Dynamodb vs redshift vs rds11/8/2023 ![]() Let’s compare these managed services considering the most critical factors based on which a data architect will choose one of these. We have now covered the basics of AWS Relational database service and Redshift. ![]() A detailed review of Redshift and its architecture can be found in one of our previous blogs here. Redshift is mainly optimized for large complex analytical workloads spanning across millions of rows, but can also support OLTP workloads if necessary though it is not the recommended practice. Like RDS, Redshift pricing is also including storage and compute resources and customers can choose to pay only for what they use. Except for some administration queries, nothing gets executed on the leader node and the real work is delegated to member nodes. Among the nodes, one of the nodes is designated as a leader node and this node is responsible for client communication, query optimization, execution plan creation, and sending tasks to individual nodes for execution. Scaling is accomplished by upgrading the nodes, adding more nodes or both. Redshift manages this optimum mix of scalability and performance through a cluster-based architecture with multiple nodes. Nevertheless, all the administrative tasks are automated here as well and customers can focus only on their core business logic. While for older generation instances that do not support elastic resize, scaling can only happen in a few hours. ![]() Scaling in the case of newer generation instances can happen in a matter of minutes using the elastic resize feature. Redshift allows the customers to choose from different types of instances optimized for performance or storage. The querying engine is PostgreSQL complaint with small differences in data types and the data structure is columnar. Redshift is a completely managed data warehouse as a service and can scale up to petabytes of data while offering lightning-fast querying performance. Key Features of AWS Redshift Image Source Storage scaling will depend on the type of database engine that is being used and the maximum it can go up is up to 64 TB for AWS Aurora database engine. The scaling normally takes a few minutes and it can go up to a maximum capacity of 32 vCPUs and 244GB of RAM. There is no concept of cluster or nodes when it comes to RDS and these individual virtualized instances can be scaled for performance or storage with just a few clicks. This means a replica of your database is automatically maintained in another region and AWS will manage a completely seamless switch in the unfortunate case of something going wrong with your database.Īrchitecturally, RDS works on top of virtualized instances. AWS also offers a high availability option in the form of multiAZ deployment. The full suite of security and compliance comes built-in with RDS along with encryption. are completely automated and the customers can focus only on their mission-critical business logic. All the typical administrative tasks related to running a database – Hardware provisioning, database setup, patching updates, backing up data, etc. Customers can also choose between different types of hardware through AWS instance types – optimized for performance, memory or IO. It allows a customer to choose from six different database engines – MySQL, MariaDB, PostgreSQL, AWS Aurora, Oracle Database, and SQL Server. Understanding Redshift and RDS Key Features of AWS RDS Image SourceĪWS RDS offers a fully managed relational database as a service. Towards the end, we also in detail mention under what circumstances/use cases you should opt for one of these two. In this blog, we will compare two of the most popular databases as a service from Amazon – AWS Redshift vs RDS, to see how they stack up to each other.
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