AWS RDS Vs Aurora: Everything You Need to Know

Delve into the nuances of Amazon RDS and Aurora in this concise comparison guide. Uncover their unique strengths, weaknesses, and suitability for diverse use cases. Whether it's performance benchmarks, cost considerations, or feature differentiators, gain the insights you need to navigate between these two prominent AWS database solutions effectively.

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AWS RDS Vs Aurora: Everything You Need to Know
successive.tech/blog/aws-rds-vs-aurora-everything-you-need-to-know/
Are you struggling to find the best AWS database for your project? Here’s the solution. This
blog post will address all your queries regarding AWS RDS and Aurora, including their
benefits, applications, and distinctions between them. If you read this article to the end, you
will be better able to select the suitable database for your application development needs.
In the current environment, businesses rely heavily on data as a vital resource to transform
their operations. Nevertheless, they need help as the data volume grows. We can solve this
problem with cloud databases using several forms of replication, distributed architecture, and
scalability.
The two most well-known cloud databases are AWS Aurora and AWS RDS. In this post, we
will compare AWS RDS vs AWS Aurora to help you select the best one for your needs. So,
let’s get started right away. 
AWS RDS: An Overview
Amazon Relational Database Service (Amazon RDS) is a web service that simplifies the
setup, operation, and scaling of relational databases on the AWS Cloud. It offers cost-
effective, scalable capacity for an industry-standard relational database and handles

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everyday database administration operations.
Amazon RDS is an easy-to-manage relational database solution emphasizing total cost of
ownership. It is easy to assemble, run, and expand to meet demand. Undifferentiated
database management tasks, including provisioning, configuration, backups, and patching,
are automated by Amazon RDS. With 8 engines and 2 deployment methods, Amazon RDS
allows users to configure databases to address their needs in minutes. Customers can pick
from various pricing choices to effectively manage costs and enhance performance with
features including AWS Graviton3-based instances, Optimized Writes and Reads, and Multi-
AZ with two readable standbys. Now, let’s dive deep into the pros and cons of AWS RDS to
understand it better.
Pros
Automated Patching & Backups
AWS RDS automates patching and backups, lowering developers’ workload while
guaranteeing that databases are kept up to date and backed up for data protection and
integrity.
Encryption at rest and in transit
RDS uses robust encryption technologies to protect data at rest and in transit, ensuring
compliance with security standards and safeguarding sensitive information from
unauthorized access.
No Hardware Maintenance Needed
With RDS, businesses don’t have to worry about hardware upkeep because AWS handles
the underlying infrastructure, allowing them to focus on their core operations without worrying
about managing actual servers.
Automated Log Shipping and Read Replica
RDS streamlines log shipping and facilitates read replica creation for increased scalability
and performance. This allows enterprises to offload read-heavy tasks and improve
application responsiveness.
Automated Additional Storage Allocation
RDS dynamically expands storage depending on consumption patterns, ensuring that
databases have the resources to handle growing data volumes without requiring manual
intervention. This optimizes cost-efficiency and scalability.
Point-In-Time Recovery

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RDS provides point-in-time recovery capabilities, allowing enterprises to restore databases
to specified points and reducing data loss due to unintentional deletions or errors.
Cons
Patching forces a Downtime
While patching is automatic, implementing patches may result in database downtime,
reduced application availability, and potentially affected company operations during
maintenance windows.
No Automated Performance Tuning
AWS RDS lacks automated performance tuning options, requiring user intervention to
maximize database performance, which may necessitate expertise and time investment from
IT teams.
No Automated Partition Management
RDS does not include automated partition management options. Thus, administrators must
manually maintain partitions for optimal database performance and resource utilization,
which may lead to inefficiencies and additional administration overhead.

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CPU and Storage Performance is not Guaranteed
CPU and storage performance in RDS instances cannot be guaranteed because it is
determined by various factors, such as instance type, workload characteristics, and resource
allocation, all of which can impact application performance and responsiveness.
Zero Data Loss is Not Guaranteed
Despite its backup and recovery features, AWS RDS can not ensure zero data loss, as data
loss might occur during backup intervals or due to other unanticipated events, emphasizing
the need for additional data protection methods.
No Automated Compression Management 
RDS does not support automated compression management, necessitating manual
configuration and control of compression settings to optimize storage consumption and
performance. This complicates database administration responsibilities.
Aurora: An Overview
Amazon Aurora (Aurora) is a fully managed relational database engine that supports MySQL
and PostgreSQL. We’ve already seen how MySQL and PostgreSQL combine the speed and
dependability of high-end commercial databases with the simplicity and cost-effectiveness of
open-source databases. Aurora supports the same code, tools, and applications you now
use with your MySQL and PostgreSQL databases. Additionally, it can give up to 5X
throughput of MySQL and 3X the throughput of PostgreSQL without requiring changes to the
existing applications.
Amazon Aurora offers exceptionally high performance and availability on a global scale and
completes MySQL and PostgreSQL compatibility at a fraction of the cost of commercial
databases. It has comprehensive compliance standards and robust security capabilities.
Aurora provides storage resilience by ensuring data durability across three AZs. Aurora
offers a 99.99% availability rate, and users can obtain local read performance via Global
Database when deployed across AWS Regions. Aurora can scale to hundreds of thousands
of transactions per second using serverless technology. Its zero-ETL interface with Amazon
Redshift provides near-real-time analytics on transaction data. To further understand Aurora,
let’s have a look at the pros and cons of it.
Pros
Fault Tolerance

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AWS Aurora includes a built-in fault-tolerant architecture with data replication across several
Availability Zones, delivering high availability and data durability while lowering the risk of
data loss and downtime.
High Performance
Aurora outperforms traditional databases regarding throughput and latency because of its
enhanced storage and processing capabilities.
High Availability
Aurora’s automatic failover and replication ensure excellent database availability, reducing
downtime and providing uninterrupted access to data for essential business operations.
Easy to Migrate
Aurora simplifies the migration process by integrating with existing MySQL and PostgreSQL
databases, reducing downtime and work necessary to convert to the Aurora platform and
ensuring smooth enterprise migration.
Highly Scalable 
Aurora provides seamless scalability, allowing enterprises to flexibly alter computing and
storage resources to meet changing workload demands while maintaining optimal
performance and cost-efficiency as data volumes expand.
Auto Scaling Capabilities
Aurora supports auto-scaling, which automatically modifies compute and storage capacity
based on workload patterns. This optimizes performance and resource consumption without
the need for manual intervention.

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Cons
Limited Resources for Small Instances 
Aurora deploys resources or applications on different instant types including smaller
instances having constrained computational resources, which could affect scalability and
performance for applications.
Additional Data Transfer Costs
Businesses utilizing Aurora may incur additional charges for data transport between
Availability Zones or regions, particularly in cases involving data replication and failover,
potentially raising overall operational expenses.
High Load Apps Result in High Cost 
High-load applications on Aurora may incur additional expenditures due to increased
resource use, potentially resulting to higher operational expenses for organizations with
heavy workloads.
Data Sovereignty

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Aurora’s data storage and processing may be subject to AWS data residency restrictions,
impacting data sovereignty and compliance obligations for enterprises operating in regulated
industries or geographic zones.
Complex Management 
Aurora databases necessitate expertise and resources because of their complicated
architecture and innovative functionality, complicating database management operations and
increasing enterprise operational costs.
AWS RDS Vs. Aurora
Let’s have a detailed comparison of both databases to help you understand which one would
better understand your business requirements.
Amazon Aurora vs. RDS: Architecture
Amazon Aurora’s cloud-native architecture separates computing and storage. Aurora saves
data to a shared cluster volume. This cluster is split between six storage nodes and three
AWS Availability Zones. This provides Multi-AZ resiliency, and Aurora automatically expands
or contracts your volume to meet your data storage requirements up to 128TiB. 
In comparison, RDS provides up to 64 TiB for the MySQL, MariaDB, Oracle, and
PostgreSQL engines and 16 TiB for the SQL Server engine. Furthermore, you do not need to
offer storage, saving you time and effort. Pricing also adjusts based on your data. Aurora
follows the “pay for what you use” paradigm of Amazon S3. The compute nodes handle
queries and communicate with the common cluster volume. You do have the same primary
and replica split. However, replication happens exclusively within the storage nodes. 
This means that no synchronous replication occurs between compute nodes. Nodes do not
have any persistent state either. In RDS, each node handles both computing and storage.
And, unlike Aurora, which is developed from the ground up for the cloud, RDS is more similar
to a typical database solution that operates in the cloud.
However, Amazon RDS streamlines databases’ creation, installation, and administration
directly on Amazon EC2 instances. With just a few clicks, you’ll have a database ready to
store your data on the AWS cloud. That design allows you to operate with up to five
alternative database engines: MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL
Server. You can also use RDS on-premises, such as Amazon RDS on AWS OutPosts and
the Database Migration Service, to migrate your data to the AWS Cloud. In comparison,
Aurora only supports MySQL and PostgreSQL.
Amazon RDS vs. Aurora: Performance and scalability

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Amazon RDS is a high-performance database that can scale up to 32 vCPUs and 244 GiBs
of memory. Discover more about AWS well architected review here. Aurora offers limitless
scalability, extending in 10 GB increments to ensure peak performance. RDS does not divide
computing and storage as Aurora does. New RDS replicas must copy all current data before
they can service requests. Furthermore, new replicas generate more replica traffic, which
can reduce your throughput rate. As a result, RDS restricts you to 5 replicas to keep
replication under control.
However, the service includes SSD-backed storage and dedicated hardware to improve
performance. Additionally, RDS allows you to select between Provisioned IOPS and General-
Purpose storage to maintain consistent IOPS performance. Furthermore, RDS on Amazon
EC2 lets you increase CPU, memory, IOPS, and storage separately. When you buy RDS on
AWS OutPosts for on-premises use, these services will work together rather than separately.
RDS provides additional DB instance classes than Aurora, including Standard, Memory-
Optimized, and Burstable, whereas Aurora only supports Memory-Optimized and Burstable.
However, Amazon Aurora Serverless allows you to automatically raise or decrease the
capacity of your computing nodes based on your data processing requirements. Amazon
Aurora’s independent computation and storage architecture, combined with support for on-
demand scaling (a serverless feature), allows Aurora to be faster, more durable, and recover
faster from failure than conventional Amazon RDS.
Amazon Aurora vs. RDS: Data backups and replication
Aurora can support up to 15 replicas, but RDS allows only 5. All Aurora clones and the
primary instance share the same volume, allowing millisecond replications. This is because
the replicas automatically access the data or updates held on the shared volume instead of
RDS MySQL, which requires it to be replicated to each instance before any can serve
requests.
Failovers are automatic here, and Aurora prevents data loss. You can also choose your
replica failover priority. In addition, Amazon Aurora enables Multi-AZ backups, which are
faster than RDS.
RDS on Amazon EC2 allows you to run a database instance across many AZs (Multi-AZ
resiliency) in the same AWS Region. The service simultaneously replicates your primary
database’s data to all standby EC2 instances in all Availability Zones. In a Multi-AZ system,
secondary databases can handle read traffic.
However, configuring failover in RDS is a laborious operation. Whenever a compute node
fails, RDS automatically replaces it. 
Amazon RDS vs. Aurora: Availability and durability

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RDS and Aurora’s automated backups provide point-in-time recovery of database instances.
RDS allows you to create backups of your instances, which are saved in Amazon S3. These
are user-initiated backups. RDS stores snapshots until you expressly delete them.
On Aurora, the Point-in-Time function allows you to restore your data anytime during the
retention period (35 days). You can also recover data from the last five minutes. Read this
blog about cloud cost analysis to further understand cloud computing costs. 
Furthermore, you can revert to your original database state without restoring data from a
backup. Aurora analyzes your drives and data blocks for mistakes and instantly replaces
them.
Amazon Aurora vs. RDS: Security, authentication, and encryption
Security on Amazon Aurora and RDS are similar to some extent. Both give you control over
who may access your database on a DB instance. The approach you use for managing
access to your RDS or Aurora database is determined by the task at hand. You can still
maintain network access control by running your database instance in an Amazon VPC.
You can also utilize AWS Identity and Access Management policies to grant permissions
(IAM database authentication). The IAM policies define who can administer your RDS or
Aurora resources.
Security groups allow you to control which EC2 instances or IP addresses can connect to
your database on a DB instance. Any effort to get access that is not approved by the policies
of the linked security group is strictly prohibited.
Transport Layer Security (TLS) or Secure Socket Layer (SSL) connections are also
supported for database instances running MySQL, PostgreSQL, MariaDB, Oracle, and
Microsoft SQL Server.
Amazon Aurora also supports Kerberos authentication, so you can authenticate users who
are connecting to your Aurora MySQL DB and PostgreSQL clusters.
Amazon RDS enhances data security by encrypting database instances and snapshots at
rest. 
Amazon RDS vs. Aurora: Pricing
Amazon’s RDS pricing depends on consumption. However, RDS billing is determined by
several criteria, including the database engine, DB instance type, DB region, purchasing
choice (Reserved or On-Demand), outbound data transfers, deployment style, and whether
you want more storage.

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You can use the RDS price parameters to estimate your costs in advance. This is not always
achievable with Aurora, particularly in the serverless mode.
Amazon Aurora pricing is based on pay-as-you-go, Reserved Instance, and On-Demand
models. Amazon Aurora invoicing is based on cluster setup and includes charges for
database instances, I/O, and storage. The invoice will also include any optional features you
choose to activate.
When to choose AWS RDS?
AWS Relational Database Service is the best option if your organization needs support
for various databases, including MySQL, PostgreSQL, Oracle, SQL Server, MariaDB,
etc.
You should utilize AWS RDS when you wish to conduct vertical scaling for database
adjustments and the workload varies or changes regarding translation volumes.
AWS RDS might be preferable if you use parameter groups to customize the database
parameter settings.
When to choose Aurora?
You want to reduce the replication lag and data latency to enable real-time functionality
in the apps.
The application needs more scalability with a Aurora database. As such, you may
utilize it to scale your database up or down dynamically based on demand.
The main goal of your application is to be compatible with the PostgreSQL and MySQL
database engines.
Amazon RDS and Aurora: How do they work together?
The following data shows how Amazon Aurora related to the conventional PostgreSQL and
MySQL engines offered by Amazon RDS:
When configuring new database servers with Amazon RDS, you can use either Aurora
PostgreSQL or Aurora MySQL as the DB engine.
Aurora leverages the well-known management and administrative tools of Amazon
Relational Database Service (Amazon RDS). Aurora manages standard database operations
like provisioning, patching, backup, recovery, failure detection, and repair using the Amazon
RDS AWS Management Console interface, AWS CLI commands, and API activities.
Instead of working with single database instances, aurora management tasks usually include
large clusters of database servers synchronized by replication. The largest MySQL and
PostgreSQL deployments are easy to set up, run, and scale because of the automatic
clustering, replication, and storage allocation.

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One-way replication or snapshot creation and restoration are two ways to import data from
Amazon RDS for PostgreSQL and MySQL into Aurora. Push-button tools can also help you
migrate your current RDS for PostgreSQL and RDS for MySQL applications to Aurora.
Looking for Database Migration Solutions- Let us help you!
To put it briefly, the cloud databases offered by AWS are Aurora and RDS. The databases
are fault-tolerant, scalable, and effective. Nonetheless, the project’s business requirements
will determine which database is best. RDS is a good option for data storage if you want to
manage a database configuration that works with several different database engines.
However, AWS Aurora can be preferable if scalability is the primary issue regarding MySQL
and PostgreSQL compatibility. Using the information above, you must utilize the best AWS
service for your project.