Amazon DynamoDB is a cloud-native, NoSQL, serverless database service.
$0
capacity unit per hour
AWS Elastic Beanstalk
Score 8.0 out of 10
N/A
AWS Elastic Beanstalk is the platform-as-a-service offering provided by Amazon and designed to leverage AWS services such as Amazon Elastic Cloud Compute (Amazon EC2), Amazon Simple Storage Service (Amazon S3).
$35
per month
Redis Software
Score 9.1 out of 10
N/A
Redis is an open source in-memory data structure server and NoSQL database.
N/A
Pricing
Amazon DynamoDB
AWS Elastic Beanstalk
Redis Software
Editions & Modules
Provisioned - Read Operation
$0.00013
capacity unit per hour
Provisioned - Write Operation
$0.00065
capacity unit per hour
Provisioned - Global Tables
$0.000975
per Read Capacity
On-Demand Streams
$0.02
per 100,000 read operations
Provisioned - Streams
$0.02
per 100,000 read operations
On-Demand Data Requests Outside AWS Regions
$0.09
per GB
Provisioned - Data Requests Outside AWS Regions
$0.09
per GB
On-Demand Snapshot
$0.10
per GB per month
Provisioned - Snapshot
$0.10
per GB per month
On-Demand Restoring a Backup
$0.15
per GB
Provisioned - Restoring a Backup
$0.15
per GB
On-Demand Point-in-Time Recovery
$0.20
per GB per month
Provisioned - Point-in-Time Recovery
$0.20
per GB per month
On-Demand Read Operation
$0.25
per million requests
On-Demand Data Stored
$0.25
per GB per month
Provisioned - Data Stored
$0.25
per GB per month
On-Demand - Write Operation
$1.25
per million requests
On-Demand Global Tables
$1.875
per million write operations replicated
No Charge
$0
Users pay for AWS resources (e.g. EC2, S3 buckets, etc.) used to store and run the application.
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 …
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 …
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.
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 …
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 …
AWS Elastic Beanstalk is a great option for an organization that's already invested in the AWS ecosystem. The greater the number of complementary features needed by the application (e.g. integrating with Amazon's Elastic Load Balancer, databases, etc), the greater the reward …
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.
The main competitor to Redis is Memcached. While I haven't used Memcached as much, I do know that it has been regarded as an inferior product to Redis. I have read that it does not have the ability to scale and work with the simplicity that Redis provides. However, I can not …
Redis is great at set operations and is very fast. Riak is a fast long-term data store, but it is expensive to run. MongoDB is good for small, quick projects. Elasticsearch is great at indexing and searching. Choose the right tool for the job, and don't be afraid to …
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
I have been using AWS Elastic Beanstalk for more than 5 years, and it has made our life so easy and hassle-free. Here are some scenarios where it excels -
I have been using different AWS services like EC2, S3, Cloudfront, Serverless, etc. And Elastic Beanstalk makes our lives easier by tieing each service together and making the deployment a smooth process.
N number of integrations with different CI/CD pipelines make this most engineer's favourite service.
Scalability & Security comes with the service, which makes it the absolute perfect product for your business.
Personally, I haven't found any situations where it's not appropriate for the use cases it can be used. The pricing is also very cost-effective.
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.
Getting a project set up using the console or CLI is easy compared to other [computing] platforms.
AWS Elastic Beanstalk supports a variety of programming languages so teams can experiment with different frameworks but still use the same compute platform for rapid prototyping.
Common application architectures can be referenced as patterns during project [setup].
Multiple environments can be deployed for an application giving more flexibility for experimentation.
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.
Limited to the frameworks and configurations that AWS supports. There is no native way to use Elastic Beanstalk to deploy a Go application behind Nginx, for example.
It's not always clear what's changed on an underlying system when AWS updates an EB stack; the new version is announced, but AWS does not say what specifically changed in the underlying configuration. This can have unintended consequences and result in additional work in order to figure out what changes were made.
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.
As our technology grows, it makes more sense to individually provision each server rather than have it done via beanstalk. There are several reasons to do so, which I cannot explain without further diving into the architecture itself, but I can tell you this. With automation, you also loose the flexibility to morph the system for your specific needs. So if you expect that in future you need more customization to your deployment process, then there is a good chance that you might try to do things individually rather than use an automation like beanstalk.
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.
The overall usability is good enough, as far as the scaling, interactive UI and logging system is concerned, could do a lot better when it comes to the efficiency, in case of complicated node logics and complicated node architectures. It can have better software compatibility and can try to support collaboration with more softwares
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.
As I described earlier it has been really cost effective and really easy for fellow developers who don't want to waste weeks and weeks into learning and manually deploying stuff which basically takes month to create and go live with the Minimal viable product (MVP). With AWS Beanstalk within a week a developer can go live with the Minimal viable product easily.
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.
- Do as many experiments as you can before you commit on using beanstalk or other AWS features. - Keep future state in mind. Think through what comes next, and if that is technically possible to do so. - Always factor in cost in terms of scaling. - We learned a valuable lesson when we wanted to go multi-region, because then we realized many things needs to change in code. So if you plan on using this a lot, factor multiple regions.
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 also use Heroku and it is a great platform for smaller projects and light Node.js services, but we have found that in terms of cost, the Elastic Beanstalk option is more affordable for the projects that we undertake. The fact that it sits inside of the greater AWS Cloud offering also compels us to use it, since integration is simpler. We have also evaluated Microsoft Azure and gave up trying to get an extremely basic implementation up and running after a few days of struggling with its mediocre user interface and constant issues with documentation being outdated. The authentication model is also badly broken and trying to manage resources is a pain. One cannot compare Azure with anything that Amazon has created in the cloud space since Azure really isn't a mature platform and we are always left wanting when we have to interface with it.
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.
