When discussing data science, there tends to be a strong focus on all of the tools that can be used to analyze and visualize data, but there is less excitement and discussion around databases and the various systems and software programs that are used to manage our data. With its focus on data storage, organization, and cleaning, database management is just as important to data science as data analysis. This article will give a general understanding of database management systems, as well as a more in-depth description of the difference between specific types of databases. It will also discuss top database management tools and how you can learn more about working with some of the most popular databases in the data science industry.
What is a Database Management System?
But first things first, what is a database management system? A Database Management System (DBMS) is a program or software package that is primarily used to store, organize, retrieve, and manage different types of data. Most database management systems are set up to work with a specific dataset, and databases are not only utilized within data science but correspond to the field(s) of database management, administration, and design.
Created to house different categories of information and data, there are several subsets or types of databases. The following list includes a brief overview of these different databases types and the data that correspond to them:
- Relational Databases - Known as one of the most commonly used databases within data science, relational databases are used to find and understand the relationship between different types of data within a dataset. Relational databases generally utilize the SQL programming language and organize data into rows, columns, and tables.
- ER Model Databases - Standing for Entity-Relationship Model, ER Model Databases offer a relational database structure that is based on entities, or specific objects. This type of database is commonly used within the creation of data models and flow charts.
- NoSQL Databases - In contrast to relational databases, which tend to utilize the SQL programming language, NoSQL databases do not require SQL and are generally used to house unstructured data. Unstructured data can be described as data that does not have a specific form or organizational structure. NoSQL databases allow the organization of data outside of the standard form of rows and tables.
- Hierarchical Databases - With a structure that is similar to a tree, data within a hierarchical database are each connected to one central node which branches out to other nodes. Similar to a trickle-down or influence model, this type of database links data together across multiple levels of the hierarchy, with one node influencing the other.
- Network Databases - Offering a web-like structure to database design, the network database builds on the hierarchical model by making it easier to organize data that branches in multiple directions.
- Graph Databases - As a type of NoSQL database which is also similar to a network database, the graph structure focuses on the relationship between nodes, edges, and properties. This type of database is commonly seen within social media analysis and studying the relationships between individuals and/or objects.
- document-based Databases - Another type of NoSQL database that stores documents as data and organizes those documents based on their unique attributes and data elements.
- Object-Oriented Databases - Primarily based in the C++ and Java programming languages, this type of database is not as commonly used as the other databases but offers a unique and malleable structure by pairing a piece of data with specific methods.
In addition, there are several database management systems and tools which correspond to the structure and form of the data that is being stored. The list below includes some of the top database management tools that are currently used for data science.
Top Database Management Tools for Data Science
1. MySQL
One of the most popular database management tools for the SQL programming language, MySQL offers both an open-source and enterprise relational database that is compatible with multiple programs and applications. Data Scientists tend to use this system for querying data, grouping, and categorization.
2. SQL Server
Produced by Microsoft, this open-source relational database management system is offered through multiple platforms and interfaces, with both a desktop and web-based application. Compatible with several operating systems, SQL Server is commonly used within businesses and corporations, as this relational database is capable of handling advanced data processing and artificial intelligence.
3. PostgreSQL
Known for its community and longevity, this open-source relational database management system has a strong architecture that is committed to the safety and security of sensitive data. This database tool is especially useful when creating your own data types and writing code for specialized projects.
4. Sequel Pro
Another open-source relational database management system, SequelPro is an excellent option for Mac users that are passionate about contributing to a coding community. Described as easy to download and use, Sequel Pro also makes it simple to import data from other SQL-based databases and platforms.
5. MongoDB
Described as a NoSQL database management system, MongoDB is commonly used for storing data on mobile applications. As an intuitive cloud-based system, writing code and managing documents is simple and straightforward with this modernized software.
6. CosmosDB
This NoSQL database management system is also a document-based system that is geared towards the most popular data types of the 21st century. With its scalability and multiplicity of uses, this system is especially useful for the analysis of big data and fast-growing information stores.
7. Oracle
With multiple products and applications, Oracle is a useful tool for storing and organizing a variety of data types across industries. Offering innovation in database design, Oracle can also be used to visualize and model the data that has been stored in this versatile system.
Want to learn more about these data management tools?
Whether you are interested in database management or design, Noble Desktop offers several data science classes and certificate programs that focus on how to use these different systems and data science tools. You can take one of Noble Desktop’s SQL classes or SQL bootcamps to learn more about how to use database management systems. For students and professionals that are interested in Microsoft SQL Server, the SQL Server Bootcamp is another educational option. You can also take in-person SQL classes in your area that focus on how to use programming languages for database management.