Ideal Data Store Combinations for Highly Durable and Available Data Storage
Question
There is a requirement for a data store.
It needs to be highly durable and available.
Initially the data will be around 10 TB.The data will continuously grow.
The data will consist of both structured and un-structed data.
Which of the following would be the ideal data store combinations for these requirements?
Answers
Explanations
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A. B. C. D.Answer - A.
The AWS Documentation mentions the following.
As mentioned above, you can also take advantage of Amazon Simple Storage Service (Amazon S3) to store large attribute values that cannot fit in a DynamoDB item.
You can store them as an object in Amazon S3 and then store the object identifier in your DynamoDB item.
You can also use the object metadata support in Amazon S3 to provide a link back to the parent item in DynamoDB.
Store the primary key value of the item as Amazon S3 metadata of the object in Amazon S3
Doing this often helps with maintenance of the Amazon S3 objects.
Option B is incorrect since Redshift is not an ideal case for unstructured data and Kinesis streams is used for streaming and not data persistence.
Option C is incorrect since AWS Aurora is not an ideal case for unstructured data and Kinesis streams is used for streaming and not data persistence.
Option D is incorrect since there is no requirement for streaming of data.
For more information on using S3 with DynamoDB, please refer to the below URL.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-use-s3-too.htmlThe ideal data store combination for this scenario would be option A: AWS S3 and DynamoDB.
AWS S3 (Simple Storage Service) is a highly durable and scalable object storage service that can store large amounts of structured and unstructured data. S3 is designed to provide 99.999999999% durability and 99.99% availability for objects, which makes it a great choice for storing data that needs to be highly available and durable.
DynamoDB is a NoSQL database service that is also highly available and durable. It is designed to provide consistent low-latency performance at any scale, making it a good choice for storing and querying structured data. DynamoDB is also designed to be highly scalable, so it can handle the continuous growth of data.
Together, S3 and DynamoDB provide a powerful and flexible data store solution. Structured data can be stored in DynamoDB, while unstructured data can be stored in S3. The two services can be integrated using AWS Lambda functions or other AWS services like AWS Glue or Amazon EMR to enable advanced analytics and machine learning capabilities on the stored data.
Option B, AWS Redshift and Kinesis Streams, is not a good choice for this scenario because Redshift is a data warehousing service that is optimized for querying large datasets, rather than storing large amounts of unstructured data. Kinesis Streams is a real-time data streaming service that is designed for ingesting and processing real-time data, so it may not be the best choice for storing large amounts of data.
Option C, AWS Aurora and Kinesis Streams, is also not a good choice for this scenario because Aurora is a relational database service that is optimized for storing and querying structured data, rather than unstructured data. Kinesis Streams, as mentioned before, is designed for ingesting and processing real-time data.
Option D, AWS S3 and Kinesis Stream firehose, is not as ideal as option A because Kinesis Stream firehose is a streaming data service that is optimized for delivering real-time data to other AWS services, rather than storing data. Therefore, it is not a good choice for a data store that needs to be highly durable and scalable.
