing a centralized location for data governance.
5. Cost savings: A data catalog can help to reduce the costs associated with managing and analyzing data by making it easier to find and use existing data assets. As the volume of data grows, it becomes more expensive to collect, store, and process data. A data catalog can help to mitigate these costs by reducing duplication of effort and promoting data reuse.

Overall, a data catalog is a critical tool for managing the growing volume of data in modern organizations. It helps to address data discovery, lineage, collaboration, governance, and cost savings challenges that arise as data volumes increase.

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