Examining Amazon Web Services’ (AWS) Support for Relational and Non-Relational Databases

Over the years, Amazon Web Services has enjoyed exceptional growth, positioning itself as a top supplier of computing, networking, and storage solutions. With a 7-year advantage in the cloud computing industry and more than two decades of experience in development, AWS has established itself as a reliable and trustworthy provider.

Boasting more than 300 services and a pricing structure that can be personalised, Amazon Web Services (AWS) is an excellent option for businesses seeking to enhance their productivity using cloud computing. With such a vast array of options, it is natural to wonder about the distinctions between AWS’ SQL and NoSQL databases.

This section defines what a database is and highlights its importance.

To begin with, let’s stick to the basics. Data that can be accessed, modified, and processed, and is stored in a computer system, is referred to as a database. Similar to a library, a database must be structured in order to utilise its resources effectively. It is easier and faster to find a specific book if the shelves are arranged alphabetically rather than being randomly placed around the room.

A programme’s functionality heavily relies on its database, which stores essential data. In a figurative sense, the central processing unit (CPU) can be compared to the brain and the programme’s operations to the cognitive processes it performs. The database, therefore, can be likened to our memory banks, which we use to save, retrieve, and update information from our surroundings.

A database is frequently necessary for programmes to save and retrieve data. Similarly, an application, without a database, cannot retain data – much like an individual with global amnesia, who cannot remember their past or form new memories.

Relational Data Modeling System (RDMS)

The concept of relational databases (SQL) was first presented by Edgar Frank Codd in his 1970 publication, “A Relational Model of Data for Large Shared Data Banks”. This proposal proposes that data can be saved in tables and connected through links.

A table is a data structure, much like an Excel spreadsheet, which comprises of rows and columns. Each cell contains unique information, such as a person’s name, address, email address, age, gender, and so on. Every row in the table contains data for a single individual.

Tables can be combined through joining. For instance, in the event that there is a table consisting of transactions, we can link each entry to the individual who carried out the transaction. This allows us to have a singular record that keeps track of the user’s history, regardless of how many transactions they have conducted.

Though there can be some inconvenience involved, the primary objective of relational databases is to offer information in an organised, controllable format.

Databases That Do Not Depend on a Relational Model

Relational databases have been widely employed in software development ever since their inception, despite not being without flaws. This is because presenting real-world data in a tabular layout can be challenging, particularly when it is intricate, enormous, and lacks structure.

Contrary to conventional relational databases, NoSQL databases do not utilise tables to organise their data. A common approach is to save data as key-value pairs or graphs, as it offers increased flexibility and scalability with fewer limitations and structures.

The growing requirement for data storage has led to an uptick in the employment of non-relational databases within the domain of data science. Although such solutions still lack the popularity of SQL-based systems, it is apparent that they are gaining significance as data collection continues to expand. To find out more about whether to use SQL or not, please checkout this blog post.

Databases on AWS

Amazon Web Services (AWS) provides a multitude of solutions, ranging from basic hosting to advanced data science applications, with thorough support for both SQL and NoSQL databases. For more information on data science innovations prioritising users’ right to privacy, please visit this blog post.

Amazon Relational Database Service (RDS) provides an effective solution for developing, handling, and scaling relational databases. The service backs up PostgreSQL, MySQL, MariaDB, Oracle Database, and Microsoft SQL Server, as well as Amazon’s unique relational database service, Aurora. To learn more about PostgreSQL, please visit their website.

RDS provides an array of tools that can be accessed either through the dashboard or API, and aids in the automation and simplification of operations such as automatic failover, backups, disaster recovery, monitoring, access management, and performance enhancement.

Aurora is a specialised relational database service that is tailored for cloud computing. It offers a speed that is 5 times faster than MySQL, full compatibility with both MySQL and PostgreSQL, and is a more economical alternative to comparable services.

By selecting Aurora Serverless, scalability can be automatically managed for you. The capacity can be initiated, halted, augmented, or diminished based on your necessities. Even though there are several choices to choose from, AWS encourages customers to experiment with and favour Aurora as their preferred relational solution.

Last but not least, before delving into NoSQL solutions, it is essential to mention Amazon Redshift, which is a data warehouse specifically created for conducting analyses on relational databases. Instead of gathering data separately for analysis, the analytical tools are included in the service.

Amazon provides over a dozen non-relational database solutions, including Elasticache for Memcaches and Elasticache for Redis for handling in-memory databases, Amazon Neptune for managing graph databases, TimeStream for managing time series, and QLDB for managing ledgers. However, our primary focus will be on their two primary products: DocumentDB and DynamoDB.

DocumentDB is a perfect selection for the large-scale processing of JSON data. It is custom-made to handle this type of workload, offering effortless integration with Amazon Web Services, and having the ability to handle millions of requests per second without any loss in performance.

DocumentDB’s ability to work with MongoDB is a significant advantage. By replacing a project’s database endpoint from MongoDB to DocumentDB, users can use the same API interface without making any alterations to their existing code.

Amazon has created their own key-value and document database system called DynamoDB. One benefit of this system is that it can grow or shrink based on demand, much like other cloud services, as it does not necessitate dedicated servers.

It is worth mentioning that DynamoDB global tables copy data across numerous AWS Regions, allowing users to access their data from the server that is most conveniently located geographically, regardless of where they are situated at present.

Which service should I choose?

Suppose you are just beginning and plan to use AWS for the foreseeable future. In that case, it is recommended that you select one of their choices. Typically, these options are more economical and provide better support than alternatives.

RDS and DocumentDB are excellent options if you want to shift your database to the cloud or ensure optimal compatibility. Regardless of your preference, it will be a wise choice.

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