What is Redis Cloud and what are its top alternatives?
Top Alternatives to Redis Cloud
- 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
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 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
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 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. ...
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
- More reliable than the other Redis add-ons3
- More expensive than the other options1
related Heroku Redis posts
- High scalability7
- Ability to query any property2
- Pay for what you use1
related Google Cloud Datastore posts
- Document-oriented storage828
- No sql593
- Ease of use549
- High performance408
- Open source215
- Replication & high availability143
- Easy to maintain110
- Easy scalability38
- High availability36
- Document database27
- Full index support25
- Easy setup25
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise Support6
- Great NoSQL DB5
- Drivers support is good3
- Aggregation Framework3
- Support for many languages through different drivers3
- Managed service2
- Easy to Scale2
- Acid Compliant1
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language1
related MongoDB posts
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!
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.
related Google Cloud Memorystore posts
- Heroku Add-on5
- Always up3
- Easy setup3
- Perfect full sync1