Alternatives to Firebase Realtime Database logo

Alternatives to Firebase Realtime Database

Parse, AWS AppSync, Firebase Cloud Messaging, MySQL, and Redis are the most popular alternatives and competitors to Firebase Realtime Database.
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What is Firebase Realtime Database and what are its top alternatives?

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.
Firebase Realtime Database is a tool in the NoSQL Database as a Service category of a tech stack.

Top Alternatives to Firebase Realtime Database

  • Parse

    Parse

    With Parse, you can add a scalable and powerful backend in minutes and launch a full-featured app in record time without ever worrying about server management. We offer push notifications, social integration, data storage, and the ability to add rich custom logic to your app’s backend with Cloud Code. ...

  • AWS AppSync

    AWS AppSync

    AWS AppSync automatically updates the data in web and mobile applications in real time, and updates data for offline users as soon as they reconnect. AppSync makes it easy to build collaborative mobile and web applications that deliver responsive, collaborative user experiences. ...

  • Firebase Cloud Messaging

    Firebase Cloud Messaging

    It is a cross-platform messaging solution that lets you reliably deliver messages at no cost. You can notify a client app that new email or other data is available to sync. You can send notification messages to drive user re-engagement and retention. For use cases such as instant messaging, a message can transfer a payload of up to 4KB to a client app. ...

  • MySQL

    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • MongoDB

    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • Amazon DynamoDB

    Amazon DynamoDB

    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. ...

  • Cloud Firestore

    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. ...

Firebase Realtime Database alternatives & related posts

Parse logo

Parse

412
437
582
The complete mobile app platform
412
437
+ 1
582
PROS OF PARSE
  • 116
    Easy setup
  • 76
    Free hosting
  • 61
    Well-documented
  • 49
    Cheap
  • 46
    Use push notifications in 3 lines of code
  • 40
    Fast
  • 39
    Cloud code
  • 31
    Good for prototypes
  • 30
    Cloud modules
  • 27
    Backed by facebook
  • 7
    Parse Push
  • 7
    Cross Platform
  • 6
    Parse Core
  • 6
    Parse Analytics
  • 5
    Multiplatform
  • 5
    Quick chat and profile capabilities
  • 5
    Free Tier
  • 4
    Cloud Based
  • 3
    Backend as a service
  • 3
    Backbone Models
  • 3
    Nice security concept
  • 3
    Free
  • 3
    Geopoints
  • 2
    Local Datastore
  • 2
    Anonymous Users
  • 2
    Easy to use
  • 1
    About to Die
CONS OF PARSE
    Be the first to leave a con

    related Parse posts

    AWS AppSync logo

    AWS AppSync

    155
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    26
    A managed GraphQL service with real-time data and offline programming
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    218
    + 1
    26
    PROS OF AWS APPSYNC
    • 7
      GraphQL
    • 4
      Real-Time
    • 3
      Offline
    • 3
      Apollo
    • 2
      Fully managed and scalable GraphQL Resolver!
    • 2
      Backed by Amazon
    • 2
      BaaS
    • 2
      AWS
    • 1
      Enterprise Security
    CONS OF AWS APPSYNC
      Be the first to leave a con

      related AWS AppSync posts

      Firebase Cloud Messaging logo

      Firebase Cloud Messaging

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      13
      A cross-platform messaging solution
      212
      279
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      13
      PROS OF FIREBASE CLOUD MESSAGING
      • 13
        Free
      CONS OF FIREBASE CLOUD MESSAGING
      • 6
        Lack of BI tools

      related Firebase Cloud Messaging posts

      MySQL logo

      MySQL

      85.4K
      69.4K
      3.7K
      The world's most popular open source database
      85.4K
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      PROS OF MYSQL
      • 793
        Sql
      • 672
        Free
      • 556
        Easy
      • 527
        Widely used
      • 485
        Open source
      • 180
        High availability
      • 160
        Cross-platform support
      • 104
        Great community
      • 78
        Secure
      • 75
        Full-text indexing and searching
      • 25
        Fast, open, available
      • 14
        SSL support
      • 13
        Reliable
      • 13
        Robust
      • 8
        Enterprise Version
      • 7
        Easy to set up on all platforms
      • 2
        NoSQL access to JSON data type
      • 1
        Relational database
      • 1
        Easy, light, scalable
      • 1
        Sequel Pro (best SQL GUI)
      • 1
        Replica Support
      CONS OF MYSQL
      • 14
        Owned by a company with their own agenda
      • 1
        Can't roll back schema changes

      related MySQL posts

      Tim Abbott

      We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

      We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

      And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

      I can't recommend it highly enough.

      See more
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 1.1M views

      Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

      The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

      https://eng.uber.com/mysql-migration/

      See more
      Redis logo

      Redis

      42.8K
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      3.9K
      An in-memory database that persists on disk
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      PROS OF REDIS
      • 875
        Performance
      • 535
        Super fast
      • 511
        Ease of use
      • 441
        In-memory cache
      • 321
        Advanced key-value cache
      • 190
        Open source
      • 179
        Easy to deploy
      • 163
        Stable
      • 153
        Free
      • 120
        Fast
      • 40
        High-Performance
      • 39
        High Availability
      • 34
        Data Structures
      • 32
        Very Scalable
      • 23
        Replication
      • 20
        Great community
      • 19
        Pub/Sub
      • 17
        "NoSQL" key-value data store
      • 14
        Hashes
      • 12
        Sets
      • 10
        Sorted Sets
      • 9
        Lists
      • 8
        BSD licensed
      • 8
        NoSQL
      • 7
        Async replication
      • 7
        Integrates super easy with Sidekiq for Rails background
      • 7
        Bitmaps
      • 6
        Open Source
      • 6
        Keys with a limited time-to-live
      • 5
        Strings
      • 5
        Lua scripting
      • 4
        Awesomeness for Free!
      • 4
        Hyperloglogs
      • 3
        outstanding performance
      • 3
        Runs server side LUA
      • 3
        Networked
      • 3
        LRU eviction of keys
      • 3
        Written in ANSI C
      • 3
        Feature Rich
      • 3
        Transactions
      • 2
        Data structure server
      • 2
        Performance & ease of use
      • 1
        Existing Laravel Integration
      • 1
        Automatic failover
      • 1
        Easy to use
      • 1
        Object [key/value] size each 500 MB
      • 1
        Simple
      • 1
        Channels concept
      • 1
        Scalable
      • 1
        Temporarily kept on disk
      • 1
        Dont save data if no subscribers are found
      • 0
        Jk
      CONS OF REDIS
      • 14
        Cannot query objects directly
      • 2
        No secondary indexes for non-numeric data types
      • 1
        No WAL

      related Redis posts

      Robert Zuber

      We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

      As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

      When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

      See more

      I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

      We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

      Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

      We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

      Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

      See more
      MongoDB logo

      MongoDB

      64.6K
      54.1K
      4.1K
      The database for giant ideas
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      4.1K
      PROS OF MONGODB
      • 824
        Document-oriented storage
      • 591
        No sql
      • 546
        Ease of use
      • 465
        Fast
      • 406
        High performance
      • 256
        Free
      • 215
        Open source
      • 179
        Flexible
      • 142
        Replication & high availability
      • 109
        Easy to maintain
      • 41
        Querying
      • 37
        Easy scalability
      • 36
        Auto-sharding
      • 35
        High availability
      • 31
        Map/reduce
      • 26
        Document database
      • 24
        Easy setup
      • 24
        Full index support
      • 15
        Reliable
      • 14
        Fast in-place updates
      • 13
        Agile programming, flexible, fast
      • 11
        No database migrations
      • 7
        Easy integration with Node.Js
      • 7
        Enterprise
      • 5
        Enterprise Support
      • 4
        Great NoSQL DB
      • 3
        Aggregation Framework
      • 3
        Support for many languages through different drivers
      • 3
        Drivers support is good
      • 2
        Schemaless
      • 2
        Easy to Scale
      • 2
        Fast
      • 2
        Awesome
      • 2
        Managed service
      • 1
        Consistent
      CONS OF MONGODB
      • 5
        Very slowly for connected models that require joins
      • 3
        Not acid compliant
      • 1
        Proprietary query language

      related MongoDB posts

      Jeyabalaji Subramanian

      Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

      We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

      Based on the above criteria, we selected the following tools to perform the end to end data replication:

      We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

      We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

      In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

      Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

      In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

      See more
      Robert Zuber

      We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

      As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

      When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

      See more
      Amazon DynamoDB logo

      Amazon DynamoDB

      3.1K
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      195
      Fully managed NoSQL database service
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      PROS OF AMAZON DYNAMODB
      • 62
        Predictable performance and cost
      • 56
        Scalable
      • 35
        Native JSON Support
      • 21
        AWS Free Tier
      • 7
        Fast
      • 3
        No sql
      • 3
        To store data
      • 2
        Serverless
      • 2
        No Stored procedures is GOOD
      • 1
        ORM with DynamoDBMapper
      • 1
        Elastic Scalability using on-demand mode
      • 1
        Elastic Scalability using autoscaling
      • 1
        DynamoDB Stream
      CONS OF AMAZON DYNAMODB
      • 4
        Only sequential access for paginate data
      • 1
        Scaling
      • 1
        Document Limit Size

      related Amazon DynamoDB posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Dmitry Mukhin

      Uploadcare has built an infinitely scalable infrastructure by leveraging AWS. Building on top of AWS allows us to process 350M daily requests for file uploads, manipulations, and deliveries. When we started in 2011 the only cloud alternative to AWS was Google App Engine which was a no-go for a rather complex solution we wanted to build. We also didn’t want to buy any hardware or use co-locations.

      Our stack handles receiving files, communicating with external file sources, managing file storage, managing user and file data, processing files, file caching and delivery, and managing user interface dashboards.

      At its core, Uploadcare runs on Python. The Europython 2011 conference in Florence really inspired us, coupled with the fact that it was general enough to solve all of our challenges informed this decision. Additionally we had prior experience working in Python.

      We chose to build the main application with Django because of its feature completeness and large footprint within the Python ecosystem.

      All the communications within our ecosystem occur via several HTTP APIs, Redis, Amazon S3, and Amazon DynamoDB. We decided on this architecture so that our our system could be scalable in terms of storage and database throughput. This way we only need Django running on top of our database cluster. We use PostgreSQL as our database because it is considered an industry standard when it comes to clustering and scaling.

      See more
      Cloud Firestore logo

      Cloud Firestore

      559
      733
      106
      NoSQL database built for global apps
      559
      733
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      106
      PROS OF CLOUD FIRESTORE
      • 14
        Cloud Storage
      • 14
        Easy to use
      • 11
        Easy setup
      • 11
        Realtime Database
      • 9
        Super fast
      • 7
        Authentication
      • 6
        Realtime listeners
      • 5
        Hosting
      • 5
        Google Analytics integration
      • 5
        Could Messaging
      • 4
        Performance Monitoring
      • 4
        Crash Reporting
      • 3
        Test Lab for Android
      • 3
        Sharing App via invites
      • 3
        Adwords, Admob integration
      • 2
        Dynamic Links (Deeplinking support)
      • 0
        Robust ALI
      CONS OF CLOUD FIRESTORE
      • 6
        Doesn't support FullTextSearch natively

      related Cloud Firestore posts

      Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.

      Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.

      Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.

      Git + GitHub + Visual Studio Code is a killer stack.

      See more

      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?

      See more