SQL databases are well-known in the data science industry, with the SQL programming language and relational database management systems being widely used in the field. However, SQL databases are not the only databases available to data scientists. There are also NoSQL databases, which have multiple structures and formats to correspond to a variety of datasets and types. These databases are very flexible and highly scalable, making them especially useful for big data projects and non-normative datasets. Data scientists can rely on NoSQL databases to collect and store large volumes of unstructured or semi-structured data. With dozens of NoSQL databases to choose from, the following description and list of NoSQL databases should assist any data science student or professional in deciding which database management system is right for them!
What are NoSQL Databases?
Standing for Not Only SQL, NoSQL databases are database management systems that are commonly used when working outside of the more traditional and rigid structure of a SQL database system. In this sense, NoSQL databases are commonly viewed as more flexible than SQL databases because of their compatibility with different types of data, schemas, and structures. Due to the numerous data types and formats available, NoSQL databases are also categorized through multiple types, with most NoSQL databases falling into the categories of document databases, column databases, graph databases, and key-value stores. Each NoSQL database reflects a specific database structure that corresponds to a unique data type.
NoSQL Databases for Data Science
While there are many NoSQL databases, as well as SQL database management systems that work with unstructured data, there are a few NoSQL databases that are commonly referenced when discussing how to work through data science and database development projects. The following list reflects some of the top NoSQL databases for data science students and professionals.
1. MongoDB
One of the most popular NoSQL databases for data scientists and developers alike, MongoDB is an open-source document database that is commonly used in the development of applications and software. MongoDB can also be operated with Atlas, a cloud-based database, as well as MongoDB React for application development. MongoDB is essential for data scientists that are working with data from websites and in need of a database that can change and develop over time.
2. Apache Cassandra
Part of the Apache Software Foundation, Cassandra is an open-source NoSQL database which makes it easier for data scientists to create a distributed database management system. Through horizontal scalability which allows data scientists to store information and data across nodes of databases, Cassandra is known for enabling ease of communication between the nodes.
3. Redis
Redis is a multi-operational open-source platform of which its role as a database is only one of its many uses. Redis can be used as a key store value NoSQL database, meaning that data within the database can be stored as unique keys and corresponding values. Key store databases, like Redis, are especially useful for data scientists that need to collect multiple types of data in the same storage system. For example, Redis allows for the collection of documents, graphs, and other objects within its storage system.
4. Apache CouchDB
Like many NoSQL databases, Apache CouchDB prides itself on being a highly scalable database management system that can be used for big data projects as well as the development of mobile applications. Operating using the JSON format, Apache CouchDB enables a single node database or clustered databases, as well as an ecosystem of products that support the replicability of data within and outside of the platform. As an open-source product, Apache CouchDB also has an interactive community of data scientists and developers that can be relied upon for resources and guidance.
5. Apache Hbase
Standing for the Hadoop database, Apache Hbase is a distributed and scalable NoSQL database that specializes in the storage of big data in the format of tables. This is different from many other NoSQL databases, as Apache Hbase is primarily used for the organization and management of structured datasets. This is an excellent NoSQL database for data scientists who want a substitute for a SQL database management system.
6. Amazon DynamoDB
Offering multiple database management tools for data scientists, DynamoDB is one of many products from Amazon Web Services (AWS). Amazon DynamoDB is a key-value store NoSQL database that is primarily marketed as a fast and efficient storage system for data scientists. In addition, DynamoDB is used within the world of web development, including the development of games and mobile applications.
7. ElasticSearch
A unique database management tool, ElasticSearch can be used for data analytics, storage, and as a search engine that can be integrated into multiple products and platforms. ElasticSearch is also a Java-enabled NoSQL database that is primarily used when working with unstructured data. Data scientists primarily use ElasticSearch for its capabilities in data indexing and querying.
8. Oracle NoSQL
Known for its many SQL database management systems and services, Oracle has also developed a NoSQL database that prioritizes web development. The Oracle NoSQL database employs the formats of JSON, tables, and key-value stores, which means that it can be used with both structured and unstructured datasets. Similar to other NoSQL databases, Oracle NoSQL prioritizes scalability, flexibility, and changeability across the data science life cycle.
9. Azure CosmosDB
Included as part of the Microsoft ecosystem of data science tools, Azure CosmosDB is compatible with the NoSQL databases Cassandra and MongoDB. Azure CosmosDB also operates on a serverless system that has multiple uses for data scientists and developers. Azure CosmosDB can be used for the development of applications, data warehousing, and online transaction processing.
10. Couchbase
Couchbase is a distributed cloud-based NoSQL database that is a go-to for database developers and data architects that are working with enterprise applications. As a document database and a key-value store, Couchbase can be used with JSON, Java, Python, and many other languages and text-based formats. In comparison to other top NoSQL databases and relational database management systems, Couchbase is a cost-effective and scalable platform for storing and managing complex datasets.
Want to work with NoSQL Databases?
Depending on the type of information and data that is collected, there are many NoSQL databases that can be useful for data scientists. When working with websites or pages, NoSQL databases like MongoDB are essential to managing and storing document-based data. Data scientists that want to work with documents, applications, or websites can take one of Noble Desktop’s NoSQL Databases with MongoDB course, which is included as part of the Software Engineering Certificate as well as the Full-Stack Web Development Certificate.
Due to the compatibility of many database management systems, learning more about the SQL programming language is also useful for data scientists that are working with NoSQL databases. Any of Noble Desktop’s SQL courses are instructive for data scientists interested in the structured querying language and database management systems. Similar to NoSQL databases, PostgreSQL is a database management system that can be used to manage unstructured data and Noble Desktop’s SQL Bootcamp includes foundational instruction in this popular SQL database.