Amazon DynamoDB vs Firebase Realtime Database

Overview

Amazon DynamoDB

Stacks4.0K

Followers3.2K

Votes195

Firebase Realtime Database

Stacks107

Followers229

Votes7

Amazon DynamoDB vs Firebase Realtime Database: What are the differences?

Amazon DynamoDB and Firebase Realtime Database are both popular NoSQL databases used for storing and retrieving data. Here are the key differences between the two.

Data Model: Amazon DynamoDB is a document and key-value store, which allows flexible schemaless data modeling. It supports nested data structures and allows complex data types like lists and maps. On the other hand, Firebase Realtime Database uses a JSON tree data structure, where data is organized hierarchically with key-value pairs.
Scalability: DynamoDB is designed for massive scalability and can handle millions of requests per second. It is built on a distributed architecture, allowing it to automatically scale up or down based on demand. Firebase Realtime Database also scales well and can handle high traffic loads, but it might be less suited for extremely large and complex applications compared to DynamoDB.
Querying and Indexing: DynamoDB provides flexible querying options with its primary key and secondary indexes. It supports both key-value queries and conditional queries. On the other hand, Firebase Realtime Database offers limited querying capabilities and does not support secondary indexes. It operates on a simple key-based lookup model, where querying is mainly based on the JSON tree structure.
Real-time Updates: Firebase Realtime Database excels in real-time data synchronization. It provides automatic synchronization across all connected devices in milliseconds, making it well-suited for real-time collaborative applications like chat or multiplayer games. While DynamoDB also supports stream-based updates and change notifications, it does not have the same level of real-time synchronization capabilities as Firebase Realtime Database.
Data Offline Support: Firebase Realtime Database has built-in offline support, allowing applications to continue functioning even when the device is offline. It caches data locally and syncs it with the cloud when the device reconnects. DynamoDB, on the other hand, requires developers to build their own offline support using other AWS services or third-party libraries.
Pricing Model: DynamoDB follows a pay-per-usage pricing model, where you pay for the provisioned throughput and storage consumed. It offers options to provision read/write capacity based on your anticipated demand. Firebase Realtime Database follows a more simplified pricing model based on data transferred and storage used. It provides a generous free tier, making it suitable for startups and small applications.

In summary, Amazon DynamoDB offers a flexible data model, massive scalability, and advanced querying capabilities, making it a great choice for large-scale applications with complex data structures. Firebase Realtime Database, on the other hand, excels in real-time data synchronization, offline support, and simple hierarchical data models, making it ideal for real-time collaborative applications.

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Advice on Amazon DynamoDB, Firebase Realtime Database

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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Detailed Comparison

Amazon DynamoDB	Firebase Realtime Database
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.	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.
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	Real time syncing for JSON data;Collaborate across devices with ease;Build serverless apps;Optimized for offline use;Strong user-based security
Statistics
Stacks 4.0K	Stacks 107
Followers 3.2K	Followers 229
Votes 195	Votes 7
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 Scaling 1 Document Limit Size	Pros 7 Very fast 0 Casandra Cons 2 Poor query
Integrations
Amazon RDS for PostgreSQL PostgreSQL MySQL SQLite Azure Database for MySQL	C++ iOS Unity Firebase Authentication Android OS Cloud Functions for Firebase

What are some alternatives to Amazon DynamoDB, Firebase Realtime Database?

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.

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.

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.

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.

Amazon DocumentDB

Amazon DocumentDB is a non-relational database service designed from the ground-up to give you the performance, scalability, and availability you need when operating mission-critical MongoDB workloads at scale. In Amazon DocumentDB, the storage and compute are decoupled, allowing each to scale independently, and you can increase the read capacity to millions of requests per second by adding up to 15 low latency read replicas in minutes, regardless of the size of your data.

Amazon SimpleDB

Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.

Datomic Cloud

A transactional database with a flexible data model, elastic scaling, and rich queries. Datomic is designed from the ground up to run on AWS. Datomic leverages AWS technology, including DynamoDB, S3, EFS, and CloudFormation to provide a fully integrated solution.

Related Comparisons

Amazon DynamoDB vs Firebase Realtime Database: What are the differences?

Amazon DynamoDB and Firebase Realtime Database are both popular NoSQL databases used for storing and retrieving data. Here are the key differences between the two.

Data Model: Amazon DynamoDB is a document and key-value store, which allows flexible schemaless data modeling. It supports nested data structures and allows complex data types like lists and maps. On the other hand, Firebase Realtime Database uses a JSON tree data structure, where data is organized hierarchically with key-value pairs.
Scalability: DynamoDB is designed for massive scalability and can handle millions of requests per second. It is built on a distributed architecture, allowing it to automatically scale up or down based on demand. Firebase Realtime Database also scales well and can handle high traffic loads, but it might be less suited for extremely large and complex applications compared to DynamoDB.
Querying and Indexing: DynamoDB provides flexible querying options with its primary key and secondary indexes. It supports both key-value queries and conditional queries. On the other hand, Firebase Realtime Database offers limited querying capabilities and does not support secondary indexes. It operates on a simple key-based lookup model, where querying is mainly based on the JSON tree structure.
Real-time Updates: Firebase Realtime Database excels in real-time data synchronization. It provides automatic synchronization across all connected devices in milliseconds, making it well-suited for real-time collaborative applications like chat or multiplayer games. While DynamoDB also supports stream-based updates and change notifications, it does not have the same level of real-time synchronization capabilities as Firebase Realtime Database.
Data Offline Support: Firebase Realtime Database has built-in offline support, allowing applications to continue functioning even when the device is offline. It caches data locally and syncs it with the cloud when the device reconnects. DynamoDB, on the other hand, requires developers to build their own offline support using other AWS services or third-party libraries.
Pricing Model: DynamoDB follows a pay-per-usage pricing model, where you pay for the provisioned throughput and storage consumed. It offers options to provision read/write capacity based on your anticipated demand. Firebase Realtime Database follows a more simplified pricing model based on data transferred and storage used. It provides a generous free tier, making it suitable for startups and small applications.

Amazon DynamoDB vs Firebase Realtime Database

Overview