The collection and storage of data is a keystone to the data science industry. In the era of big data, it is vital for individual companies and larger institutions to develop solutions to storing and managing datasets that are not only large but also scalable. In response to the problem of big data storage and database management, many software and technology companies have developed database management systems that operate using cloud servers and platforms.
These cloud-based platforms differ from traditional server systems because they tend to be highly scalable and flexible, not only in the type of data being stored but also in how much data is stored and the ways that it can be accessed. The many benefits of cloud databases have resulted in more companies and organizations migrating to cloud computing systems and databases. Whether you are a data science student or a professional, it is important to stay informed about the most popular cloud databases within the data industry.
What is Cloud Computing?
Cloud computing is a type of computing involving the relationship between cloud providers and individuals or teams of users. In contrast to traditional forms of computer systems, cloud computing uses serverless cloud storage, which gives users access to different types of information and data at the same time. Through virtual machines, cloud storage systems, and a multitude of products and services, cloud computing allows users the freedom to store and distribute large amounts of information and data across a network without having to rely on a single computer or server for data storage and retrieval.
Benefits of Cloud Computing in Database Design
There are many benefits to storing data in the cloud, instead of on a single server. Depending on the type of database management system, traditional systems tend to offer limited data storage because the storage is relegated by the type of data that is being stored, as well as the initial storage capabilities of the server. Many times, these systems are vertically scalable: the system can only store as much data as the server can handle and cannot add more storage after a certain size limit.
When working with big data collection, many data science projects will require additional storage at some point, which leads to the recurring issue of having to purchase a new storage system. Cloud computing systems tend to be horizontally scalable, making it easier to add databases to a primary system. In addition, the horizontal scalability of cloud databases also allows for greater data mobility when migrating data or creating pipelines between databases. When working within the same ecosystem or cloud provider, moving data from one place to another (as well as accessing data from different places) is built into cloud databases. Cloud databases tend to be geared towards big database management, such as the creation of data warehouses and lakes.
Top 5 Cloud Databases and Providers
The list below includes some of the most popular cloud databases on the market and the features that they offer. While some of the offerings on the list are cloud providers with multiple database management systems available to users, some of these databases are included as one of many products and services offered by a specific technology company. This list includes both SQL and NoSQL database management systems.
1. Amazon Web Services
One of the largest cloud providers on the market at this time is Amazon Web Services (AWS), which includes dozens of data science tools that are useful for everything from data analysis and visualization to machine learning and artificial intelligence. AWS offers several cloud databases for Data Scientists including Amazon RDS, a SQL relational database management system, and Amazon DynamoDB, a NoSQL key-value database. In addition to these cloud database management systems, Amazon Redshift can be used to create data warehouses and pipelines for easy data migration.
2. Microsoft Azure
Although Microsoft Azure is known as a more traditional SQL relational database management system, it has many of the same cloud computing capabilities seen with some of the major cloud providers in the data science industry. As one of many database management systems developed by Microsoft, Azure also allows users to work with the SQL programming language using the Microsoft Cloud. Tools such as Azure Arc can be used to streamline the process of working with multiple databases or cloud systems.
3. MongoDB Atlas
Known as the go-to NoSQL database management system for developers and Data Scientists alike, MongoDB Atlas is a document database management system commonly used within mobile applications. Cloud databases are useful in the development of mobile applications because there is a need to store a lot of data in a very small space, and the database needs to communicate with many other devices and systems. MongoDB Atlas is unique in that it allows for multi-cloud deployment: you can work with more than one cloud provider at the same time, making it more efficient to migrate data across clouds.
4. IBM DB2
IBM DB2 can be described as a “family of database management products,” which primarily includes relational databases like Db2. These products are unique in their reliance on artificial intelligence and automation to organize and manage datasets. IBM promotes hybrid database management systems. These incorporate both cloud and server-based databases and can be useful for companies with a large and diverse collection of data. Through the Common SQL Engine and IBM’s Cloud Pak, this software company offers a multitude of tools to integrate some of the benefits of cloud computing into more traditional systems. IBM DB2 offers database management systems for individuals and businesses seeking enterprise products.
5. Google Cloud Platform
The Google Cloud Platform (GCP) is another collection of tools from a cloud provider known for creating multiple cloud-based products and services. Products such as Google Drive and Sheets are great for beginner Data Scientists who want to store and manage smaller datasets. However, for Data Scientists who are interested in the storage of larger datasets, Google also offers products like Google Cloud SQL. This is a cloud-based relational database management service that is compatible with MySQL, PostgreSQL, and Microsoft SQL Server by acting as the cloud storage system for these well-known databases. Data Scientists who are already working with a traditional database management system can use the Google Cloud to take advantage of the many benefits that come with cloud database storage.
Want to learn more about Database Design and Development?
The popularity of cloud computing within data science has greatly influenced the tools and techniques used for database design and development. Noble Desktop’s Cloud Computing with AWS teaches students how to use Amazon Web Services, including database design. In addition, Noble Desktop’s data science classes offer instruction in how to engage in the most up-to-date methods of data collection and storage through the development and design of database management systems.
When learning about database design, SQL is the primary programming language used to communicate with database management systems like Amazon RDS and Microsoft Azure. Students and professionals that would like to learn more about using SQL databases can take SQL courses. Noble Desktop also offers instruction in working with other database management systems in NoSQL Databases with MongoDB. There are classes available that reflect the many ways that Data Scientists work within the realm of database design and development.