A company is planning on using AWS Redshift as their data store.
They have a lot of files that are going to be dropped into AWS S3 by different departments.
They want to have the ability to automate the way the files get loaded into Redshift.
How can they accomplish this in an efficient and cost-effective manner?
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A. B. C. D.Answer - B.
An example of this is given in the AWS Blog sites.
Here you can use AWS Lambda code available in a Gut repository which can be used to automatically transfer files from various S3 buckets to AWS Redshift.
Option A is incorrect since this does not cost efficient.
Option C is incorrect since there are no triggers as of yet in Redshift.
Option D is incorrect since there SQS would be ineffective in this scenario.
For more information on this use case scenario, please visit the url.
https://aws.amazon.com/blogs/big-data/a-zero-administration-amazon-redshift-database-loader/Option B is the most efficient and cost-effective way to automate loading data into AWS Redshift from S3. Here's why:
AWS S3 is a highly scalable object storage service that can store and retrieve large amounts of data from anywhere on the web. AWS Redshift is a data warehouse that allows businesses to store, analyze, and retrieve large amounts of data from multiple sources. To load data from S3 into Redshift, the company needs an automated solution that can transfer the data in a timely, efficient, and cost-effective manner.
Option A: Create a cron job on an EC2 Instance to poll the S3 buckets and drop the content onto AWS Redshift This option involves manually setting up and managing an EC2 instance and writing a custom script to poll the S3 bucket for new files and load them into Redshift. This approach requires a lot of maintenance, as the company will have to monitor the EC2 instance and ensure that it's always up and running. Additionally, this approach does not take advantage of any of the built-in AWS services that can automate the process.
Option B: Use S3 events to invoke Lambda functions that will transfer the files to AWS Redshift. This option leverages AWS services such as S3 and Lambda to create an automated process for loading data into Redshift. S3 events can trigger Lambda functions whenever new files are added to a bucket. The Lambda function can then load the data into Redshift using the COPY command, which is optimized for loading large amounts of data quickly. This approach is highly scalable, as it can handle a large number of files and can automatically scale to meet demand. It's also cost-effective, as Lambda charges only for the time the function runs, and there are no charges for idle time.
Option C: Use AWS Redshift triggers to poll the S3 buckets and drop the content onto its tables This option requires setting up a Redshift trigger that will poll the S3 bucket for new files and load them into Redshift. This approach is less efficient than using S3 events because it requires polling the bucket, which can increase the number of API requests and reduce performance. Additionally, this approach does not take advantage of any of the built-in AWS services that can automate the process.
Option D: Use AWS S3 events to call SQS and then use the queues to drop the content onto its tables. This option involves using S3 events to trigger an SQS queue, which can then load the data into Redshift. This approach is less efficient than using Lambda because it requires setting up and managing an SQS queue, which can increase complexity and reduce performance. Additionally, this approach does not take advantage of the built-in capabilities of Redshift, which can optimize the loading process.