DynamoDB vs Amazon RDS: When to Use NoSQL or SQL

Choose DynamoDB when your application fits known key-based access patterns and you want managed partitioning with request-based billing. Choose Amazon RDS when relational tables, joins, constraints and an existing SQL engine are central to the application. RDS is a family of managed relational database options, not a NoSQL engine, and its behavior depends on the engine and deployment you select.

DynamoDB and Amazon RDS: an overview

DynamoDB is a managed key-value and document database with its own AWS APIs. Records can have different attributes, including nested data, but efficient queries depend on the primary key and indexes.

Amazon RDS manages relational engines including PostgreSQL, MySQL, MariaDB, Oracle Database, Microsoft SQL Server and IBM Db2. Amazon Aurora is also part of the RDS family, with a distinct architecture and Serverless options. The Aurora comparison covers those separately.

Both services manage infrastructure work. RDS retains more engine-level choices, such as instance sizing, database parameters and version upgrades. DynamoDB removes those choices but requires more explicit access-pattern modeling. Neither eliminates the application's responsibility for correct data updates and efficient queries.

DynamoDB vs RDS at a glance

Query design and transactions

A relational model fits data with relationships that applications query in different ways. An RDS database can keep customers, invoices and payments in separate tables, join them and enforce supported constraints. The exact capabilities come from the selected engine.

DynamoDB favors access patterns defined up front. You might place related items under one partition key, duplicate selected attributes or maintain an index for another lookup. This can make frequent application requests efficient, but it changes how the application handles new queries and repeated data.

DynamoDB's PartiQL syntax does not provide arbitrary SQL joins. Conversely, storing JSON in PostgreSQL or MySQL does not make every query efficient without indexes. Choose based on actual reads and writes rather than equating structured data with SQL and unstructured data with NoSQL.

DynamoDB transactions support up to 100 unique items and 4 MB of aggregate item data within one account and Region. They are unavailable on MRSC global tables. RDS transactions use the chosen engine's isolation and locking behavior, so test conflicts and retries for the workflow that matters.

For example, updating an order and its inventory reservations may require a transaction in either model. A broad reporting query over all historical orders may fit SQL more naturally even if individual order lookups fit DynamoDB well.

Performance and storage

DynamoDB

DynamoDB is designed for low-latency operational requests that touch a limited set of items. Key distribution, item sizes and query shape determine how efficiently the application uses it. A filter applied after a scan does not turn that scan into an indexed lookup.

DynamoDB indexes add alternate access patterns but also consume storage and write resources. DAX can help repeated eligible reads, while hot writes still require appropriate data modeling. There is no universal tenfold acceleration for every workload.

Amazon RDS

RDS performance depends on the engine, instance CPU and memory, storage type, indexes, query plans and connection load. Storage IOPS alone does not predict transaction latency. A lock wait or a poorly chosen join can dominate a request even when storage has spare capacity.

Current RDS storage options include General Purpose SSD and Provisioned IOPS configurations with engine-specific limits. Check the exact storage type, throughput limits and instance support.

Likewise, there is no single RDS maximum database size. Avoid copying one engine's limit to another or treating Aurora's storage architecture as ordinary RDS storage. Benchmark the intended deployment using realistic data volume and concurrent connections.

Availability and durability

DynamoDB replicates data across Availability Zones within a Region. Multi-Region global tables provide additional replication options, but availability still depends on the application's retry and routing behavior.

RDS offers Single-AZ and supported Multi-AZ deployments. The Multi-AZ documentation distinguishes two configurations: a Multi-AZ DB instance has a standby that does not serve reads, while a Multi-AZ DB cluster has two readable standby instances. Availability and read scaling are therefore related but different design decisions.

Read replicas are not a universal substitute for a failover configuration. Select a topology based on the chosen engine and recovery objectives, then test connection recovery during failover. A successful database failover still requires applications to reconnect and retry safely.

For regional disaster recovery, compare the appropriate RDS replication mechanisms with DynamoDB global tables. Replication is not a backup against accidental updates that are valid enough to replicate everywhere.

Security

DynamoDB integrates with IAM and resource-based policies and encrypts data at rest. You can choose the documented AWS owned, AWS managed or customer managed key option. Application data access is authorized through AWS requests.

RDS security combines network access, database users and permissions, encryption and supported authentication options. IAM permission to modify an RDS instance does not automatically grant SQL access to all its databases.

Use private network boundaries and TLS where appropriate, and manage credentials with supported mechanisms. IAM database authentication is engine-dependent; do not assume one feature list applies to every RDS engine. Audit both administrative changes and application-level data access according to the system's requirements.

Backups and restore

DynamoDB offers on-demand backups and PITR with a configurable 1-to-35-day window. A restore creates a new table. Restore planning also needs to account for consumers, endpoints and settings that must be configured on the recovered table.

RDS automated backups are retained according to the configured retention period and support point-in-time recovery. Manual snapshots are a separate mechanism. Automated backups should not be described as snapshots kept forever until explicitly deleted.

Test recovery through application cutover, not just snapshot creation. Confirm how much data can be lost, how long restore takes with a realistic dataset, and how the application handles external actions that occurred after the selected restore point.

Scalability: storage is not compute

DynamoDB on-demand capacity adjusts throughput for changing request volumes; provisioned capacity can use auto scaling. Both still have quotas and hot-key limits. A sudden spike can require pre-warming or a change in partition design.

RDS storage autoscaling increases allocated storage under documented conditions. It does not automatically scale database CPU, shrink storage afterwards or solve a saturated writer. Instance resizing and read-replica changes are separate operations.

Aurora Serverless adds a different compute-scaling model. On-demand and Reserved Instance pricing, meanwhile, are billing choices rather than scaling algorithms. Keep those concepts separate when comparing an RDS proposal with DynamoDB on-demand capacity.

Pricing

DynamoDB pricing includes reads, writes, storage and optional features. Item size, read consistency, transactions, index updates and multi-Region writes change usage. The DynamoDB pricing calculator provides a useful request-based estimate.

RDS pricing depends on engine, instance type, deployment, storage and applicable IOPS or throughput. Backup, transfer, licensing and additional services can add costs. Reserved options can reduce eligible compute cost with a commitment; they are not exclusive to Aurora.

Compare complete applications. A low-traffic DynamoDB workload may avoid continuously allocated compute, while a relational engine may express a complex workflow with fewer application-side operations. Neither a monthly storage rate nor an instance entry price establishes which architecture costs less.

Moving DynamoDB data to RDS, or using both

Moving from DynamoDB to RDS requires more than copying rows. Map item types and nested attributes to a relational schema, choose keys and constraints, and decide how to represent denormalized records. Validate type conversions, counts and business totals against a consistent source snapshot.

For ongoing synchronization, a DynamoDB Streams consumer can apply changes to a relational projection. It must handle duplicate delivery, ordering, deletes and replay. A snapshot backfill and live changes need a defined boundary so neither misses nor overwrites updates incorrectly.

Do not assume an AWS DMS connector works in both directions. Check the supported DMS sources for the actual direction and engine before planning a migration around it.

Using both can make sense when separate application components have different requirements. Keep one authoritative owner for each dataset, use explicit asynchronous propagation where needed and measure lag. Avoid application dual writes that can leave one database updated and the other stale without a recovery path.