What are Cloud-Native Databases?

Cloud-native databases are built specifically for cloud environments, utilizing the scalability, elasticity, and distributed architecture of cloud infrastructure. Google’s Spanner, for example, can replicate data between regions; Google states its multi-region instances offer 99.999% availability.

These databases are constructed from the ground up to flourish in the cloud, unlike conventional databases, which were designed for on-premise environments but can struggle to adapt to the cloud’s dynamic nature.

• High scalability, availability, and cost efficiency are their defining traits.
• Some famous products in this category are Amazon DynamoDB, Google Cloud Spanner, and MongoDB Atlas.
• Cloud-native databases should be used where your applications require high scalability, you deal with enormous amounts of data, or you want to minimize operational costs.

The main difference is based on scale, architecture, and what approaches are used to achieve availability. Cloud-native databases are designed to add nodes horizontally to achieve high availability and performance in the cloud. Traditional databases, on the other hand, are typically planned for vertical scaling and may struggle to adapt to the cloud’s dynamic nature.

Cloud-native databases can integrate attributes such as a distributed architecture, managed services, and pay-as-you-go pricing, which may contribute to their compatibility with certain modern, cloud-based applications.

Easy scalability, high availability, low costs, and low operational overhead are some of the perceived benefits of products in this category. Cloud-native databases can rapidly scale out or in, depending on response to load conditions, which helps avoid taking the database offline. Some separate compute from storage: Microsoft’s Azure SQL Database Hyperscale lets you scale storage (up to 128 TB) independently of compute (Microsoft).

Pay-as-you-go services offer flexible payment based on usage, while fully managed services may help reduce the need for manual configuration and maintenance.

The main issues are cloud vendor lock-in, data migration, and the skills needed to deploy and manage these databases.

It is crucial to consider how easily you can move the data if migration becomes necessary, and to have a clear idea of your database management experience or whether you will rely on managed services.

Notable examples span relational and NoSQL designs:

• Amazon DynamoDB: A NoSQL database used for high-performance applications that require flexibility in data modeling.
• Google Cloud Spanner: It leverages a distributed architecture across various locations in its relational database, aiming to concurrently achieve data consistency and service availability. Google lists 99.999% availability for multi-region instances, 99.99% for regional ones (Google Cloud).
• MongoDB Atlas: A document-oriented database offered as a managed cloud service (database-as-a-service), designed for both flexibility and scalability.
• Azure Cosmos DB: This is a type of database that is hosted in the cloud and can work with data models including document, key-value, graph and table (Microsoft).

Industry use cases include e-commerce platforms, gaming applications, and IoT devices.

Use cloud-native databases when your applications need to be highly scalable and available, when you process large amounts of data, when you want to spend minimal time on operations, or when building new cloud-native applications.

Cloud-native databases are designed for the cloud environment. While they may offer advantages over traditional or SaaS solutions regarding scalability, flexibility, and cost-efficiency, these benefits depend on the workload.

If your needs are relatively basic, and your demands can be easily predicted, then a SaaS solution may be the best place to start. Nevertheless, when your application gets more complicated and your company needs more independence and customization, a cloud-native database may be the best solution.