Setting up a database before cloud technology was an expensive and time-consuming procedure. Databases may now be started with only a few mouse clicks as companies provide more efficient and scalable platforms. Amazon Web Services manages and offers two superior cloud-based database services: DynamoDB and Amazon RDS (AWS).
Both of these services are widely used across the world. DynamoDB is a NoSQL database service by AWS. It’s quick, reliable, and scalable, making dealing with NoSQL data easier. On the other hand, Amazon RDS stands for “Relational Database Service”, and it is mainly used for structured and relational data (SQL).
In contrast to DynamoDB, RDS refers to the service rather than the database engine. As a result, we have six database engines to select from, including Amazon Aurora, MySQL, and MariaDB. This post will explain these two services and how they compare and contrast.
What is DynamoDB?
Amazon DynamoDB is an unstructured NoSQL data database entirely managed by Amazon. AWS handles all of the “behind-the-scenes” administration for you using DynamoDB, ensuring that your key-value pair database works smoothly and efficiently.
DynamoDB is the same database that Amazon created for their retail website when other databases failed to meet their requirements. In addition, DynamoDB supports ACID transactions and can scale up and down automatically.
Why should you use DynamoDB?
Using relational databases may be time-consuming and costly in some situations. In these situations, NoSQL databases are the ideal option. DynamoDB is the perfect NoSQL database model if you currently use AWS services.
It’s very scalable, and scaling or backups don’t slow things down. As a result, this database solution is perfect for systems that need to read and write data quickly. Several scenarios in which DynamoDB can be used are listed below.
- Needs for a lot of I/O
- unstructured gaming data
- Bidding in real-time
- Shopping carts
- Mobile apps
What is RDS?
“Relational Database Service” is what Amazon RDS stands for. It allows customers to create a relational database on AWS. It’s a service that lets you create the ideal relational database.
You may pick from various instance types and database engines while using RDS. Amazon Aurora, MariaDB, PostgreSQL, MySQL, Oracle Database, and SQL Server are among the six database engines available with RDS. RDS is also easily connected with AWS Database Migration Service (DMS), making migrating your current database to the cloud easier.
RDS is also scalable and can be adjusted to match current demands with minimum downtime.
Why should you use Amazon RDS?
Amazon RDS is used to store relational and structured data. RDS is a solution for conventional applications, e-commerce platforms, enterprise-grade applications, and CRMs. It may also be used as a data warehouse source system.
Amazon RDS vs DynamoDB: The Difference in 2022
The main difference between Amazon RDS vs DynamoDB is speed. DynamoDB is recognized for its speed, as it can handle over 10 trillion requests in a single day, with peaks of over 20 million requests per second. In Amazon RDS, there are two SSD-backed storage choices. Cost-effective alternative for general-purpose use and a high-performance option.
Because SSDs have great IO performance, they have low latency and reaction time while reading and writing data. In addition, DynamoDB Accelerator (DAX), an in-memory cache, may improve read performance tenfold.
The reading time can be reduced from milliseconds to microseconds due to this. As a result, DynamoDB has low latency and maintains it within milliseconds, regardless of the size of the tables.
In Amazon RDS, the performance of the general-purpose solution is 3 IOPS per supplied GB and can scale up to 3000 IOPS. Per database instance, the high-performance option may give up to 40000 IOPS. Throughout the lifespan of the database instance, this high-performance IOPS rate is maintained.
Let’s talk about some other major differences.
Amazon RDS vs DynamoDB: Availability
The data in DynamoDB is automatically duplicated across three availability zones. On the other hand, users do not have the option of selecting availability zones; they can only specify a region. Data availability is ensured through data replication across many physical nodes, even in the event of power failures or major natural catastrophes.
The data flow between the remaining nodes will continue uninterrupted, even if any node fails.
The same applies to RDS, and Amazon RDS uses Multi-AZ deployments to improve the availability of its databases. As a result, the desired data will be made available in the event of a hardware loss.
Amazon RDS vs DynamoDB: Security
DynamoDB is a cloud-native database, and it has fine-grained access control using IAM policies to limit access. In short, it integrates very well with IAM. In addition, Amazon RDS also integrates smoothly with IAM for MySQL and PostgreSQL engine authentication.
You may isolate your database in your VN, connect to your on-premises system using industry-standard VPNs, manage firewall settings, and limit network access to your database instances, among other security capabilities of RDS.
Further, DynamoDB and RDS security is ensured by AWS KMS (Key Management Service) encryption keys. The KMS facilitates the creation, storage, and management of encryption keys. AWS handles these keys. Consumers can also use SSL to improve data security. Both are solid solutions in terms of security.
Amazon RDS vs DynamoDB: Pricing
For each database instance, Amazon RDS costs the user a monthly fee. RDS is also available on a pay-as-you-go basis. There are also several price levels depending on the database and the server capacity we utilize. Amazon RDS also provides an on-demand pricing model, like DynamoDB.
DynamoDB’s essential cost is based on how much data is read, written, and stored. The fees vary depending on the extra services the customer has subscribed to. There are two price tiers in dynamo DB -provisioned capacity and on-demand capacity mode. The payment is computed depending on the application traffic in on-demand mode.
This option allows the workload of the database instance to scale up and down as needed. There is no requirement for the user to define a throughput. This option is perfect if your database traffic is less predictable and you want to pay for what you need.
The user must define the reads and writes per second in provided capacity mode. Then, the user can utilize auto-scaling to alter the table size if necessary. We may utilize this technique if the app traffic is predictable and consistent.
Amazon RDS vs DynamoDB: Storage Size
Amazon RDS provides a variety of storage sizes for various databases, including 128 TB for Aurora, 64 TB for Oracle, MySQL, MariaDB, and PostgreSQL engines, and 16 TB for SQL Server. DynamoDB, on the other hand, does not have a storage limit and can accommodate tables of nearly any size.
In addition, the amount of tables per unit in RDS varies depending on the database engine; with DynamoDB, it is 256 tables per unit.
Amazon RDS vs DynamoDB: Maintenance
Amazon RDS will apply the most recent fixes to databases. In addition, you are given the ability to choose if and when your database instance is patched. Because DynamoDB is a serverless database, no maintenance is necessary.
Final words
The requirements of your applications determine the database you pick. To manage various application demands, many firms employ different database types. Further, the above-given comparison will help you identify a good choice for you. For example, RDS is a good option for structured and SQL-based data, whereas DynamoDB is good for NoSQL unstructured databases.
That’s it for this tutorial.
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Amit Doshi is a Cloud Engineer who has experienced more than 5 years in AWS, Azure, and Google Cloud. He is an IT professional responsible for designing, implementing, managing, and maintaining cloud computing infrastructure, applications, and services.