Sql is much more feature rich yet costly and harder to maintain. Requires physical servers while dynamo everything is in the cloud across multiple AZs. Redis is actually great to put on top of dynamo to use as a read cache which is much faster and cheaper, but the storage and …
We have been preferring DynamoDB over Redis for persistent data. It has a better encryption model and is operationally simpler.
For materialized views we've been using Elasticsearch, but are starting to consider using DynamoDB there too.
MongoDB is mostly document store while Amazon DynamoDB supports both key/value and document store making it more versatile. Azure Cosmos DB is multi-modal like Amazon DynamoDB and it makes more sense when you have data already in Azure Cloud. If you are mostly using AWS then …
DynamoDB is a natural fit for anyone using the AWS environment for their code. If we were using Google or not tied to anything then Firebase might have been a better choice as it supports pub / sub among other things. It doesn't really act as a cache like redis does, but it can …
MongoDB vs. Amazon DynamoDB:• MongoDB requires more human management than DynamoDB, which is a fully managed service.• DynamoDB's scalability is automatic, whereas MongoDB's horizontal scaling may require more work.• When compared to DynamoDB, MongoDB offers more extensive data …
We started using DynamoDB because of the AWS ecosystem; it integrates well with everything. The IAM for role management as well. But using MongoDB with other AWS products was not seamless; we had to create custom APIs to make it work. But if the need for your organization is …
I've used SQL and NoSQL solutions, such as MongoDB and MySQL. I would not choose Dynamo to be a primary datastore and one of the others is likely a better option. Dynamo is good as almost viewed as a large cache. If you want something that is more supported and easier to work …
As a fully managed NoSQL service, DynamoDB provides a lot of functionality for relatively low cost. Scaling, sharding, throughput performance is managed for you, and you only pay for the bandwidth you provision.
9/10 times I would recommend using MongoDB over DynamoDB. The only real benefit of DynamoDB over MongoDB is that it's already deeply nested in the Amazon ecosystem with tight integration with other AWS tools. Working with Amazons sdks is clunky compared to Mongo, it lacks a …
DynamoDB is a fully managed key-value store by Amazon. It provides more powerful indexing to the tables, which certainly increases the performance if searching is what you need. However, it is also a lot more expensive to use compared to Redis. If your use case is more on the …
We evaluated Oracle and at first it seems competitive but after the contract term pricing would jump. Heard this from business associates and online communities
Couchbase doesn't keep up with what they offer and what really does. MongoDB just doesn't scale out, reads are performed across multiple nodes but writes still go to the single master. DynamoDB is good overall but just way too expensive.
Redis is had higher performance at a cheaper cost than any of these alternatives. The downside is the data is not as durable as these alternatives. Redis is like SQL where you pay of the instance running 24/7 where dynamo and s3 you pay per usage. Redis schema most closely …
It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad
Redis has been a great investment for our organization as we needed a solution for high speed data caching. The ramp up and integration was quite easy. Redis handles automatic failover internally, so no crashes provides high availability. On the fly scaling scale to more/less cores and memory as and when needed.
Easy for developers to understand. Unlike Riak, which I've used in the past, it's fast without having to worry about eventual consistency.
Reliable. With a proper multi-node configuration, it can handle failover instantly.
Configurable. We primarily still use Memcache for caching but one of the teams uses Redis for both long-term storage and temporary expiry keys without taking on another external dependency.
Fast. We process tens of thousands of RPS and it doesn't skip a beat.
We had some difficulty scaling Redis without it becoming prohibitively expensive.
Redis has very simple search capabilities, which means its not suitable for all use cases.
Redis doesn't have good native support for storing data in object form and many libraries built over it return data as a string, meaning you need build your own serialization layer over it.
It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.
We will definitely continue using Redis because: 1. It is free and open source. 2. We already use it in so many applications, it will be hard for us to let go. 3. There isn't another competitive product that we know of that gives a better performance. 4. We never had any major issues with Redis, so no point turning our backs.
Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.
It is quite simple to set up for the purpose of managing user sessions in the backend. It can be easily integrated with other products or technologies, such as Spring in Java. If you need to actually display the data stored in Redis in your application this is a bit difficult to understand initially but is possible.
It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.
The support team has always been excellent in handling our mostly questions, rarely problems. They are responsive, find the solution and get us moving forward again. I have never had to escalate a case with them. They have always solved our problems in a very timely manner. I highly commend the support team.
The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.
We are big users of MySQL and PostgreSQL. We were looking at replacing our aging web page caching technology and found that we could do it in SQL, but there was a NoSQL movement happening at the time. We dabbled a bit in the NoSQL scene just to get an idea of what it was about and whether it was for us. We tried a bunch, but I can only seem to remember Mongo and Couch. Mongo had big issues early on that drove us to Redis and we couldn't quite figure out how to deploy couch.
I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.
Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.
It has allowed us to add more scalability to some of our systems.
As with any new technology there was a ramp up/rework phase as we learned best practices.
Redis has helped us increase our throughput and server data to a growing amount of traffic while keeping our app fast. We couldn't have grown without the ability to easily cache data that Redis provides.
Redis has helped us decrease the load on our database. By being able to scale up and cache important data, we reduce the load on our database reducing costs and infra issues.
Running a Redis node on something like AWS can be costly, but it is often a requirement for scaling a company. If you need data quickly and your business is already a positive ROI, Redis is worth the investment.