What Are AWS NoSQL Databases?

A NoSQL database is used to store unstructured data. Unstructured data is information that does not fit into a constraint. NoSQL databases store data in a non-relational format, which means that you don’t need to fit them in predefined columns. A NoSQL database is often used when developers don’t know the data structure of a source, so they store data in a NoSQL database without relational database constraints. For example, instead of storing data in fields, developers can store it in a JSON document. 

Unstructured formats come in their own types such as document, key-value, graph, and column-family. The NoSQL vendor you choose will store data in one of these formats, but they all scale into enterprise storage silos capable of handling large amounts of data. Depending on the NoSQL vendor, querying uses a different query syntax from standard SQL databases. 

There are several NoSQL databases available within the AWS ecosystem, so we’ll cover a few common ones to help you choose the right solution for your project.

Overview of AWS NoSQL Databases

Amazon Web Services (AWS) has several NoSQL databases for you to choose from. It’s important to research each vendor and what a database offers to ensure it’s right for your business project, including open source applications. We’ve included several AWS NoSQL databases with their advantages and disadvantages.

Amazon DynamoDB

Amazon DynamoDB is a cloud-based serverless database. In a serverless environment, the database program and hardware run entirely in the cloud. This means that your business doesn't need to manage a virtual machine, dedicated server, or any configurations for an on-premises hybrid environment. It’s beneficial for open source applications.

Developers use an API to send queries to the serverless DynamoDB database. The database then sends a JSON response to the developer’s application. Administrators can dynamically and automatically scale the database horizontally—meaning adding servers—as loads increase. Use DynamoDB when you have applications that must store large amounts of data and you suspect that the application’s user base and data storage requirements will increase quickly.

Amazon DocumentDB

For developers familiar with MongoDB, Amazon DocumentDB is similar and modeled after the MongoDB structure. Amazon DocumentDB runs in a virtual cloud, so it can be separated from other servers in your environment using data abstraction. For example, you might use Amazon DocumentDB for a public-facing application where you need a demilitarized zone (DMZ) to protect from internet traffic. Amazon DocumentDB would be a good resource for this architecture.

Amazon DocumentDB stores data as a JSON object, which makes it easier for developers to parse. Instead of reformatting data to insert into the database, developers can use the original JSON object collected from a source. Compute power is decoupled from storage, so administrators can scale storage without increasing compute power, saving on costs.

Amazon Neptune

The Amazon Neptune NoSQL database works with a graph structure. A graph database stores data in nodes and then builds relationships between each node to query and connect them together. Social media applications use a graph database. Every profile comment can be linked to various data points to determine if your project is linked in some way to others including categories of interests.

Businesses with massive global databases can take advantage of Amazon Neptune. It’s also beneficial for artificial intelligence (AI) and generative AI (GenAI) applications. Amazon claims that Neptune can handle more than 100,000 queries per second and scale to 128TiB per cluster.

Amazon Keyspaces

Like Amazon Neptune, Amazon Keyspaces is also a graph database. Businesses with IoT data collection or massive data collected from various sources can benefit from Amazon Keyspaces. For example, a manufacturer with IoT data collected to monitor machinery can use Amazon Keyspaces to more quickly store, analyze, and retrieve data. The gaming industry also uses Amazon Keyspaces to collect player data and manage applications necessary for quick response to gamer input.

Time-series data is often stored in Amazon Keyspaces databases, and this type of data is used in real-time applications. Every AWS database solution offers fast response times, but Amazon Keyspaces has the lowest latency with responses within a millisecond. It also scales as compute power is necessary and more storage capacity is required.

Comparing AWS NoSQL Databases

All AWS NoSQL databases support large data storage, but the key difference is the way data is stored. A document database such as DynamoDB and DocumentDB stores information in a JSON format. Document databases are the most intuitive for most developers familiar with relational databases. Graph databases are beneficial when you have a large amount of related data, and the time-series Amazon Keyspaces database is best for real-time applications.

All four AWS databases support scaling and running in the cloud, but serverless DynamoDB requires less staff management overhead. AWS also supports security and monitoring necessary for databases storing sensitive data and compliance regulations.

Conclusion

Building an application usually requires a database, and AWS has a solution for any large-scale enterprise storage requirements. Because they run in the cloud, businesses know that their databases will have high availability with little latency, provided that your business deploys enough resources to handle queries and storage. Everpure has the storage capacity for any enterprise application with its cloud block storage, and it supports the AWS databases mentioned in this article.