Alternatives to Redis Cloud logo

Alternatives to Redis Cloud

Heroku Redis, Google Cloud Datastore, MongoDB, Google Cloud Memorystore, and Redis To Go are the most popular alternatives and competitors to Redis Cloud.
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What is Redis Cloud and what are its top alternatives?

Redis Cloud is a fully-managed service for running your Redis dataset. It overcomes Redis’ scalability limitation by supporting all Redis commands at any dataset size. Your dataset is constantly replicated, so if a node fails, an auto-switchover mechanism guarantees data is served without interruption.
Redis Cloud is a tool in the Redis Hosting category of a tech stack.

Top Alternatives to Redis Cloud

  • Heroku Redis
    Heroku Redis

    Heroku Redis is an in-memory key-value data store, run by Heroku, that is provisioned and managed as an add-on. Heroku Redis is accessible from any language with a Redis driver, including all languages and frameworks supported by Heroku. ...

  • Google Cloud Datastore
    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. ...

  • 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. ...

  • Google Cloud Memorystore
    Google Cloud Memorystore

    Cloud Memorystore for Redis provides a fully managed in-memory data store service built on scalable, more secure, and highly available infrastructure managed by Google. Use Cloud Memorystore to build application caches that provides sub-millisecond data access. Cloud Memorystore is compatible with the Redis protocol, allowing easy migration with zero code changes. ...

  • Redis To Go
    Redis To Go

    Redis To Go was created to make the managing Redis instances easier, whether it is just one instance or serveral. Deploying a new instance of Redis is dead simple, whether for production or development. ...

  • RedisGreen
    RedisGreen

    Redis drives the best sites on the web, from Twitter to Pinterest. RedisGreen makes it easy for anyone to use. Customers can spin up databases at the click of a button. RedisGreen's future is in very fast tools to make the most difficult aspects of modern web application development faster, cheaper, and less labor-intensive.<br> ...

Redis Cloud alternatives & related posts

Heroku Redis logo

Heroku Redis

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Reliable and powerful Redis as a service
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PROS OF HEROKU REDIS
  • 3
    More reliable than the other Redis add-ons
CONS OF HEROKU REDIS
  • 1
    More expensive than the other options

related Heroku Redis posts

Google Cloud Datastore logo

Google Cloud Datastore

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A Fully Managed NoSQL Data Storage Service
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PROS OF GOOGLE CLOUD DATASTORE
  • 7
    High scalability
  • 2
    Serverless
  • 2
    Ability to query any property
  • 1
    Pay for what you use
CONS OF GOOGLE CLOUD DATASTORE
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    related Google Cloud Datastore posts

    MongoDB logo

    MongoDB

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    The database for giant ideas
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    PROS OF MONGODB
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      Document-oriented storage
    • 593
      No sql
    • 549
      Ease of use
    • 465
      Fast
    • 408
      High performance
    • 256
      Free
    • 215
      Open source
    • 180
      Flexible
    • 143
      Replication & high availability
    • 110
      Easy to maintain
    • 42
      Querying
    • 38
      Easy scalability
    • 37
      Auto-sharding
    • 36
      High availability
    • 31
      Map/reduce
    • 27
      Document database
    • 25
      Full index support
    • 25
      Easy setup
    • 16
      Reliable
    • 15
      Fast in-place updates
    • 14
      Agile programming, flexible, fast
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      No database migrations
    • 8
      Easy integration with Node.Js
    • 8
      Enterprise
    • 6
      Enterprise Support
    • 5
      Great NoSQL DB
    • 3
      Drivers support is good
    • 3
      Aggregation Framework
    • 3
      Support for many languages through different drivers
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      Awesome
    • 2
      Schemaless
    • 2
      Managed service
    • 2
      Fast
    • 2
      Easy to Scale
    • 1
      Consistent
    • 1
      Acid Compliant
    CONS OF MONGODB
    • 6
      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
    Google Cloud Memorystore logo

    Google Cloud Memorystore

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    Fully managed Redis instances by Google
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    PROS OF GOOGLE CLOUD MEMORYSTORE
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      CONS OF GOOGLE CLOUD MEMORYSTORE
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        related Google Cloud Memorystore posts

        Redis To Go logo

        Redis To Go

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        Simple Redis hosting
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        PROS OF REDIS TO GO
        • 5
          Heroku Add-on
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          Pub-Sub
        • 3
          Always up
        • 3
          Easy setup
        • 3
          Affordable
        • 1
          Perfect full sync
        CONS OF REDIS TO GO
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          RedisGreen logo

          RedisGreen

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          Dedicated NoSQL Databases for the Realtime Web
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          PROS OF REDISGREEN
          • 1
            Heroku Add-on
          CONS OF REDISGREEN
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            related RedisGreen posts