8 SQL Databases You Should Explore as a Data Analyst

7 min readApr 5, 2023

Selecting a database management tool has become more important due to the increasing use of data for different purposes and the influx of databases in the market. Consequently, knowledge of several SQL databases has become one of the highly sought-after skills in the data analytics industry.

So, to be proficient in data analysis, you must be familiar with SQL databases and their use cases. This article will provide in-depth information on the top eight databases with features that seamlessly facilitate data analysis.

However, let’s first refresh our understanding of SQL databases.

What Is a SQL Database?

A Structured Query Language Database is a powerful tool widely used by data analysts, developers, and administrators to store, manipulate and retrieve the data stored in a relational database. SQL databases are organized in tables (columns and rows) and used in various applications.

Note: To further learn how SQL works, you can utilize free tutorials on tech platforms like Codecademy and W3Schools. You can also opt for paid courses and read books on SQL. Some books are “SQL: A Beginner’s Guide” and “Practical SQL: A Beginner’s Guide to Storytelling with Data.”

Top SQL Databases

The sheer number of databases available today can make it challenging to choose one. Fortunately, to help you narrow your search, I have curated eight high-ranking structured query language databases you can choose from as a data analyst. So without further ado, let’s get right into them.

1. Microsoft SQL Server

Microsoft SQL Server is an advanced relational database management system offered by Microsoft. It’s renowned for its scalability, reliability, business intelligence, and user-friendliness. This database also provides data analysts speed and efficiency in querying large datasets.

Furthermore, Microsoft SQL Server is popularly used in Windows environments and is an excellent choice for small, medium, and large-scale businesses. To get started:

Connect to SQL Server Management Studio (SSMS). Meanwhile, Microsoft created the SSMS for managing and connecting to SQL Server and Azure SQL Databases.
Retrieve and clean your data using specific SQL queries.
Perform analysis depending on the business requirements. These requirements could range from performing trend analysis to building a predictive model or calculating core performance indicators.
Visualize your results — using data visualization tools like PowerBI or SQL Server Reporting Services (SSRS) — and ensure the visuals are easy to explore and understand for other relevant stakeholders.

2. PostgreSQL

The PostgreSQL database is one of the best cutting-edge open-source relational databases in the world. That is, anyone can modify the source code for any purpose. It is also compatible with several operating systems — including Windows, Linux, MacOSX, and Unix.

Moreover, the PostgreSQL database is excellent for GIS applications because it supports geospatial data types and functions. Below are more key features of PostgreSQL.

PostgreSQL complies with the ACID (Atomicity, Consistency, Isolation, Durability) properties to ensure data integrity and consistency.
It allows users to create custom functions, operators, and indexing methods.
It also supports horizontal and vertical scaling, enabling you to handle large volumes of data.
PostgreSQL supports multiple concurrent connections, allowing several users to access the database efficiently simultaneously.
It offers various security features, such as role-based access control and data encryption.
It supports foreign data wrappers, which enables it to be integrated with external data sources like other databases or web services.
Besides supporting structured and unstructured data, it can be programmed using several programming languages like Python.

Having explored the features, here’s how you can use this database efficiently for data analysis:

Download and install the latest version of PostgreSQL on your computer (desktop or laptop).
After installing PostgreSQL, use a PostgreSQL client to connect to the database.
Once connected, import the data you want to analyze into PostgreSQL using SQL commands or tools like pgAdmin or DataGrip.
Use PostgreSQL’s array of functions and operators to explore, manipulate, and get insights from imported data based on specific requirements.
Then visualize data using tools like Tableau and PowerBi to create graphs and charts for better understanding.
Finally, export results once you are satisfied with your analysis.

3. MySQL

As a data analyst, MySQL is a potent tool that enables you to access and manipulate data sets. Additionally, it is an open-source relational database management system that you can use on multiple platforms. In other words, it runs on Linux, Solaris, and Windows and supports platforms with programming languages such as C, C++, Java, and Python.

MySQL is scalable, easy to use, fast, secure, and suitable for small to large-sized businesses–companies like Facebook, Twitter, Wikipedia, and YouTube utilize its backends. Most importantly, you don’t have to be a professional to use MySQL, as anyone can download, install and use it in a few minutes.

4. Oracle Database

Oracle Corporation created this multi-model database administration system to aid online transaction processing (OLTP), data warehousing (DW), and mixed (OLTP & DW) database workloads. It is also considered a cost-effective, high-performance solution that supports all data types involving relational, graph, structured, and unstructured information.

Oracle Database can operate on servers from other organizations. It has unique recovery features that ensure data is available during downtimes. The license is expensive, and you may need extensive knowledge of SQL. Below are a few tips to perform data analysis:

Connect to the Oracle database using SQL developer. Then provide a database name, username, and password.
Use SQL queries to access and clean your data.
Afterward, perform analysis using embedded analytical tools like Oracle Business Intelligence and Oracle Analytics Cloud to explore data and identify patterns.
Share the results of your analysis with stakeholders or colleagues using tools like Oracle Reports.

5. SQLite

SQLite is one of the best integrated and serverless relational database management systems. It is an in-memory open-source library that needs no installation or setup and is simple to operate because it doesn’t require any server support. Moreover, SQLite is lightweight and commonly used by developers for mobile applications and small-scale projects.

All you need to do to start data analysis with SQLite is to install SQLite on your computer and become familiar with how to execute SQL queries. Once you have familiarized yourself with SQLite and SQL, you can start writing queries and performing data analysis.

6. Google Cloud SQL

Google Cloud SQL is a fully managed relational database service offered by Google Cloud Platform (GCP). That is, it provides automated backups and updates, allowing developers to focus on other areas of their applications’ development. Likewise, as a data analyst, you can use Cloud SQL to store and analyze large volumes of data.

It also provides a scalable database environment for web, mobile, and enterprise applications. Interestingly, you can integrate Google Cloud SQL with SQL Server, MySQL, PostgreSQL, and other GCP services — such as Google Kubernetes Engine, App Engine, and Compute Engine — providing a seamless development experience.

Here are some other key features of Google Cloud SQL.

Numerous security options, including user authentication and authorization, data encryption, and controlled database access control.
Users can troubleshoot and analyze database performance using monitoring and logging tools.
It is low-cost, allowing you to pay for only the resources you use.
It has limited support for non-relational databases.
It is not open source, limiting customization options.

7. Amazon Relational Database Service

The Amazon Relational Database Service (Amazon RDS) makes setting up, running, and developing a relational database in the AWS Cloud simpler. It manages typical database administration tasks and offers affordable, re-sizable capacity for an industry-standard relational database.

But why should you run a relational database in the AWS Cloud? Running a relational database in the AWS Cloud is efficient because AWS takes over many of a relational database’s difficult and tedious management tasks. To begin, set up your RDS instance, get familiar with writing SQL queries, and perform your data analysis with little or no hassle.

8. MariaDB

MariaDB is one of the most widely used database management tools, initially developed by MySQL. This tool transforms data into structured information in different applications.

It is scalable, secure, fast, and has several plugins that suit various use cases. Moreover, it is compatible with languages used with MySQL, such as Python and C++.

Be Proficient in At Least One SQL Database

SQL databases are essential for successfully managing, storing, and analyzing data. Although you may only use some of the eight SQL databases I have discussed, it is crucial to be proficient in at least one SQL database to stand out from others in your field.

Furthermore, using any of the aforementioned depends on the business requirements, priorities, budget, and other resources available to you as a data analyst. And when selecting a SQL database, consider the opinions of people who have used any of these tools in real-time. This will help you make an informed choice.

I trust you found this article helpful. Meanwhile, before you go, subscribe to my blog to get notified when there is a new article on topics that interest you.