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If data is the world’s new oil, then databases are the data barrels, albeit pretty sophisticated ones. State-of-the-art containers that can help your company expertly handle your real-world data management workloads — store data, sort it, and share it, and provide answers to questions, all without getting your hands filthy and ruining your shoes.
All the information about who you are as an organization and what your company does is stored in databases. They’re crucial to the successful running of an organization; they’re the solid building blocks of intelligent, satisfying user experiences on websites and in apps. And in this era of machine-learning-aided Big Data, with an increase in the volume of data that businesses are creating, analyzing, and using daily — either on premises or off — it pays to consider the various types of databases and their functionality.
Relational databases are key to making the types of data your business holds more useful to the people who need the information. But how do these “barrels” work?
What’s the structure of a relational database model?
A relational database is a type of database organized expressly to recognize connections between different informational items — types of “relational data.” It was invented in 1970 by an IBM programmer named E.F. Codd.
More fluid than a hierarchical database, the database design is made up of different tables that can be mixed and matched, put together in various ways, to enable users to easily locate exactly the data points they’re looking for in order to be able to get all the details they need at once, make correlations, and draw conclusions.
Each table in a relational database includes:
The rows are where the unique data is stored, and each table has a “primary key” to define its information. So for example, to store information for an everyday purchase order, a primary key column might be tagged Customer, with columns for categories such as Name, Contact Details, and Date. Alongside this might be an “Order Number” primary key with columns for Customer, Product, Price, and other details.
After setting up the organizational view they need to easily interpret the criss-cross of information, an employee can search the database to find the specific data subset that suits their needs. For instance, they might need to find:
Relational databases are structured using Structured Query Language (SQL), which enables users to query it to bring together the bits of information they need.
When first devised, the relational database model was a departure from IBM’s previously hierarchical structures, which required users to navigate through a “treelike” design with multiple “branches” to retrieve desired information.
In a hierarchical database, the same data is required to be repetitively stored in multiple places, which makes searching for specific information in near real time and seeing how certain data relates to each other more difficult. To find needed information, you’d have to search the entire model from top to bottom. And in a modern workplace where the volume of data is increasing all the time, making connections and drawing insights from that data is almost impossible.
A good example of a hierarchical database is one most people have seen at some point: an organizational ladder chart. The information is stored based on fundamental elements (items such as tables, records, and fields).
So for instance, John Smith, HR staff member, sits below Megumi Yee, HR assistant manager, who sits below Adita Gupta, HR manager, and so on.
The elements of the database are set and the information is static. You get one particular view of the organization, and this view facilitates a single purpose — easily knowing employees’ positions and their working relationships.
But there are some fairly major problems with this design. For example, if you wanted to see when John joined the company in relation to when Megumi was promoted to assistant manager, you would find that detail difficult to figure out. Also, if you had staff details stored in multiple places and needed to change something, you would need to make the same change in each separate data-storage location, as opposed to being able to simply update a single master instance.
By contrast, a relational database is more flexible.
Here are three use cases — relational database examples illustrating how data complexity can be handled.
A relational database allows records from one relational table to be linked to information in other tables. So for instance, each employee could be tagged with a primary key (perhaps their unique user ID), and all the information related to their employment could be connected under that unique identifier. That means redundant dataset information is removed (not repeated in multiple places), and you can more easily find the answers to complex questions.
Let’s say you own an ecommerce business and you’re getting ready to have a sale. If you’re using a relational database, you can simply update the sale prices of the items and you’re ready to go. With a nonrelational database, you would create separate records for versions of the items at the sale prices.
Creating ever larger amounts of data would also make for a pretty tedious data-entry process — one that would likely also cause you to introduce some errors while keying in information — so a database management aficionado would tell you to avoid this option.
If you’re using a nonrelational database, every time a customer buys something, a separate instance of their name is created along with their product purchase information for transaction processing. This leaves room for error. The customer’s name, for example, might be recorded slightly differently (e.g., spelled wrong or with a middle initial not used in another instance) for each transaction, depending on who enters it. If you also add the possibility of typos, you can see how there’s potential for informational chaos (not to mention the perceived need to staff up on more database administrators).
The main idea behind the relational data model is the ability to let users draw meaningful insights by maneuvering database tables together to illuminate and understand the relationships between various data types.
There are several benefits to storing and being able to sort data using a relational database:
With a relational database, you can quickly gather information from multiple data-storage locations and then filter it to meet your particular informational needs.
Relational databases are tightly structured and the data is organized meticulously using constraints, checks, and other database software “rules.” This advanced structure facilitates high levels of accuracy and integrity.
Because of the strong structure, in a relational database, instead of duplicating information and storing it in multiple places, you retain only single records of unique information. This keeps things from getting overly complicated and redundant, plus ensures that your employees and customers are all working with the same up-to-date information.
Relational database systems can be configured to admit only authorized users, and they typically provide features such as role-based security to protect sensitive data. You can also set field-access control and control permissions at the user level.
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