Amazon DynamoDB Reviews

I use the solution in my company as a fast storage system. Since there are some serverless functions, we used to write the outcome of those into the Amazon DynamoDB. My company then used to receive it later from the other apps. It mostly does fast retrieval and acts as a good storage system.

The most valuable feature of the solution is its performance.

There are actually several Amazon services related to Amazon Connect, a contact center solution. These include Amazon Connect, Lambda functions, Amazon DynamoDB, Amazon Kinesis, Amazon SNS, and Amazon SES.

We use DynamoDB to manage our contact center dynamically. This requires the use of a DynamoDB table and Lambda functions.

The ability to store multiple data attributes is crucial. For example, in a contact flow, if a customer calls, we can integrate DynamoDB dynamically. We need only the customer's mobile number as the primary key, which is stored in the DynamoDB table. 

With that unique ID, we can automatically fetch all the associated customer details. This eliminates the need for manual processes or asking the customer for redundant information over and over again like their case ID or past history. DynamoDB allows us to streamline the process.

Another valuable feature is how DynamoDB lets us handle multiple processes within a company. For instance, if customers call with different issues – like inquiries about loans, credit cards, or savings accounts – DynamoDB helps us automatically route each customer to the appropriate department. If a customer has a question about a loan, they'll be seamlessly directed to the loan queue.

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 use Amazon DynamoDB to track account IDs, address ranges, and descriptions. It is primarily used to automate the process of maintaining our about 1,120 AWS accounts. We also use DynamoDB in production for a pretty large-scale product.

Amazon DynamoDB has been a reliable service that is very performant and meets our expectations. Its automation capacity reduces the need for more personnel, which aligns perfectly with our strategy of focusing on serverless services. The ease of data replication between regions is a significant benefit since it was already integrated into the solution.

In our organization, Amazon DynamoDB stores data temporarily in tables that can be exported later. 

There are multiple on-demand features present in Amazon DynamoDB. The solution is able to auto-scale without manual intervention or monitoring. The AWS Lambda integrates with the solution and functions automatically as per the need. When the product is used for the data streaming background, a significant load is required. 

The first use case was indexing large quantities of data streaming in from Kinesis so that we could look up the data and collect it for MapReduce jobs.

Its current use case is as primary storage for a web-based service. It's a global data store for everything to do with content and customers.

In terms of its version, it's hosted. There is no version. You just have DynamoDB.

We switched to DynamoDB from a relational database that would've had scale problems and would've cost a lot of money to run at scale. DynamoDB allows us to match expenses to usage. When not many people are using and it's a quiet day, there is a low cost. On a busy day, there is a higher cost. We get good performance along the way and less maintenance on the database.

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.

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.