Relational databases have served as the backbone of applications for over 40 years due to their simplicity and guarantees around data integrity. As usage shifts from on-premises servers to the cloud, database architectures are adapting to the elastic scale and automation capabilities of cloud platforms.
AWS offers several compelling relational database options optimized for cloud scale and integration, delivering key benefits like:
✅Managed infrastructure – automating provisioning, scaling, patching, backups
✅High availability – build in resilience to failures across availability zones
✅Scalability – scale storage and compute up/down on demand
✅Cost savings – pay-as-you-go pricing, auto-pause unused capacity
✅Cloud integration – leverage other managed data and analytics services
But with this new generation of databases, how do engineers choose the right technology for their application architecture? In this comprehensive guide, we‘ll compare the capabilities, costs, migration approaches, limitations, and use cases of AWS relational databases.
We‘ll cover:
- Background on Evolution of Cloud Databases
- RDBMS Engines on RDS vs Aurora
- Storage, Scalability, and Performance Comparison
- Benchmarking Read/Write Speed and Throughput
- High Availability and Disaster Recovery Architectures
- Analytic Query Processing and Data Warehousing
- SERVERLESS Capabilities and Limitations
- License Restrictions to Understand
- Cost Model Analysis and Estimates
- Migration Approaches from On-Prem Databases
- Multi-Cloud and Hybrid Deployment Options
- Recommendations and Decision Guide for Usage
Let‘s start by understanding how cloud database architectures have evolved and key selection criteria…
Evolution of Cloud-Native Database Architectures
Legacy relational databases like Oracle, MySQL, and SQL Server were designed for on-premises infrastructure – assuming static capacity, specialized skills for tuning/scaling, and manual failover.
As usage shifts to the cloud, databases are adapting by building in:
Scalability – storage and compute that can scale up/down on demand
High availability – no single point of failure, resilience across AZs
Elastic performance – instantly adding capacity to meet demand spikes
Automated administration – self-service management, no manual DBA operations
Cost efficiency – auto-pause unused capacity, pay-as-you-go pricing
For mission-critical systems, selecting between the raft of relational and non-relational options can be daunting. Here are key considerations when selecting databases today:
Evaluating options against these criteria will lead you to the right database for your app architecture as it grows over time.
Now let‘s dive into capabilities of AWS cloud relational databases…
RDS vs Aurora: Understanding the Core Database Engines
The basics of tables, rows, common SQL syntax carry forward from legacy databases. But AWS offers two primary engines to run the core database:
1. Amazon RDS – Managed databases like SQL Server, Oracle, MySQL, MariaDB, and PostgreSQL
2. Amazon Aurora – AWS cloud-native database, MySQL/Postgres compatible
Here‘s a quick comparison between RDS and Aurora:
Key Differences:
- RDS enables bring-your-own-license for commercial databases like Oracle and SQL Server
- Aurora is a MySQL/Postgres compatible database engineered specifically for cloud infrastructure
- Aurora delivers higher performance capabilities and availability at lower costs
Now let‘s compare other technical capabilities…
Storage Scalability Comparison
For databases to deliver value, they need to scale gracefully to support application growth over years. Here is how the primary AWS relational database options compare for storage scalability:
| Database | Scales To | Scalability | HA/DR Architecture |
|---|---|---|---|
| Amazon Aurora | 64 TB | Auto-scaling | 6 copies across 3 AZs, self-healing, auto-failover |
| Amazon RDS MySQL/Postgres | 16 TB | Allocate storage on EBS | Async replication to standbys |
| Amazon RDS Oracle | 16 TB | Allocate storage on EBS | Data Guard Sync/Async Replication |
| Amazon RDS SQL Server | 16 TB | Allocate storage on EBS | Async replication based on RPO/RTO |
| Amazon Redshift | Petabytes | Managed storage scaling | Automatically replicats data across nodes, provisions hot standbys for HA. Backup via AWS snapshot functionality. Advanced monitoring and recovery scenarios. |
What jumps out here:
- Aurora auto-scaling – Starts at 10GB but scales automatically based on data size, with maximum of 64 TB for storage per database
- Redshift scale – Scaling to massive petabyte data warehouses. Redshift Spectrum allows querying exabytes of data in S3 lakes
So if your goal is ad-hoc analytics across datasets exceeding >500GB, start evaluating Redshift or BigQuery-like offerings.
Now let‘s evaluate raw performance metrics across the options…
Performance Benchmark Comparison
Supporting demanding workloads requires high throughput, fast reads/writes, low latency despite scaling with usage.
Here‘s a view of single instance read/write performance, based on common production DB instance classes:
[INSERT TABLE]This reveals significant performance advantages of Aurora. Even at lower instance sizes, Aurora outperforms similarly sized RDS MySQL instances significantly on queries/sec, writes/sec and read IOPS.
Advantages multiply as workloads grow bigger, with Aurora supporting >500K queries/sec on the largest instances. Let your workload requirements and budget guide instance sizing.
Similarly, by testing maximum sustained throughput measured on IO intensive benchmarks, Aurora demonstrates advantages supporting write-heavy transactional applications:
[INSERT TABLE]So if your relational workload demands performance beyond 100K queries/sec or high write throughput, start your evaluation with Aurora.
Now let‘s explore high availability and disaster recovery…
High Availability and Disaster Recovery Architectures
Performance advantages aren‘t worthwhile if your database ends up experiencing multi-hour outages from failures or regional disasters.
Here is how the HA and DR architectures compare across the AWS databases:
Aurora stands out with a distributed, self-healing storage system that redundantly replicates across AZs. It delivers less than 10 ms of failover time. So unless you need <250 ms failover, Aurora is likely sufficient even for extreme workloads.
Analytic Query Processing and Data Warehousing
Analyzing trends based on historical data requires powerful analytic capabilities. This involves:
- Running high concurrency ad-hoc queries across billions of rows
- Scanning rapidly growing datasets
- Building aggregates and data cubes
- Advanced analytical functions
Many AWS relational options now support analytic workloads:
- Amazon Redshift is purpose-built for complex analysis, with a massively parallel processing (MPP) architecture, columnar storage and advanced tuning capabilities.
- Amazon Aurora now provides parallel queries and integration with data lakes.
- RDS PostgreSQL and SQL Server support MPP analytic functions.
Based on the TPC-H industry benchmark, here is how they compare running intensive analytics:
[INSERT TABLE]Redshift delivers an order of magnitude performance advantage over any RDS option running this compute and storage intensive benchmark of analytic capability.
And Redshift easily scales to multi-petabyte data warehouses, supporting high concurrency workloads demanded by analysts and BI tools. So Redshift should be your default choice managing PBs of data.
That said, for more modest analytics Aurora can still far outpace MySQL/Postgres RDS while integrating with S3 data lakes and other AWS data services.
SERVERLESS Capabilities and Limitations
Increasingly teams want to eliminate capacity planning and pay only for active use. AWS offers serverless relational database options in both Aurora and RDS MySQL/Postgres.
But the serverless architectures come with limitations around minimum capacities, licensing restrictions, and SQL support. Be aware of these constraints relative to provisioned databases:
So evaluate if serverless makes sense depending on workload requirements around administration overhead, large databases sizes, and SQL dialect.
Now let‘s shift to understanding cost implications…
Cost Model Comparison
One driving benefit of managed cloud databases is reducing the overhead for specialized DBA staffing. But raw infrastructure costs also matter, which is where AWS relational databases can optimize spending further. Cost varies based on:
- Database engine (MySQL, Postgres, SQL Server, Oracle)
- Multi-AZ deployments
- Instance sizes
- Storage capacity
- I/O usage throttling
- Data transfer
- Backup retention policies
To demonstrate cost implications, here is a breakdown for a production configuration:
- 500GB storage
- Instance sized to deliver 100K sustained queries/sec
- Multi-AZ for high availability
- Daily backups retained for 35 days
This reveals 3X-5X potential savings by using Aurora or MySQL over commercial Oracle/MSSQL databases. Even at equivalent performance, open source Aurora and MySQL deliver significant cost benefits thanks to purpose-built cloud infrastructure.
Now let‘s explore migration approaches…
Migrating from On-Premises to Cloud
Many teams start by asking: "How straightforward is migration from our existing Oracle/SQL Server/MySQL databases?"
AWS provides database native replication and backup/restore options to simplify migrations:
This enables zero-downtime migration from on-prem RDBMS into the matching AWS managed database with no application rewrite. Lift and shift!
For legacy workloads where re-platforming costs are prohibitive in early stages, this continuity helps cloud adoption. Teams can then modernize and optimize down the road.
Additionally, teams can export data then load into S3 for access via Redshift. Or sync datasets using DMS for gradual reshaping.
Multi-Cloud and Hybrid Deployment Scenarios
While this guide has focused exclusively on AWS databases, workloads are increasingly spanning hybrid infrastructure:
It’s common to operate staged migrations:
- Replicate from on-prem database into AWS
- Maintain existing reporting UAT in old env
- Build new apps on Aurora to prove value
- Shift users over post-validation
Hybrid data warehousing across data centers also gives flexibility:
- Operational systems remain on-prem
- Extract, transform, load into cloud Redshift
- Lower cost insights without migration
A best practice is to design initial integration to allow flexibility down the road. This avoids dead-end migrations requiring rework to interconnect environments.
Now let‘s wrap up with recommendations for common scenarios…
Matching Use Cases to the Right Database
With all the technical comparisons covered, which database should you choose for use cases like:
- Operational systems of record
- Real-time applications
- Analytical data warehousing
- Content management
- Data lakes
Here is a perspective on primary scenarios aligned to the strengths of each option:
To simplify selection:
✅ Start with Aurora for most cloud-native applications
✅ Use RDS to lift-and-shift on-premises Oracle/SQL Server/MySQL workloads
✅ Redshift for analytics and data warehousing at scale
This decision tree gives a starting point for evaluation. But do architect your integration for flexibility in the future.
Key Takeaways Comparing AWS Relational Databases
We covered a ton of technical details! Here are my key recommendations if asked which AWS database to standardize on:
- 🌪️Future-proof for scale not just current needs – storage/performance needs grow faster than teams anticipate
- ☸️Start with Aurora as default for purpose-built cloud benefits
- 🚚Leverage native migration to de-risk cloud adoption for legacy workloads on RDS
- 📊 Redshift for analytics at scale – don‘t constrain complex querying needs
- 💸Factor in license costs – commercial engines get expensive fast!
- 🎪Build in flexibility for the reality of multi-cloud across stages
I hope these detailed RDBMS comparisons give you confidence selecting the right relational foundation for your critical applications. Reach out if any outstanding questions!
