Scalable Database Deployment for Cloud-Native Applications

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Written By Naomi Porter

Naomi Porter is a dedicated writer with a passion for technology and a knack for unraveling complex concepts. With a keen interest in data scaling and its impact on personal and professional growth.

Scaling your data, empowering your growth.


Cloud-native applications are becoming increasingly popular in today’s technological landscape. These types of applications require a highly scalable database architecture to handle changing demands efficiently. When it comes to database performance and scalability, there are several techniques and providers available to handle data volumes effectively.

In this article, we will explore the different options available to design a highly scalable database architecture for cloud-native applications, including vertical and horizontal scaling, read replicas, caching, sharding, and database design in a microservice architecture. We will go into detail about the techniques used to achieve scalability in the database, the providers that allow for scalable database deployment, and the benefits of different types of databases for cloud-native applications.

Achieving Scalability through Vertical and Horizontal Scaling

One of the techniques used to achieve scalable database deployment is scaling. Scaling can be achieved through either vertical or horizontal scaling, each having its benefits.

Vertical scaling involves increasing the processing power on a single server. This technique is easier because it does not require adding servers. However, it has limitations concerning how much processing power and storage you can put on a single server. Vertical scaling is best suited for applications that require a small amount of processing power or for databases with lower data volumes.

Horizontal scaling involves adding servers and partitioning the dataset and load over those servers. This technique is more complex and requires load balancing between servers. However, it provides larger load handling capacities for reads and writes, as well as a total data storage. With horizontal scaling, data is partitioned into smaller fragments, and those fragments are evenly distributed across servers. As the load increases, the number of servers can increase for highly scalable databases.

MongoDB is an example of a document-based database that supports both replication and partitioning techniques for horizontal scaling. With MongoDB’s sharding feature, data is partitioned and distributed across multiple shards in a cluster. This feature allows for horizontal scale for reads and writes, added storage volume, and geographical regions.##Techniques for Scalable Database Deployment

There are several techniques involved in scalable database deployment. These include hardware optimization, caching, contention resolution, partitioning, replication, and clustered computers.

NoSQL systems have gained favor because of their scalability and support for non-relational data types. These systems do not rely on pre-defined schema or constraints that limit the structure of the data. They can be adapted dynamically to support different data models and evolving data structures. NoSQL databases also support scaling through sharding and replication, providing elastic scalability and high availability.

In contrast, relational databases like Oracle have limitations to scalability because they were designed for centralized data management architecture. However, relational databases are an essential component of most enterprise applications. In many cases, a hybrid approach that combines the advantages of NoSQL and relational data models is the best choice.

Techniques that are used to manage databases include resource usage optimization, encryption for security, and schema as tools for data definition. Extract, transform, and load (ETL) processes are used to migrate data into the database, and transform functions are used to prepare data for queries.

Scalable Database Providers

Several providers offer distributed databases that are optimized for cloud-native applications and highly scalable database deployment. Some of these providers include CockroachDB, PlanetScale and Oracle, to name a few.

CockroachDB provides a distributed SQL database for speed, scale, and survival with elastic, efficient scalability and hotspot detection. Its capabilities include simple, automated horizontal scaling for reading and writing, automated rebalancing, and tuning geo-distributed queries. CockroachDB is designed for cloud-based databases and provides a solution for fault tolerance and disaster scenarios, ensuring that data consistency is maintained in the event of the cluster going down.

PlanetScale provides a serverless MySQL platform for scalable and high-performance database deployment. It is powered by open-source technology and allows for a groundbreaking query cache that boosts query performance. PlanetScale delivers independently scalable microservices, branching databases, and highly available read replicas in multi-AZ deployments.

Oracle offers database classic cloud service for scalable, enterprise-grade security, and database scalability. It harnesses the power of Oracle Database, providing a highly available and scalable database service for cloud-based applications. Users can choose between horizontally scalable databases with scale-out capabilities or vertically scalable databases with higher I/O compute shape.


In conclusion, achieving scalability in database deployment for cloud-native applications is of utmost importance. The techniques for doing so include vertical and horizontal scaling, hardware optimization, contention resolution, partitioning, replication, clustered computers, and NoSQL systems.

CockroachDB, PlanetScale, and Oracle provide innovative solutions for scalable database deployment, providing a distributed SQL database, a serverless MySQL platform, and horizontally or vertically scalable databases, respectively, for highly scalable, efficient database design. It is essential to consider the strengths and weaknesses of each option to optimize database performance for cloud-native applications.

With a scalable database architecture in place, cloud-native applications can handle data growth and increasing demands while delivering real-time performance feedback. The space-based architecture for data access and in-memory caching, together with encryption and constraints, can ensure a high-performance database deployment suitable for globally distributed applications.