Manage DynamoDB Tables for Analytics Processing

Managing DynamoDB Tables for Analytics Processing

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Question

An application currently makes use of DynamoDB tables.

There are a thousand requests made per second on the DynamoDB table.

Another application takes the changes to the items in the DynamoDB table for further analytics processing.

Which of the following can be effectively used to manage this requirement?

Answers

Explanations

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A. B. C. D.

Answer - D.

The below information from the AWS Documentation helps to supplement this requirement.

Capturing Table Activity with DynamoDB Streams.

Many applications can benefit from capturing changes to items stored in a DynamoDB table in time when such changes occur.

Here are some example use cases:

An application in one AWS region modifies the data in a DynamoDB table.

A second application in another AWS region reads these data modifications and writes the data to another table, creating a replica that stays in sync with the original table.

A popular mobile app modifies data in a DynamoDB table at the rate of thousands of updates per second.

Another application captures and stores data about these updates, providing near real-time usage metrics for the mobile app.

A global multi-player game has a multi-master topology, storing data in multiple AWS regions.

Each master stays in sync by consuming and replaying the changes that occur in the remote regions.

An application automatically sends notifications to the mobile devices of all friends in a group as soon as one friend uploads a new picture.

A new customer adds data to a DynamoDB table.

This event invokes another application that sends a welcome email to the new customer.

DynamoDB Streams enables solutions such as these and many others.

DynamoDB Streams captures a time-ordered sequence of item-level modifications in any DynamoDB table and stores this information in a log for up to 24 hours.

Applications can access this log and view the data items as they appeared before and after they were modified, in near real-time.

Options A and B are incorrect since these would result in a lot of throughput requirement for the table.

Option C is incorrect since this is used for the replication of data.

For more information on DynamoDB streams, please refer to the below URL-

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.html

To manage the requirement of having another application take changes to the items in the DynamoDB table for further analytics processing, enabling streams for DynamoDB would be the most effective solution.

Streams are a feature of DynamoDB that allow real-time data to be sent to other AWS services or applications. When an item in a DynamoDB table is updated, deleted, or inserted, a stream record containing the details of the change is added to a DynamoDB stream. This stream can then be used to process the changes in real-time, enabling the other application to process the changes for further analytics processing.

Enabling a scan on the entire table or creating a query to check for changes would not be an effective solution for managing this requirement. Scanning an entire table can be a resource-intensive operation and can cause performance issues, particularly when there are a high number of requests per second. Creating a query to check for changes would also be resource-intensive, and may not provide real-time updates, meaning the other application may not have access to the latest changes in the table.

Enabling global tables for DynamoDB would not be an effective solution for managing this requirement either. Global tables are used for replicating data across multiple regions for disaster recovery or to improve access times for users in different geographic locations. While global tables can be used to replicate data across multiple tables, they are not designed for real-time data processing or analytics.

Therefore, enabling streams for DynamoDB would be the most effective solution for managing this requirement, as it would allow real-time data processing and analytics.