This article will explore the role regional databases play in data analytics, as well as the benefits and drawbacks of working with them.

What is a Database?

In order to understand relational databases, it’s important to first learn about databases in general. A database is essentially a large collection of millions or even trillions of pieces of related information. A database makes it possible for users to quickly and easily sort through huge stores of data. Most organizations rely on operational and production systems connected to databases that are constantly fed with streams of transactional data. This data comes from a variety of sources, such as customer activity on web pages, digital footprints, and enterprise resource planning platforms. When the data from a database is analyzed, it provides helpful insights pertaining to how operations are running and how customers are utilizing products, among others.

Database management systems (DBMSs) are systems that enable users to create and maintain a database. These systems are tasked with handling large information stores, as well as performing other tasks such as managing security, executing data backups, and importing and exporting data. The most common place where relational data is located is a relational database management system (RDBMS).

What are Relational Databases?

Relational databases are a kind of database that organizes data into tables that can then be linked based on common data. Information contained in data points inside a relational database is organized based on defined relationships, which leads to easy access to this information. Data structures such as indexes, views, and tables, are apart from the physical storage structures, which makes it possible for administrators to perform edits on the physical data storage while avoiding the logical data structure. Every row in a relational database contains information pertaining to related objects. It has a key, which is a unique identifier, and every column is made up of attributes of the data. The records designate a value for each feature so that the relationship between data points is simple to pinpoint.

By using just one query, relational databases allow users to retrieve a new table from data in one or multiple tables. This provides organizations with a more holistic understanding of the relationships present among all data, which can help with the decision-making process.

Relational databases originally became popular in the 1980s, and have been in use since this time.

How are Relational Databases used in Data Analytics?

Most data analysis involves more than one table. Often, many data tables are required, which must be combined to find answers to important questions. These multiple tables, also known as relational data, are important because their importance extends beyond the individual datasets, and into their relations to one another.

Although some assume that relational databases aren’t designed to graph data visualizations, it’s possible to do so with optimal data modeling. Tabular views sometimes even present the most efficient way to store and query this information. In order to do so, it’s important to convert relational data structure to a network. The way this data is modeled in the form of a graph is dependent on not just the data that is to be visualized, but also the sorts of questions being asked. Here are the five basic steps required to visualize data from a relational database:

  • Those working with relational databases must understand the data. This not only entails knowing which record formats and entity attributes are contained in the data but also how they are related.
  • Next, the required data relations must be combined. Often, this involves moving data into one table.
  • Key relationships are then pinpointed. These pertain to the relationships users have to comprehend to find answers to the questions at hand.
  • Data can then be modeled around key relationships. One example of this modeling is how an Insurance Fraud Analyst would have to find cars with multiple insurance policies, and then would need VIN numbers and policy numbers as well.
  • In order to visualize the actual content of a database, most databases offer a basic visualization of the stored data via their client applications. These visual representations typically only provide a glimpse of the local aspects if stored entities and relations, as well as associated properties. Some data explorers also offer users a way to edit the data that’s stored in their graphical user interface. The challenge with this process pertains to the fact that built-in explorers can limit the quantity of data that can be simultaneously displayed, or simplify the representation and interaction. In these instances, it’s important to customize the visualization beyond what built-in solutions can provide, which can require additional software or libraries.

Benefits of Using Relational Databases in Data Analytics

There are several benefits to working with relational databases:

  • Within a relational database, it’s possible to designate specific tables as confidential. They can then be protected with a username and password, which ensures that only those with authorization can have access.
  • Relational databases, as well as the systems in place to manage them, tend to be stable.
  • Because relational databases optimize primary and foreign keys to enable their tables to interrelate, the stored data is non-repetitive and doesn’t duplicate. This ensures the accuracy of the data.
  • Structured Query Language, or SQL, is used to create and interact with relational databases.
  • Due to the simplicity of relational databases compared to other network models, their speed of operation is generally fast. They do not require complex queries and can perform with simple SQL queries.

Drawbacks of Using Relational Databases in Data Analytics

In addition to the numerous advantages of working with relational databases, there are some drawbacks to be aware of as well:

  • Because relational databases are highly structured, their rigidity can be a limiting factor. In situations in which a new set of data doesn’t adhere to the tables’ parameters, including it becomes problematic.
  • Scalability is a problem in some relational databases. This means that each time a database’s side increases, the infrastructure must be altered to support this expansion.
  • Cost is one of the main deterrents to working with relational databases. Separate software is required to set up a relational database. In addition, system maintenance is typically performed by a professional technician. These costs can add up quickly, especially for organizations with limited budgets.
  • Relational databases need a significant amount of physical memory to operate properly. The various operations within a relational database each require separate physical storage.

Hands-On Data Science & Coding Classes

Learning to code is an in-demand skill for those working with data. It can open professional doors and also lead to upward career mobility within a Data Analyst role. Noble Desktop has a variety of coding classes available for interested learners. They are taught in-person in NYC and are also available in the live online format. These classes and bootcamps cover topics like SQL, machine learning, HTML, CSS, and Python.

In addition, over 100 in-person and live online coding classes are available from a variety of top providers. These small classes are designed for novice coders, as well as intermediate and advanced learners.

Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.

For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs.