Exam AWS Certified Data Analytics - Specialty topic 1 question 26 discussion

D seems correct

Ans D Note: A and B are immediately out because RDS is not for analysis. C and D both work, but D is balanced between performance and cost. C may cost less (depending on data compression, frequency of queries) but query to recent data will be slower.

when we say "analytics queries", "analytics workloads" or "complex analysis", in 80% of the cases we call redshift... if we sum a short period of analysis (6 months in this case) redshift is a better option... rds will continue as a relational database, don't run analytics queries

D: I passed the test

Option D is the most suitable solution for this scenario. Explanation: Incrementally copy data from Amazon RDS to Amazon S3: This allows for storing of historical data in a cost-effective manner while allowing frequent querying of the more recent data in RDS. Load and store the most recent 6 months of data in Amazon Redshift: This provides a performant solution for frequent queries of the most recent data. Configure an Amazon Redshift Spectrum table to connect to all historical data: This enables joining of historical data with the more recent data in Redshift, providing the required analysis capability. Option A does not address the requirement to optimize performance for querying the most recent data. Option B involves creating a read replica of RDS, which may not be efficient for frequently queried data. Option C also does not provide a solution for frequent querying of the most recent data.

Option D suggests copying data from RDS to S3 incrementally, storing the most recent 6 months of data in Amazon Redshift, and configuring an Amazon Redshift Spectrum table to connect to all historical data. This approach allows the company to optimize cost and performance as Redshift is a cost-effective data warehousing solution that can handle large volumes of data. Additionally, using Redshift Spectrum enables the company to query both the recent and historical data sets together in real-time. Option A suggests creating a read replica of the RDS database to store the most recent 6 months of data and copying the historical data into Amazon S3. This approach does not allow for real-time querying of the historical data and may result in increased query latency.

A. By moving the data to S3 and Glue Catalog that carries both RDS and S3 schema will enable them to use the same schema for queries. Remember the requirement says "low cost". Redshift is out of the picture.

D for the win

D is the right answer as loading and querying recent 6 months of data via Redshift gives better performance and old data can be queried via Redshift spectrum C is wrong though it's possible to query the entire data in S3 using Athena, however, it will not be able to match the high performance offered by Redshift to query the last six months of data. So this option is not the best fit for the given use case. Options A & B are wrong as RDS is not an ideal solution to store and query historical data. Also, 6 months data may be several terabytes large.

D seems correct

Answer-D

Answer-D

D seems correct

D is correct...

effective way to query across S3 and RDS is using redshift spectrum

Historical Data points to Redshift Spectrum. Hence D

answer is D.