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When we configure trigger functionality in a DynamoDB table, it will trigger an AWS Lambda function. The feature is comparable with the traditional RDBMS databases. TriggersĭynamoDB supports triggers capacity using AWS Lambda functions. When we set auto-scaling mode, we scale based on a percentage of load and then scale down and keep a consistent read-write capacity for a normal workload. The auto-scaling feature is more expensive. A workload that can go up and come down using on-demand capacity will auto-scale instantly. So we can either use a read-write capacity mode we can use an autoscale feature. And that’s usually how most of the applications behave the read access is usually 70% on average compare to write capacity.Īlong with that, it provides an auto-scale feature to unpredictable workload as well. We can have more readers and fewer writers to keep the cost minimal. Write capacities are more expensive compare to read capacity. Using Read/Write capacity we can manage the cost of DynamoDB because it charges based on Read/Write capacity. Now let’s talk about few important concepts of DynamoDB. And because of that, it supports an extremely flexible schema. Fifthly, DynamoDB can support ACID transactions.Īnother very important thing to mention about DynamoDB is that it uses key-value and document data models.Fourthly, it can support more than 10 trillion requests per day and up to 20 million requests per second.Thirdly, DynamoDB supports in-memory cache for highly scalable applications.Secondly, it fully managed and can be set up to support multi-region active-active mode.Firstly, DynamoDB is an extremely fast serverless NoSQL database.And finally, I am going to access the table using.Thirdly, I am going to create a DynamoDB table using the AWS Console.
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I will initially focus on features, and after that accessing AWS DynamoDB using a C# application. In this blog post, I am going to walk through the AWS DynamoDB database and its feature.