Amazon DynamoDB Reviews

We are a data classification company. We classify non-structured data, like documents in the organization. When we classify a document, we store the document's name and severity in Amazon DynamoDB. If the document has a sensitive classification, we add a tag, which will connect with the company's CM so they can send that document out of the company. We store all the document data on Amazon DynamoDB.

The transactions are very fast, and the cost is low with Amazon DynamoDB. We also have a web platform where we show our clients all their data. When we use DynamoDB with OpenSearch for other searches, we see that the platform is faster.

The process of making a query could be optimized. Sometimes, it can be difficult if you don't design it properly at the beginning of the project. It's more expensive to look for a field in your database than when you look for it in a relational database.

The solution should find another way to handle the queries. Counting all your data or the number of fields you have in a relational database is easier than in a non-relational database.

I have been using Amazon DynamoDB for two years.

The solution's scalability is very good. We don't have problems using Amazon DynamoDB with small clients, and it also scales perfectly for bigger clients.

The solution’s technical support is very good.

Positive

We previously used MongoDB. We switched to Amazon DynamoDB because its cost is lower.

It is not complex to deploy Amazon DynamoDB. The complex part is making a proper design and understanding how your application is going to grow because you have to map all these scenarios at the beginning. It could be hard to make changes in the initial design.

I didn't take part in the solution's setup. We have a DevOps team that does the configuration. In the setup, we try to design properly so that we can relate the field that can optimize the queries in the future.

In the past, we paid more for MongoDB, but now we optimize our searches and how we store the data on DynamoDB. We previously paid around $20,000 a month for MongoDB, and now we're paying just $4,000 monthly for Amazon DynamoDB.

It's easy to maintain the solution. We had some issues with the backups because DynamoDB has internal backups or restore points. We're trying to have backups with AWS, which is more expensive than having the backups in DynamoDB.

We're also trying to make a configuration with Lambda functions to save the backups into S3, but that is more expensive than an AWS backup. I don't know if there is another way that we can have that backup. When we have to put in progress a disaster recovery plan, we can have those backups.

Overall, I rate the solution an eight out of ten.

DynamoDB is a NoSQL, cloud-based AWS service. It is a database service. It supports structured and unstructured data, providing flexibility similar to SQL and NoSQL databases.

While AWS offers relational database services like RDS for structured data, DynamoDB handles unstructured data efficiently. It's designed for scalability and ease of use, allowing users to store and retrieve data quickly without predefined schemas or table creations. This flexibility extends to integrating software frameworks like LAMP, enabling seamless data sharing across applications.

Amazon DynamoDB is known for its cost efficiency and speed compared to other databases. It offers quick performance and rapid data retrieval, often providing limited data initially but scaling up to fulfill larger demands seamlessly. In terms of speed, it outperforms many other databases. There is no need to install anything. There is no need to manage those AWS databases. Everything is taken care of by this cloud alone, right? Everything is taken care of by the Amazon cloud. We must create a table, store data, configure the storage, and write the code. However, these tasks are managed by AWS. There is no downtime up to 99.9% uptime. Even if there is downtime in one region, it's considered in another, so data synchronization happens later, ensuring no loss for the customer.

We are currently consuming this data and storing it using coding. We are storing data in the database and retrieving those details. Everything is based on the EAP, but we are creating. There is no need to go to the particular TIN, DB, or application DB or search for the ID.

Amazon DynamoDB does not currently offer any import functionality. However, users have requested this feature. For instance, if you have a large dataset stored in Excel and you wish to migrate it to DynamoDB, there is currently no direct import option available through the portal. Nevertheless, this limitation can be overcome using third-party tools such as AMP, which facilitates data importation into DynamoDB. Having an import option, whether through browsing or local file uploads, would significantly improve the efficiency of data migration, enabling users to swiftly transfer large volumes of data into DynamoDB.

I have been using Amazon DynamoDB for 5 years.

The product is stable. Most customers are using it because of its stability. They're getting everything, including automatic scanning. Suppose a large number of customers are using it and data are increasing. In that case, it automatically scales, meaning it can handle those data at scale and provide all the data without delay. There is no latency.

We are satisfied with the customer support for Amazon DynamoDB because we haven't encountered any issues with the data handling. Everything is documented, so if a problem arises, we refer to the documentation. We haven't experienced any limitations or drawbacks with DynamoDB.

The initial setup is easy because they provide everything, including CSV imports, pipeline, and Terraform cloud permissions. You can set it up once, and it will automatically generate everything as if you have a proper DevOps team. With a proper DevOps team, creating these setups becomes easy. You can create tables and store data, which will be handled using Lambda and other tools. However, the setup can be managed entirely by having a DevOps team.

The product has a pay-as-you-go pricing model.

If you're using DynamoDB, you also need to understand Lambda coding. In programming, we handle everything, including creating a DynamoDB table, which only takes a few minutes. Once the data management panel is created, we primarily use Lambda for operations. Lambda requires us to write the code for tasks like storing and managing data. DynamoDB is generally straightforward to work with. For example, I navigate to DynamoDB to create a table and make it. However, you'll need to understand various programming concepts and optimize your code for more complex tasks like working with large datasets.

To create the table and input data manually, we need to be proactive. However, updating and handling such tasks programmatically can be challenging. It becomes necessary when developers need to manipulate data through coding, particularly Node.js, Python, or Java. Managing the structure comprehensively while using DynamoDB can pose difficulties.

You can proceed with that option if you want to store data manually. However, from an application perspective, you would need to hire a developer to perform the necessary actions to expose the data through a gateway for other users. It's essential to consider CAPA for applications and presentations. The relevant teams need to develop these CAPA aspects. DynamoDB comes into play to manage the data. We can seamlessly switch data between the backend and the APM. The frontend OS fetches this data from the APM to display in the UI, allowing users to access all the necessary information from the database.

All the data will be synchronized if you store data in one region and access a global table in another. Any data in one table will also be present in the other.

Security is paramount with Amazon DynamoDB. Everything is handled, so we're not exposing anything. It offers controlled access, ensuring users have their data. If you have the necessary access rights, you can check the data. Otherwise, secret access is not granted. The cloud takes care of everything seamlessly.

Overall, I rate the solution a nine out of ten.

We use the solution to handle structured data. So, whenever you need to make a runtime call and send any data, you can create it. To store the data exclusively, we need to use granularity. You must integrate with Lambda to process and store the records, whether they're coming from Connect or elsewhere.

The most unbelievable feature is NoSQL. Unlike traditional SQL databases, DynamoDB doesn't require predefined schemas. You don't have to design the schema, which can be limiting in SQL databases. Additionally, in SQL databases, modifying the schema to add new columns can be confusing and must be done before adding new data. DynamoDB allows for flexibility in adding new attributes to your data at runtime without altering the schema. This makes data processing more efficient and customizable. The performance of DynamoDB is highly configurable, depending on the provisioned Read Capacity Units and Write Capacity Units that you allocate to your tables.

The solution could be cheaper.

I have been using Amazon DynamoDB for 6 years.

The solution doesn't break down.

The availability is very good. It is also scalable.

The solution is expensive.

DynamoDB is one of the services that 90% of people use on AWS.

Let's say we are developing an application using AWS. For the backend data storage, DynamoDB is the best solution AWS offers for NoSQL databases. If SQL is needed, then RDS is the way to go.

You must understand the basic CRUD operations of databases, along with the APIs. Knowing how to create a schema, determining primary and foreign keys is essential. The AWS documentation provides detailed guidance on these.

DynamoDB supports multiple areas and has good monitoring and security features. AWS CloudWatch can be used for monitoring, and third-party tools like Datadog or additional integration are available for functionality.

Overall, I rate the solution a nine out of ten.

Amazon DynamoDB is used to store data in the form of JSON. I use AWS Lambda to insert data into Amazon DynamoDB.

In a relational database like MySQL, we convert values, like name, age, or other details about a person, into tables. We directly pass the JSON value to Amazon DynamoDB, which is why Amazon DynamoDB is faster than relational databases.

The solution's efficiency and performance should be faster than other databases.

I have been using Amazon DynamoDB for six months.

I rate the solution a nine out of ten for stability.

Amazon DynamoDB is a scalable solution that can store more data than other databases. AWS manages Amazon DynamoDB, such as increasing the storage, RAM, or CPU of its actual server. We need not worry about the solution's scalability. The solution's availability is 24/7.

The solution’s technical support is good.

I previously used Amazon RDS.

The solution’s initial setup is easy.

We can deploy Amazon DynamoDB in one to two hours.

The solution was free to use one month ago, but now it is a paid service. Amazon DynamoDB is not an expensive solution.

We can use Amazon DynamoDB for both on-premises and in the cloud.

Overall, I rate the solution a seven out of ten.

We are using Amazon DynamoDB to store structured data. We recently created an application that stores streaming data from different events in DynamoDB for further processing and analytics.

Using DynamoDB allows us to handle large amounts of data without worrying about scalability, failover, and other concerns. It has been beneficial for us compared to MongoDB, where we had to handle everything ourselves.

The most valuable feature of DynamoDB is that it handles a large amount of data efficiently. Being serverless, it alleviates concerns about scalability and failover. The integration with other AWS services is also very easy.

I don't have in-depth knowledge to suggest specific improvements for DynamoDB, however, I haven't faced any significant challenges so far.

We have been using it recently for one of our serverless applications.

I haven't encountered issues with the stability of DynamoDB. Everything was smooth for us.

No worries about scalability since DynamoDB as a serverless service handles scaling well.

If you purchase the support plan, their support team is good. If not, it takes two to three days to respond, sometimes even more.

Neutral

We used MongoDB previously. We switched to DynamoDB since it reduced the need for us to handle everything.

I would definitely recommend DynamoDB to anyone looking to use NoSQL databases.

I'd rate the solution nine out of ten.

Personally, I use DynamoDB for creating my Proof of Concepts (POCs) for testing DynamoDB functionalities. It's not used extensively by me but is likely used by the database team.

DynamoDB is easy to configure, easy to use, and allows for writing data in JSON format.

I have not used it extensively at a larger scale, so I cannot provide feedback on improvements.

I have been using DynamoDB for about four years.

I haven't used DynamoDB in large volume, so I haven't faced any stability issues during my usage.

I haven't used DynamoDB for large scale data handling. I've used other solutions like MongoDB and S3 for such purposes.

Support is better if using a paid version. I gave the support a rating of seven out of ten.

Neutral

I have used other resources like MongoDB and S3 for handling large-scale data.

I would recommend DynamoDB to other people.

I'd rate the solution eight out of ten.

The solution is being used for scaling. For instance, if your throughput is X, then automatically, we can handle two X without incurring any additional cost, which gives you leverage over the spiking nature of the customer.

The latency feature is precious.

The setup cost could be reduced. But overall, the tool works smoothly.

I have been using Amazon DynamoDB for the last two years. 

Amazon DynamoDB is a managed database by AWS, so it handles scalability. I've seen improvements in scalability. For customer data, they replicate it three times independently of use, ensuring they can handle peak throughput at twice the usual rate. From a cost perspective, I'm charged based on the amount of data scanned and returned.

It provides premium support for enterprise solutions and real-time assistance.

Positive

Integration with Amazon DynamoDB is straightforward due to clear documentation and comprehensive support. 

Investing in Amazon DynamoDB has provided us with reliability and scalability, which are crucial for our operations. This has been a significant return on our investment.

It is costly. To reduce the costs, users need to read the node in front of it. For read-heavy groups, cache optimization can help manage costs. I can't disclose specific pricing, but it's competitive compared to others in the market, and this information is easily accessible online.

Amazon DynamoDB stands out primarily in its managed database feature compared to other tools. While other tools like MongoDB offer similar support, DynamoDB excels in its GSI and LSI capabilities, especially in partitioning and data handling. Its reliability and security features are notable, meeting our company's data security requirements.

I would rate Amazon DynamoDB an eight out of ten, primarily for its reliability, scalability, and security features. In terms of support, I would rate it above nine, especially for enterprise solutions with premium backing that ensures real-time assistance.

DynamoDB doesn't directly support AI-driven applications, but it can certainly be used as a backend database for such applications.

I would rate Amazon DynamoDB an eight out of ten, primarily for its reliability, scalability, and security features.

One of our customers has been using DynamoDB for four years. They are using it to store long strings of data, particularly experiences shared by people in nature domains, such as recreation zones. 

These experiences could be related to hiking, swimming, observing nature, or anything else. People can send in short messages, and those messages are stored in DynamoDB.

DynamoDB is a service by Amazon. It is what it is. 

DynamoDB is a key-value database, and it's valuable if you have simple scan queries and don't need to do point searches. For example, if you want to see what happened on Wednesday afternoon at two o'clock and correlate that with what happened on Thursday morning at eleven o'clock, DynamoDB can handle those queries. 

However, if you need to do more complex queries that involve multiple indexes or multiple keys, you're better off using an operational database. DynamoDB can still be a good choice for single-T value streams and smaller datasets, but it does have a limitation of four KB for a record, which can be restrictive.

Essentially, for those with some experience in the field, we could call it an index sequential access method. 

Maybe the documentation could be improved a bit. Sometimes, it's a little confusing, and people can easily be mistaken about DynamoDB. 

That's the main thing, the documentation could be a little more explicit or a little more elaborate. But that's it. It's a key-value system, and it works well for what it is. The only thing I can see that could be improved is the documentation.

We have been using it since 2019, so for four years now.

The service itself is stable. We haven't found any faults in DynamoDB. It is what it is. I would have developed it or designed it with some more resilience in terms of flexibility and data, but the thing is stable. I've never had a crash.

It is scalable up to its limits. DynamoDB can be used by all types of businesses, from small startups to large enterprises.

We've always gotten help when we've called, so in my case, customer service and support are good.

We don't have any complaints about that or any information to provide on that. We always get a response within the contractual parameters, so everything has been okay for us.

Positive

We have a few DynamoDBs, and we are also planning to restore unstructured data next week.

In 1982, which is now 40 years ago, we were working with an IBM system called ISAM and VTAM. Those very old mainframe systems were exactly what DynamoDB is today.

DynamoDB is a key-value storage device for a system. You have a key, which is one, two, three, and you have values, such as apples, orange juice, pears, whatever. 

You can have a data construction or design in which you have one value with one key, or you could have a data structure designed for your application with a specific type of data. And then, you have to set up your DynamoDB accordingly. So, again, it's like comparing what is better: white bread, whole wheat bread, or slightly less white bread. It's all still bread.

Any service in AWS is relatively easy to deploy. The deployment time for any variable GB is about a second.

One person can do the deployment. The service itself doesn't require maintenance. It's the content that needs attention. It's like any data store or any form. If you put garbage or corrupt data in it, you will get a corrupted result. So, the service itself is flawless. It works exactly as advertised. 

But if you store the wrong data in it, with the wrong key and the wrong values, you will get the wrong results. So, the maintenance is not so much in DynamoDB itself. The maintenance is in your data stream.

We have a cloud engineer who takes care of running, upgrading, and supporting about 60 to 70 DynamoDBs in the physical sense, although that might be a bad term to use with cloud services. And he does it all by himself. So, one person can, in the current state of affairs with cloud services, oversee quite a large number of services.

Compared to a high-end relational database, it's cheap. But then it doesn't have the capabilities of a high-end relational database because it's not meant to be a high-end relational database.

So, it's like looking at what the price difference is between a scooter and a Bentley or a GTR. They are means of transport that are in a different league altogether. So, you don't compare a Bentley with a scooter.

The pricing is based on usage. DynamoDB is one of the services within Amazon Web Services. So, the pricing for anything in cloud services is based on usage and volume. So, the more you use it and the more volume you pump into it, the more the price will go up. But that is the normal business case for any storage service in the cloud.

If you've done your data architecture and analyzed what you'll be using your data for, where you'll be using it, and you have your data flows and conceptual model, and you see that it's a sequential storage of keys with values attached to it, DynamoDB is a valuable and valid option. 

However, don't use it just because it's easy. You should use it when you don't need some of the other aspects of a relational database, like joining, multiple endpoints, and comparing or having a key on multiple datasets. If that's your use case, if you want it for your entire application, don't use DynamoDB. 

But if it's for something simple, like a record of sales or events happening on a particular day or moment, please do use DynamoDB. It all comes down to the quality of your data architect.

I would give it a ten in some cases and a zero in others. For example, if you want to have a research database where you need multiple perspectives on the same set of data, and you try to do that within DynamoDB, you're going to have trouble.

But if you have a log and you want to do some statistical research on, for example, the sales in a supermarket, which are a simple timeline with the cash register data, timestamp, value, and then the goods, that's all very simple, key-value, and you can use DynamoDB for that.

So, it depends on the use case. For the use cases that you're using it for, you would give it a ten. So, the solution is excellent for the purpose you're using it for.

For my use cases, I would rate it an eight out of ten. We chose DynamoDB. We could have done the same thing with a relational database, but then again, you wouldn't choose a Bentley Continental GTR just to go to the grocery store. You can go to the grocery store on a scooter. 

We decided against the relational database because of the overhead and cost, and we went with DynamoDB instead. Because the dataset is just a key timestamp and some values, a key and a value, we can restore them sequentially, which is exactly what DynamoDB can do without any problems.