Choosing the Right Cloud Database: Aurora vs DynamoDB vs Cosmos DB vs Spanner

Aurora vs DynamoDB is an important comparison for enterprises planning cloud migration or database modernization. Although Amazon Aurora and DynamoDB are viable cloud database solutions, other products such as Azure Cosmos DB and Google Cloud Spanner also have features that satisfy unique enterprise requirements.

For CIOs, data platform leaders, and enterprise architects, the relevant considerations include data models and workload characteristics, performance features, scalability economics, and the migration’s impact on the enterprise. This comparison highlights the factors that distinguish these solutions to assist enterprises in selecting the most appropriate database technology.

Five factors, four databases, side by side.

The critical difference is in the workloads each one supports. The best cloud database is the one that fits the application pattern and business objectives.

What each database is built around, and where it belongs.

Amazon Aurora: Relational Enterprise Applications

Amazon Aurora is a relational database that provides compatibility with MySQL and PostgreSQL. As a result, Aurora supports SQL queries, joins, relational constructs, and transaction workloads.

This database is best suited for relational applications and transaction workloads in areas such as order management, financial applications, and other domains that require connected records and queries. In a comparison of Aurora vs DynamoDB AWS, enterprises should consider Aurora for existing relational applications or workloads that require joins and relational queries.

Amazon DynamoDB: High-scale NoSQL applications

DynamoDB is a NoSQL database that is optimized for high-volume workloads. DynamoDB is horizontally scalable and is best suited for applications with predictable access patterns.

Common use cases for DynamoDB include shopping carts, customer sessions, gaming applications, and application data. However, it is not suitable for applications that entail frequent relational joins or querying of interrelated records.

Azure Cosmos DB: Globally Distributed Data

Azure Cosmos DB is a distributed database that is available in multiple APIs and offers configurable consistency options. This database is designed for applications that require data to be accessible in multiple geographic regions.

The performance and cost characteristics of Cosmos DB depend on the partitioning strategy, throughput allocation, indexing strategy, and consistency settings.

Google Cloud Spanner: Distributed Relational Workloads

Google Cloud Spanner merges relational data management with distributed scalability and transactional consistency. Spanner is intended for use by applications requiring relational schemas in regional or multi-regional architectures.

Spanner may prove useful to large transactional systems and global applications whose availability, consistency, and scalability necessitate the architecture provided by Spanner.

The data model is the decision — everything else follows from it.

The key distinction between Aurora and DynamoDB is that of their respective data models. Aurora uses a relational database model and provides sophisticated support for SQL queries, whereas DynamoDB employs the NoSQL database design.

This relational vs NoSQL cloud database decision should be based on the data requirements of the enterprise application. Migrating data from Aurora to DynamoDB would require schema conversion and application re-engineering to accommodate DynamoDB’s access patterns. At the same time, Aurora may involve unnecessary overhead for applications that mainly leverage simple key-based access patterns.

Sometimes the answer is both — at a price.

Enterprises should use Aurora for applications that require relational databases and DynamoDB for applications with predictable access patterns. Some enterprises use both DynamoDB and Aurora for heterogeneous workloads. For example, Aurora can be used for transactional business record management whereas DynamoDB can be used for customer sessions or application data.

There is no universal winner — only results under your own load.

In Aurora vs DynamoDB performance, there can never be a universally winning player as results vary according to factors such as query complexity, indexing, transactions, concurrency, and workload design.

While DynamoDB performs best when dealing with queries that require low-latency key-based data access, Aurora is ideal for performing complex relational queries.

The compute line is the part you can see. TCO is the part that decides.

The comparison of Aurora vs DynamoDB cost should not be limited to direct costs because enterprises should take into account the total cost of ownership (TCO). The indirect costs of a cloud database can include replication costs, data transfer costs, application changes, and other factors.

Aurora costs should be evaluated in terms of compute, storage, I/O, and other relevant factors. DynamoDB costs should be evaluated in terms of request volume, capacity allocation, indexes, storage, and other relevant factors.

Neither option is necessarily more cost-effective than the other, and Impressico Business Solutions can assist enterprises in analyzing their specific requirements and migration considerations prior to committing to a long-term decision.

Neither is better. They answer different questions.

Cosmos DB is not inherently better than DynamoDB because both databases have compelling use cases depending on the requirements of the enterprise application. That said, enterprises should consider DynamoDB for NoSQL workloads that entail predictable access patterns whereas Cosmos DB is more compelling for distributed applications that require consistent API access.

Enterprises should analyze factors such as throughput requirements, regional distribution, consistency requirements, and required application changes when considering Cosmos DB vs DynamoDB.

Relational consistency, at global scale.

Google Cloud Spanner is used for relational applications that require transaction consistency at scale. It can be utilized for global commerce applications or financial platforms that require relational consistency.

Six criteria that narrow four options down to one.

The best cloud database for enterprise depends on the requirements of the application and the capabilities of the enterprise. Enterprises can utilize these criteria when narrowing their options:

Impressico Business Solutions can assist enterprises with this workload assessment and database selection process. A structured Database Modernization approach can help enterprises make informed technology decisions based on objective criteria.

Fit first, then cost, then the migration itself.

The choice between Aurora and DynamoDB lies in determining which fits your workload. Aurora is for relational databases, DynamoDB is for scalable NoSQL databases, Cosmos DB is for global distribution requirements, and Spanner is for distributed relational databases.

Prior to making a decision, consider performance, total cost of ownership, scalability, and migration issues. Impressico Business Solutions can assist organizations with assessing these and creating a plan for database modernization.

Five questions enterprises ask before committing.

Impressico Business Solutions — database modernization, cloud migration and data platform engineering for the enterprise.