Overview

Amazon DynamoDB

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Votes195

Amazon RDS for PostgreSQL

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Votes40

Amazon DynamoDB vs Amazon RDS for PostgreSQL: What are the differences?

Introduction

Amazon DynamoDB and Amazon RDS for PostgreSQL are both popular database services provided by Amazon Web Services (AWS). However, they have key differences that make them suitable for different use cases.

Data Model: DynamoDB is a NoSQL database that uses a key-value store data model, allowing for flexible schema designs. On the other hand, Amazon RDS for PostgreSQL is a relational database, following a fixed schema with tables and rows.
Scalability: DynamoDB is designed to scale horizontally by automatically partitioning data across multiple servers. This allows for virtually unlimited storage and throughput as the workload scales. In contrast, Amazon RDS for PostgreSQL can scale vertically by upgrading the hardware running the database but has limitations on storage and throughput based on the chosen instance type.
Query Capabilities: DynamoDB offers fast and efficient querying based on primary key and secondary indexes. It also provides advanced querying capabilities like filtering, sorting, and aggregation. In comparison, Amazon RDS for PostgreSQL supports complex SQL queries, including joins and advanced data manipulations.
Data Consistency: DynamoDB offers eventual consistency by default, where data modifications may take some time to propagate across all replicas. However, it also provides the option for strong consistency, ensuring immediate data consistency. On the other hand, Amazon RDS for PostgreSQL supports ACID transactions and offers strong consistency by default, ensuring immediate data consistency.
Backup and Restore: DynamoDB automatically takes incremental backups and provides point-in-time recovery for up to 35 days. It also offers cross-region replication for disaster recovery. In contrast, Amazon RDS for PostgreSQL provides automated backups with adjustable retention periods and supports cross-region automated backups. It also allows manual snapshots for longer-term backups.
Pricing Model: DynamoDB pricing is based on provisioned throughput, storage, and data transfer. Users pay for the consistent throughput they provision and the storage they consume. Amazon RDS for PostgreSQL pricing is based on instance types, storage, and data transfer. Users pay for the size of the chosen instance type, allocated storage, and data transfer.

In summary, Amazon DynamoDB and Amazon RDS for PostgreSQL differ in their data models, scalability options, query capabilities, data consistency models, backup and restore features, and pricing models. These differences make each service suitable for different application requirements and use cases.

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Advice on Amazon DynamoDB, Amazon RDS for PostgreSQL

Doru

Solution Architect

Jun 9, 2019

Reviewon

Amazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

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akash

Aug 27, 2020

Needs adviceon

Cloud Firestore

Firebase Realtime Database

Amazon DynamoDB

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

199k views199k

Comments

Lonnie

CEO - Co-founder US, Mexico Binational Tech Start-up Accelerator, Incubator at Framework Science

May 9, 2019

Reviewon

Amazon DynamoDB

Amazon RDS for PostgreSQL

We use Amazon RDS for PostgreSQL because RDS and Amazon DynamoDB are two distinct database systems. DynamoDB is NoSQL DB whereas RDS is a relational database on the cloud. The pricing will mainly differ in the type of application you are using and your requirements. For some applications, both DynamoDB and RDS, can serve well, for some it might not. I do not think DynamoDB is cheaper. Right now we are helping Companies in Silicon Valley and in Southern California go SERVERLESS - drastically lowering costs if you are interested in hearing how we go about it.

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Comments

Detailed Comparison

Amazon DynamoDB	Amazon RDS for PostgreSQL
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.	Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS.
Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture	Monitoring and Metrics –Amazon RDS provides Amazon CloudWatch metrics for you DB Instance deployments at no additional charge.;DB Event Notifications –Amazon RDS provides Amazon SNS notifications via email or SMS for your DB Instance deployments.;Automatic Software Patching – Amazon RDS will make sure that the PostgreSQL software powering your deployment stays up-to-date with the latest patches.;Automated Backups – Turned on by default, the automated backup feature of Amazon RDS enables point-in-time recovery for your DB Instance.;DB Snapshots – DB Snapshots are user-initiated backups of your DB Instance.;Pre-configured Parameters – Amazon RDS for PostgreSQL deployments are pre-configured with a sensible set of parameters and settings appropriate for the DB Instance class you have selected.;PostGIS;Language Extensions :PL/Perl, PL/pgSQL, PL/Tcl;Full Text Search Dictionaries;Advanced Data Types : HStore, JSON;Core PostgreSQL engine features
Statistics
Stacks 3.7K	Stacks 823
Followers 3.2K	Followers 607
Votes 195	Votes 40
Pros & Cons
Pros 62 Predictable performance and cost 56 Scalable 35 Native JSON Support 21 AWS Free Tier 7 Fast Cons 4 Only sequential access for paginate data 1 Document Limit Size 1 Scaling	Pros 25 Easy setup, backup, monitoring 13 Geospatial support 2 Master-master replication using Multi-AZ instance
Integrations
PostgreSQL MySQL SQLite Azure Database for MySQL	No integrations available

What are some alternatives to Amazon DynamoDB, Amazon RDS for PostgreSQL?

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Heroku Postgres

Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

ElephantSQL

ElephantSQL hosts PostgreSQL on Amazon EC2 in multiple regions and availability zones. The servers are continuously transferring the Write-Ahead-Log (the transaction log) to S3 for maximum reliability.

Google Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

CloudBoost

CloudBoost.io is a database service for the “next web” - that not only does data-storage, but also search, real-time and a whole lot more which enables developers to build much richer apps with 50% less time saving them a ton of cost and helping them go to market much faster.

Firebase Realtime Database

It is a cloud-hosted NoSQL database that lets you store and sync data between your users in realtime. Data is synced across all clients in realtime, and remains available when your app goes offline.

restdb.io

RestDB is a NoSql document oriented database cloud service. Data is accessed as JSON objects via HTTPS. This gives great flexibility, easy system integration and future compatibility.