Importing CSV Data into DynamoDB

Answer: A.

Option A is CORRECT because creating an AWS Lambda Function to read the file from the S3 bucket and import the data items into the DynamoDB table is the simplest and most cost-efficient method to import CSV data.

Option B is incorrect because AWS Data Pipeline is not the optimal solution.

It requires non-trivial activities and costs associated with the configuration of Data Pipelines and EMR cluster infrastructure.

Option C is incorrect because AWS CLI can only import JSON formatted data into DynamoDB tables.

Option D is incorrect because it is only possible to enter single items into DynamoDB tables using AWS Management Console.

This would be very time-consuming for bulk import of a large CSV file.

Reference:

https://aws.amazon.com/blogs/database/implementing-bulk-csv-ingestion-to-amazon-dynamodb/

The optimal method for importing a CSV file stored in an S3 bucket into a DynamoDB table depends on several factors such as the size of the file, the frequency of imports, and the available resources. However, in general, the most efficient and scalable way to import data into DynamoDB from S3 is to use AWS Data Pipeline or AWS Lambda Function.

AWS Data Pipeline is a managed service that enables you to easily create, execute, and monitor data-driven workflows in the cloud. Data Pipeline provides pre-built connectors for various AWS services, including S3 and DynamoDB. By creating a pipeline, you can define the data source, the data destination, and the transformations to be applied to the data. Data Pipeline also supports scheduling and parallelism, allowing you to import large datasets quickly and efficiently.

AWS Lambda is a serverless computing service that enables you to run code without provisioning or managing servers. You can create a Lambda function that reads the CSV file from S3, parses the data, and inserts it into the DynamoDB table. Lambda functions are highly scalable and can process millions of records in parallel.

The AWS CLI is a command-line tool that provides a unified interface to various AWS services. You can use the CLI to read the CSV file from S3 and insert the data into the DynamoDB table. While the CLI is a viable option for small datasets, it can be challenging to use for large datasets, and it may not be as scalable as Data Pipeline or Lambda.

Using the AWS Management Console to import data into DynamoDB is not recommended for large datasets as it may take a long time and can be prone to errors. The console is suitable for small datasets and ad-hoc queries.

In summary, the optimal method for importing data into DynamoDB from S3 depends on several factors, but generally, AWS Data Pipeline and AWS Lambda Function are the most efficient and scalable options for large datasets, while the CLI and Console are better suited for small datasets and ad-hoc queries.