Relational Database Services (RDS) and Dynamo Database (DynamoDB) are both widely-popular cloud-based database services managed and offered by Amazon Web Services (AWS).
So what’s the real difference between the two? And, as a tech leader, how do you know which is best for your business? In this post we will cover these two services and the 12 significant differences between them that you should know about.
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SQL databases are relational databases that use structured query language for storing and retrieving data. NoSQL databases are non-relational databases that use various means for storing data.
SQL databases are relational databases that store data in table format. NoSQL databases are non-relational databases that store data in various formats, including JSON, XML, and Binaries.
SQL databases are based on the relational model, which organizes data into tables with rows and columns. NoSQL databases are based on the non-relational or ” NoSQL ” model, which stores data in documents with keys and values.
Amazon RDS can use the Amazon Aurora engine, with up to 128 TB and 64 TB of storage space for MySQL, MariaDB, Oracle, and PostgreSQL engines. On the other hand, SQL Server engine requires 16 TB of storage.
It supports tables of virtually any size.
The storage size ranges for General Purpose SSD DB instances are as follows:
The main difference between the two services is that Amazon RDS is designed for use with relational databases. In contrast, DynamoDB is intended for use with non-relational databases. RDS is more expensive than DynamoDB but offers more features and flexibility. DynamoDB is less costly but has fewer features and flexibility.
RDS performance scales automatically to meet the needs of your application. RDS will automatically add more resources to maintain performance when your application’s load increases. When the load decreases, RDS will remove resources accordingly. This allows you to focus on developing your application rather than managing the infrastructure.
DynamoDB automatically scales throughput capacity to meet the demands of your applications. As the number of requests increases, DynamoDB increases the number of capacity units allocated to your table. This enables DynamoDB to maintain consistent performance as the size of your data grows.
Amazon RDS is a cloud-based relational database service that supports a variety of database engines, including MySQL, MariaDB, PostgreSQL, Microsoft SQL Server, Oracle, and Amazon Aurora. Amazon RDS makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching, and backups. Amazon RDS is available on Amazon Elastic Compute Cloud (Amazon EC2) and is integrated with Amazon Simple Storage Service (Amazon S3), Amazon Virtual Private Cloud (Amazon VPC), and Amazon CloudWatch, providing a complete solution for database management in the cloud.
DynamoDB is a highly durable and available database. All data is automatically replicated across multiple Availability Zones within AWS Regions to provide built-in redundancy and high availability. Data is automatically replicated to multiple Availability Zones to maintain high availability and durability in the event of an infrastructure outage or failure. DynamoDB uses synchronous replication across all Availability Zones to give you a low-latency read and write experience.
Amazon RDS is a cloud-based relational database service designed to make it easy to set up, operate, and scale a relational database in the cloud. Amazon RDS provides two scalability options to help you manage the growth of your database workloads: vertical scaling and reads replicas. Vertical scaling, also called scaling up, means increasing the compute and storage capacity of your Amazon RDS DB instance. When you scale up, you can improve your DB instance’s CPU, memory, and storage resources. On the other hand, read replicas are copies of your DB instance that can be used to offload read traffic from your primary DB instance.
DynamoDB is designed to scale horizontally. This means additional servers can be added to the system to increase capacity and throughput. DynamoDB can also scale vertically, which means that the ability of a single server can be improved.
AWS and you both share responsibility for security. This is referred to as cloud security and cloud security in the shared responsibility model.
Cloud security: AWS is in charge of defending the system that powers AWS services on the AWS Cloud. You can use the security services that AWS offers you. As part of the AWS compliance processes, external auditors regularly examine and confirm our security’s efficacy. Visit AWS services in scope by compliance program to learn more about the compliance programs that concern Amazon RDS.
Protection in the cloud The AWS service you utilize will decide your obligation. Other matters, such as the sensitivity of your data, the needs of your company, and any applicable laws, are also your responsibility.
Amazon DynamoDB offers a highly resilient storage system for mission-critical and primary data storage. In an Amazon DynamoDB Region, data is redundantly stored across numerous devices and locations.
Users’ data kept at rest, in transit between on-premises clients and DynamoDB, and between DynamoDB and other AWS services located in the same AWS Region are all protected by DynamoDB.
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Utilize keys that you control with AWS KMS to encrypt your databases. When encryption is turned on, all automated backups, read replicas, and snapshots of the data stored at rest are also encrypted.
Enables transparent data encryption in Oracle and SQL Server.
Enables the secure transmission of data via SSL.
By default, DynamoDB encrypts data in transit using encryption keys kept in AWS KMS.
Point-in-time recovery for your database instance is made possible via the automated backup feature. Database snapshots are user-initiated copies of your instance retained in Amazon S3 until they are specifically deleted.
Point-in-time recovery (PITR) offers continuous backups of the data in your DynamoDB tables, allowing you to restore the table to any second during the 35 days prior. You can create complete backups of the data in your DynamoDB tables for data archiving using on-demand backups and restore.
Amazon RDS will apply the most recent patches to databases. You can choose whether and when your database instance is patched.DynamoDB maintenance.
No maintenance since DynamoDB is server-less.
You can pay for Amazon RDS using Reserved Instances or On-Demand pricing. Utilize the AWS Pricing Calculator to forecast your monthly expenses.
See the price pages for RDS Performance Insights and RDS Proxy for information on Amazon RDS feature-level pricing.
For the Amazon Free Tier, you get the following:
DynamoDB has two capacity modes, which come with specific billing options for processing reads and writes on your tables: on-demand and provisioned:
When you use DynamoDB’s on-demand capacity mode, you will be charged for any data reads and writes that your application makes to your tables. Because DynamoDB automatically adapts to your workloads as they ramp up or down, you do not need to specify the read and write throughput your application is expected to deliver.
When using the provisioned capacity mode, you can specify how many reads and writes per second your application will need. To assure application performance while cutting expenses, you can utilize auto-scaling to dynamically adjust your table’s capacity based on the desired utilization rate.
Amazon RDS is mostly used in traditional applications, ERP, CRM, and e-commerce. Such applications do not require fetching data in real-time and are used in a relatively large scale.
Amazon DymanoDB is often used in internet-scale applications, real-time bidding, shopping carts, customer Preferences, content management, Personalization, and mobile applications.
Now, that you have a better understanding of these two database services, it’s worth noting that the service you choose will depend on your specific use case and requirements.
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