Relational Database

• What is a Relational Database Management System
• Key factors to consider when selecting a relational database
• SQL, Python, and R: A guide in under 4 minutes
• What’s a SQL Query?
• Relational database model
• Benefits of relational databases
• Challenges of relational databases

What is a Relational Database Management System?

A relational database management system (RDBMS or just RDB) is a common type of database that stores data in tables, so it can be used in relation to other stored datasets. Most databases used by businesses these days are relational databases, as opposed to a flat file or hierarchical database. The majority of current IT systems and applications are based on a relational DBMS.

Relational databases have the muscle to handle multitudes of data and complex queries. Multiple tables are standard usage for modern databases. The data is often stored in many tables, also called ‘relations’. These tables are divided into rows, also called records and columns (fields). There can be millions of rows in a database. Columns are made up of one specific data type, like name or price.

In contrast, a non relational database (also called a NoSQL database) were primarily designed with management of large sets of data in mind. And don’t be fooled by the “NoSQL” tag; think of it more as “not only SQL” as they do often support some SQL commands.

Non relational databases do not store data in rows and columns. They are able to use whatever format is optimal for the data it houses. Unlike relational databases, they only accept specific queries. But because they aren’t SQL-based, they avoid rigid schemas for structuring data.

Much like the relationships between data in an entity’s relationship diagram, the tables in the relational database can be linked in several ways:

• Characteristics of one table record may be linked to a record in another table
• A table record could be linked to many records in another table
• Many table records may be related to many records in another table.

These relationships are what you see when you look at widgets (such as pivot tables) in your dashboard visualizations. It makes these complicated relationships easy to understand, so you can reveal the connections in your data and get your questions answered.

Key factors to consider when selecting a relational database

Initial Setup Setting up a DBMS, optimizing it for ideal operations, and future-proofing it for growth requires adequate flexibility for integration into the current data infrastructure. Synchronization with other platforms is also essential for uninterrupted workflow.

Data security Every DBMS will provide different security methods, like encryption, customizable routines, and access rights, to protect your data. These should all be carefully considered during the evaluation process. You probably want access controls like authorization and authentication to be default features, meaning data in tables within a RDBMS is limited to access by specific users.

Data model How will you tell which model is right for your data? If you need to work with unstructured data, then a relational model won’t work. NoSQL databases are often available as open source, whereas a RBDMS is usually a commercial purchase.

Data accuracy/reliability Some of the questions you will be asking yourself here are your accuracy requirements, and whether to rely on business logic. Financial data and government reports, for example, will have more stringent requirements.

What gives RBDMSes their robust reliability is support of the ACID properties — atomicity, consistency, durability and isolation — which are the basis of reliable transaction processing.

SQL, Python, and R: A guide in under 4 minutes

What’s a SQL Query?

All RDBMSes use SQL queries to access the data stored within. SQL stands for Structured Query Language. You can create, delete, and modify tables using SQL queries, as well as select, insert, and delete data from existing tables.

Relational database model

As we’ve stated, in the relational database model, data is stored in tables. This means any record can be related to any other record, and new relations can be easily added. This structure sets up relational databases as an efficient, flexible, and intuitive way to store information. They can be accessed by any application.

This is important because if one user updates a record in the database — for example, the sales team updates with the monthly wins — all the instances of the database will be automatically refreshed. So the executive team can access the info on their dashboards in real-time (that is, if you are using a BI platform like Sisense, which connects to multiple data sources and provides real-time insights).

Benefits of relational databases

If you want to design a data storage system that makes it easy to manage lots of information, and is scalable and flexible, a relational database is a good bet.

• Manageability: For starters, an RDB is easy to manipulate. Each table of data can be updated without disrupting the others. You can also share certain sets of data with one group, but limit their access to other groups – for example, allowing only the HR department to see confidential information about employees.
• Flexibility: If you need to update your data, you only have to do it once – no more having to change multiple files one at a time. And it’s pretty simple to extend your database. If your records are growing, a relational database is easily scalable to grow with your data.
• Avoid Errors: There’s no room for mistakes in a relational database because it’s easy to check for mistakes against data in other parts of the records. And since each piece of information is stored at a single point, you don’t have the problem of old versions clouding the picture.

Challenges of relational databases

• Scalability: Relational databases are built on a single server. This means in order to scale, you’ll need to purchase more expensive hardware with more power, storage, and memory.
• Performance: Rapid growth in volume, velocity, variety, and complexity of data creates even more complicated relationships. Relational databases tend to have a hard time keeping up, which can slow down performance.
• Relationships: Relational databases don’t actually store relationships between elements, which makes understanding connections between your data reliant on other joins.