Why Learn NoSQL Databases for Data Science?

The SQL programming language is widely considered to be one of the top skills in data science. Many data scientists have been prepared to work with SQL databases, and specifically relational database management systems (RDBMS). However, relational databases are not the only databases that are useful to data scientists. Contrasting with the structure and form of SQL databases, NoSQL databases can be used to work with unique datasets, with a focus on unstructured data, which greatly expands the type of data science projects that you can undertake. Through learning more about NoSQL databases, data scientists can also transition their skills into other fields and industries, such as web development, software engineering, and database design.

What are NoSQL Databases?

NoSQL databases are usually described as any databases which operate outside of the more traditional frameworks of a relational database. The term NoSQL also stands for “Not Only SQL,” and NoSQL databases are generally differentiated from relational databases which are structured by rows and columns and that compare data within a table. NoSQL databases are databases that take a more flexible approach to the storage of different types of data. Through storing either unstructured or semi-structured datasets, there are several types of NoSQL databases that correspond to different database management systems and formats.

Types of NoSQL Databases

In comparison to SQL databases, NoSQL databases are known for their changeability and capacity to work with less structured datasets. Instead of relying on the rows and columns format of a relational database, NoSQL databases are inclusive of systems that organize data in a few key formats. NoSQL databases tend to fall into four categories or types of databases: column-oriented databases, document databases, graph databases, and key-value stores.

Column Oriented Databases

As an efficiency-focused method of data storage, column-oriented databases organize data by columns instead of the rows and columns structure of a relational database. However, while many NoSQL databases work with other programming languages, column-oriented databases can also be used with the SQL programming language due to structural similarities between the two. With this more compressed NoSQL database system, column-oriented databases make it faster and easier to index and return datasets that are stored in columns. These systems are also commonly used when there is a large amount of data that needs to be indexed at the same time, such as scientific or medical data, as the columns are grouped into families which increases their ease of accessibility.

Document Databases

While many databases focus on numerical data or keywords as values, there are also many datasets that are made up of documents. Document databases are especially common within text-based online environments and when storing archival materials. Instead of breaking up the content of a website into discrete parts or removing the format of a piece of paper, the data in the document database is stored as a complete page. Document databases are also compatible with text-based formats, such as JSON and XML, which make it easier to navigate the documents within the database.

Graph Databases

Commonly used within the realms of network analysis and when working with machine learning models, graph databases store data as nodes and edges as well as charting the relationship(s) between these entities. Within a graph, nodes are individual entities and edges demonstrate the linkages between the nodes. Data that is stored in a graph database is usually linked together in specific ways that make it easier to visualize the relationship between the nodes and edges. While some databases store graphs as a table, other graph databases rely on one of the other NoSQL database structures to store their data, such as storing the graph as a document.

Key-Value Stores

Key-value stores also have a structure that is comparable to relational database management systems, albeit very different from these more traditional models. As the name suggests, key-value stores are made up of attributes called keys and corresponding values. Similar to the rows and columns structure and pairings seen in relational databases, key-value stores are also returned using the columns of the key and the value. Within this NoSQL database, there are also major keys (which act as the leader of the key) and minor keys (which follow from the major keys).

Using NoSQL Databases for Data Science

Although NoSQL databases are not always the go-to for data scientists, there are many reasons why NoSQL databases are useful for specific projects. Especially if you are working with unstructured data, or formats such as JSON, NoSQL databases are essential to storing and retrieving data that does not follow a specific schema or format. NoSQL databases are also much easier to scale, as these databases can be broken apart or joined together to build up a data warehouse. This compatibility with multiple data types and horizontal scalability make NoSQL databases essential to data scientists that want to break out of more normative structures of data analysis, collection, and storage.

Within the data science industry, there are several NoSQL database management systems that are commonly used when working with specific types of NoSQL databases. Platforms such as MongoDB, Cassandra, Redis, and ApacheCouchDB are known for the features that they provide to students and professionals within the field of data science and their compatibility with certain NoSQL database types. For example, when working with key-value stores, it is recommended to use NoSQL database management systems such as Cassandra, whereas document databases are compatible with MongoDB.

Interested in learning more about NoSQL Databases?

Of the many NoSQL databases available, MongoDB is one of the most popular database management systems and Noble Desktop offers multiple courses focused on this NoSQL database. Specifically, the NoSQL Databases with MongoDB course includes instruction on how to use JavaScript to build database models. NoSQL databases are also commonly used within the development of mobile applications, so data science professionals that want to pursue a career as a Developer might take an interest in Noble Desktop’s Full-Stack Web Development Certificate.

In addition to data scientists that are interested in web development and design, developing knowledge of NoSQL databases is also instructive for those that want to learn more about database management systems in general. Noble Desktop also hosts SQL courses that offer instruction using the SQL programming language to work with some of the most popular and well-known database management systems in the industry. For example, PostgreSQL is known for its capabilities with both SQL and NoSQL databases, and it is the database system used within the SQL Bootcamp. By learning both SQL and NoSQL databases, data science students and professionals will develop a more well-rounded knowledge of the key platforms and programming languages for database design, management, and the analysis of multiple forms of data.