Within the world of database management, there are multiple systems that database administrators, information technologists, developers, and other data science professionals are able to choose from. These databases each come with their own benefits and challenges, so choosing between them can get confusing, as there are not only different types of databases but also multiple database management systems.

Databases can be hierarchical or relational, document-based or object-oriented, and database management systems tend to fall into the categories of SQL or NoSQL. Learning more about SQL and NoSQL database management systems involves distinguishing the differences between the two in order to discover which databases are right for you.

What is Database Management?

Database management is a field and industry that exists adjacent to data science in its focus on the storage and security of information and data. Database management revolves around ensuring that data is easily accessible and navigable throughout the process of the data science life cycle. The field of database management also overlaps with web development, database design, and systems administration, as many professionals within these fields are tasked with not only maintaining already established databases but also creating new databases for a project, company, or website. Proper database management requires an in-depth knowledge of database management systems and other such data science tools.

Through learning about different database management systems and languages, you are able to better understand the vast ecosystem of tools and methods which can be used to create, update, and maintain a collection of data. This also means that database management includes understanding specific programming languages and syntax unique to different database management systems. While many databases rely on SQL, or Structured Querying Language, to code and communicate with a dataset, other databases allow for a variety of languages, and some even include their own unique syntax to manage and manipulate data.

When to Use SQL vs. NoSQL Databases

When deciding which database is the best choice for a company or project, it is easy to distinguish between different types of databases by determining whether a SQL or NoSQL database should be employed for database management. In comparing and contrasting SQL vs. NoSQL databases, both database systems are popular within data science, development, and database design with each including features that are useful to the collection and storage of different types of data.

SQL databases are known for their reliance on the SQL programming language to write queries i.e. to organize and search through a structured dataset. In contrast, NoSQL databases work with a variety of programming languages that can make sense of unstructured data. The languages used for SQL and NoSQL databases also correspond to the type of database being used. Relational databases rely on SQL to communicate with the stored dataset, while graph databases are NoSQL databases that commonly use languages like Java to write queries.

Structured vs. Unstructured Data

In this sense, there are specific circumstances in which it makes more sense to use either a SQL or NoSQL database management system. Generally, SQL databases are used when working with data that either is, or needs to be, structured in the very specific format of rows, columns, and tables that SQL tends to support. In contrast, it is more common to work with NoSQL databases when you are working with a messier dataset or unstructured data that includes a greater variety of data types within the same project or database. This is because NoSQL databases include multiple formats for different types of data, such as graph or document-oriented formats.

Horizontal vs. Vertical Scalability

Due to their table-based format, SQL databases are also known for their vertical scalability which allows an information and data professional to work up in building the database. This vertical scalability is important when working on a project that requires more storage capacity as you collect and add new information and data to the database. However, this vertical scalability means that SQL databases tend to be limited by the capacity of a single machine. NoSQL databases are known for their horizontal scalability which allows for the distribution of storage across multiple machines. This is especially useful when working with multiple databases or collecting large stores of data from multiple sources.

Rigidity vs. Flexibility in the Dataset

One of the biggest differences between SQL and NoSQL databases is rigidity vs. flexibility. While SQL databases tend to be more rigid in their form and function, NoSQL databases tend to be more dynamic and easily changeable. SQL databases work best when working with data that is more stable or static, while NoSQL databases are the go-to for projects which require more room for speculation. The list below includes some examples of the most popular database management systems under the categories of SQL vs. NoSQL.

Examples of SQL vs. NoSQL Database Management Systems

  • SQL Databases: IBM Db2, MySQL, PostgreSQL, Oracle Database, and Microsoft SQL Server
  • NoSQL Databases: MongoDB, Cassandra, Redis, and CouchDB

Most of these database management systems are open-source platforms that are not only used to collect and store data but also include features that are useful for data cleaning, organization, analysis, and visualization, as well as iterations of machine learning and model deployment.

Using SQL and NoSQL for Big Database Management

Transitioning from the era of database management into the era of big database management there is now significantly less separation between the uses of SQL and NoSQL databases. Unlike the era of database management which focused on the separation between different types of data and databases, big data management incorporates multiple databases, data science libraries, programming languages, servers, and cloud-based computing systems to ensure faster data processing speeds and greater storage capacities.

As relational database management systems develop, many of these databases have also worked to integrate more dynamic features which allow for the use of multiple platforms, programming languages, and packages. In addition, more rigid SQL databases have also become more flexible in the inclusion of unstructured data through the incorporation of text-based formats like JSON. Data science professionals that are interested in database management would greatly benefit from instruction in multiple databases and languages.

Need to know more about Database Management?

Whether you need to learn more about SQL or NoSQL database management systems, Noble Desktop has you covered! If SQL databases are your system of choice, then Noble Desktop offers multiple SQL courses which teach the ins and outs of the SQL programming language and relational database management systems. The SQL Bootcamp includes coursework, exercises, and resources that are useful to anyone who wants to learn more about using open source database systems.

For students and professionals that are more interested in database management with NoSQL databases, MongoDB is one of the most popular NoSQL databases and any of Noble Desktop’s MongoDB courses are instructive in developing skills in web development and database design. Especially for data scientists and developers that are interested in working with storage systems for mobile applications, the NoSQL Databases with MongoDB is an excellent course to take. Both SQL and NoSQL databases come with their own unique benefits and technological affordances, so you can also take courses in both to gain a more well-rounded portfolio of data science and database management tools.