How To Choose the Right AWS Database for Your Application

Migrating applications to the cloud often involves moving your databases. As a cloud architecture advisor, one of the top questions I get asked is – which AWS database should I use?

With so many options to pick from, making the optimal choice can be confusing based on your specific needs. This comprehensive guide will take a closer look at various databases in terms of how they differ, their pros and cons, when to choose them, real-world examples, and more.

So if you are wondering whether to opt for relational, NoSQL, in-memory or a ledger database, read on as I share my insights from years of experience designing high performance, secure and scalable systems on AWS.

The Growing Demand for Cloud Databases

First, let me share some statistics that highlight the accelerating adoption of cloud databases:

  • According to Gartner, over 75% of all databases will be deployed on cloud platforms by 2022
  • By 2025, Gartner predicts 100% of new databases will be born on the cloud
  • Amazon AWS and Microsoft Azure control over 50% of the worldwide database-as-a-service market
  • MongoDB Atlas and Redis Enterprise Cloud also seeing 60%+ growth on their cloud database services

What‘s driving this rapid migration from on-premise databases to cloud databases?

Lower TCO: Cloud databases eliminate the need to procure expensive servers and storage hardware upfront. Instead you pay-as-you-go based on actual usage, which studies show reduces TCO by 50% or more.

Automated Administration: With cloud databases, you offload routine tasks like hardware provisioning, setup, patching, replication, and backups to the cloud provider. This leaves your team free to focus on higher-impact application improvements.

Global Scale: Cloud databases make it easy to replicate across regions and geo-distribute your workloads based on demand patterns. This enables use cases with billions of users and near real-time global access.

Faster Innovation: Leveraging purpose-built cloud database services reduces time-to-market for releasing new features from months to weeks or days. Their turnkey integrations with complementary services also fuel innovation.

Key Considerations for Choosing a Cloud Database

But not all cloud databases are made equal. Getting the decision right depends on assessing options based on your priorities across:

Performance: Read/write latency, throughput capacity and scalability to handle workload fluctuations.

High Availability: Multi-region configurations, replication techniques and auto-recovery from failures or outages.

Security: Encryption, network isolation, access control and auditing capabilities.

Cost: Infrastructure and licensing costs especially as data volumes grow over time.

Functionality: Query, indexing, storage and other technical capabilities including analytics support.

Ease of Use: How quickly you can configure, operate and build applications without deep database expertise.

Ecosystem Integration: Out-of-the-box integration with complementary data analytics, machine learning and other cloud services.

Now let‘s do a deep-dive into the most popular relational and NoSQL databases available on AWS across these considerations.

AWS Relational Databases Compared

Relational databases store data in tables with pre-defined schema and use SQL for managing and accessing data. They are a staple for transactional applications as well as reporting and analytics use cases.

Here is a head-to-head comparison across popular relational database options on AWS.

Relational database comparison

Relational Database Comparison (Source: Geekflare)

As you can see, Amazon Aurora wins on most criteria because of its modern cloud-native architecture optimized for AWS. Cost can be up to 68% lower compared to commercial databases for equivalent workloads.

However, if you have already invested in Oracle or SQL Server licenses, lifting and shifting those to Amazon RDS allows leveraging existing skills while improving cost, security and availability.

MySQL, PostgreSQL and MariaDB relational databases have their place for open source centric applications or smaller scale use cases where licensing costs are critical. But they lag in enterprise grade capabilities.

When To Choose a Relational Database

Here are some common scenarios where relational databases would be my first choice:

  • Transactional applications needing ACID compliance with data consistency safeguards
  • Legacy systems previously running Oracle, MySQL or SQL Server
  • Analytics applications leveraging SQL querying for business intelligence
  • Applications requiring complex joins, aggregations and referential integrity

If these match your requirements, delve deeper into the relational database comparison above before deciding on Amazon Aurora or RDS options.

Next let us explore popular NoSQL and ledger databases on AWS.

Evaluating NoSQL and Ledger Databases on AWS

While relational databases form the backbone of business applications, alternative non-relational or NoSQL databases play complementary roles in modern cloud architecture.

NoSQL Database Comparison

NoSQL Database Comparison (Source: Geekflare)

Here is an overview of purpose-built AWS NoSQL and ledger databases available:

Amazon DynamoDB delivers single-digit millisecond performance for internet scale applications needing consistent response times. As a fully-managed service, it auto-scales capacity while handling trillions of requests per day across AWS customers.

Amazon DocumentDB provides MongoDB compatibility while adding scalability, availability and security enhancements. It handles JSON document workloads in a cost-optimized way.

Amazon Timestream optimizes storage and analysis of time series data gathered from industrial assets, connected devices and telemetry sensors. It aligns with IoT and DevOps use cases requiring temporal analytics at scale.

Amazon QLDB brings immutable, cryptographically verifiable ledger functionality using a transparent SQL interface. It suits high integrity workloads like financial transactions and manufacturing supply chains.

Amazon ElastiCache offers fully-managed Redis and Memcached engines to power ultra-fast in-memory caching, which boosts performance of web and mobile apps by reducing latency 10x.

Amazon Neptune provides best-in-class graph database functionality for building recommendation engines, fraud detection and knowledge graphs supporting billions of relationships.

When To Consider a NoSQL or Ledger Database

So when should you opt for a NoSQL or purpose-built database over relational options?

Here are pointers from real-world scenarios where I would recommend a NoSQL or other specialized database:

  • Applications with large volumes of time series IoT data requiring temporal analytics at scale
  • Systems demanding millisecond response times with predictable ultra-low latency
  • Chat, gaming or social media apps needing blazing fast data caching
  • Blockchain applications with distributed ledger requirements for immutable secure record keeping
  • AI/ML apps with relationship graph analytics across people, places and things
  • Legacy apps constrained by MongoDB limitations that need greater scalability

By assessing your application‘s specific data access patterns, content types and performance needs against database capabilities, you can narrow down the optimal choice.

Architecting Multi Database Deployments on AWS

Now that we‘ve compared individual database options, let‘s discuss how you can combine them in cloud-native architectures.

Applications often use multiple databases in different roles:

Multi Database Architecture

Multi Database Application Architecture (Source: Geekflare)

For example:

  • Applications may use DynamoDB to power high velocity user interactions needing millisecond response times
  • While Redshift handles analytics performed periodically against aggregate data sets
  • With SQL Server transactional data stored in Amazon RDS for long term retention

This multi-database pattern delivers optimal performance, cost and scale across different data types and access patterns.

Here are some leading practices to integrate disparate database effectively:

  • ETL pipelines to synchronize data across databases
  • Unified metadata store enabling governance across databases
  • Pub/sub messaging for event driven data movement
  • Query federation to join data views across databases

Multi-region deployment topologies provide business continuity and local latency across geographic locations:

Multi Region Database

Multi-Region Database Architecture (Source: Geekflare)

AWS makes it easy to set up regional replicas and failover end-points for your databases automatically.

Over the last decade, I have helped various enterprises and cloud-native startups optimize their data persistence strategies leveraging combinations of relational, NoSQL and purpose-built databases on AWS. Reach out if you need any guidance.

Now over to you – which database are you planning to use? Share your questions or feedback on the comparisons above. I‘m happy to help clarify or recommend the best fit.

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