![]() ![]() It promises all the benefits of a traditional relational database including ACID transactions, relational schemas, SQL queries, and high availability but with the scale and performance of distributed scale-out architecture. In addition to Cloud SQL, Google is aiming to transform the SQL database landscape with the forthcoming launch of its new horizontally scalable relational database service, Cloud Spanner. Cloud SQL is also easier and more flexible when it comes to setting up your database deployments. On the other hand, RDS lacks feature parity across its supported database engines. RDS can also be launched in Amazon VPC, whereas Cloud SQL doesn’t yet support a virtual private network (VPN). You can also take advantage of its Provisioned IOPS feature, to improve I/O between your database instance and storage. RDS supports storage volume snapshots, which you can use for point-in-time recovery or share with other AWS accounts. You can also modify your machine type by editing your instance settings.īoth RDS and Cloud SQL support read-only horizontal scaling, by which you can add replicas to improve query performance. You can increase storage space manually, up to a maximum of 10TB, or configure your instance settings to increase it automatically. Aurora is more flexible and scales automatically in 10GB increments up to a maximum of 64TB storage.Ĭloud SQL is somewhat more straightforward. However, it has no automatic resizing capability. Standard RDS provides up to a maximum of 6TB storage. ![]() However, you’ll still need to modify your instance or change storage type to increase your allocated capacity. Storage is decoupled from database instances. You can do this either through the AWS console or a simple API call. You can vertically scale your RDS deployment to handle higher loads by increasing the size of your virtual machine. Aurora’s cluster architecture is designed to address some of the scaling and replication issues associated with traditional databases. As Amazon’s own proprietary database engine, Aurora uses a different storage infrastructure from the other five services. ![]() PostgreSQL, MySQL, MariaDB, Oracle and Microsoft SQL Server are hosted on Elastic Block Store (EBS) volumes. RDS supports six database engines, Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle and Microsoft SQL Server, whereas Cloud SQL only supports MySQL. ![]() What’s more, both services provide automated backups. As you’d expect from two mature cloud vendors, both solutions offer automatic replication and are highly durable and available. Nevertheless, a cloud-based SQL DBaaS is the ideal solution for moving existing SQL databases to the cloud when your scaling needs are not too great.Īmazon’s Relational Database Service (RDS) is the market leader’s managed relational database service while Cloud SQL is Google’s SQL counterpart. This presents scaling issues and restricts query performance on larger datasets, which are limited by disk size, CPU, and available memory. But at the same time, traditional SQL deployments are built on single-node architecture. While NoSQL has seen a huge surge in interest over the last five to ten years, traditional relational databases remain the workhorses for most websites, applications, and legacy systems.Īfter all, SQL is an almost universally supported language, the data is highly structured, and schemas ensure data integrity without the need for substantial coding. In this post, we will compare the core DBaaS options on offer by two of the leading cloud vendors, AWS and Google Cloud Platform, and consider some of the key differences such as the types of databases offered, the underlying infrastructure, and the querying capabilities. So, it’s important to understand these differences to find the right fit for your cloud-based application. This makes a compelling case for using Database as a Service (DBaaS) as these solutions streamline many of the tasks involved in database management such as provisioning, administration, data replication, security, and server updates.īut while the DBaaS offerings of the leading cloud vendors share many similarities, they also come with their own individual characteristics to suit different use cases. But at the same time, they face the logistical challenges of migrating their databases and maintaining cloud-based infrastructures. The public cloud is now seeing widespread enterprise adoption as organizations migrate their workloads and explore the latest technologies for storing and analyzing their data. ![]()
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