Exam AWS Certified Data Analytics - Specialty topic 1 question 31 discussion

B for sure. The COPY command loads the data in parallel from multiple files, dividing the workload among the nodes in your cluster. When you load all the data from a single large file, Amazon Redshift is forced to perform a serialized load, which is much slower. Split your load data files so that the files are about equal size, between 1 MB and 1 GB after compression. For optimum parallelism, the ideal size is between 1 MB and 125 MB after compression. The number of files should be a multiple of the number of slices in your cluster

Ans B A = wrong, compression means download file from S3 then compress, time-consuming, also you use COPY for files not INSERT. C = wrong, will not improve performance. D = wrong, vacuum frees up storage. This is a question about parallel loading.

B for sure

B: I passed the test

Option B is the most appropriate solution for improving data loading performance. Splitting large .csv files and using a COPY command can parallelize the load process and reduce the data load time. The data partitioning by date can help further optimize the load process by reducing the data scanned for each load. Compressing the .csv files may help reduce the storage cost, but it may not improve the data load time. Using an INSERT statement to ingest data into Amazon Redshift can be slow and does not take advantage of Redshift's parallel processing capability. Amazon Kinesis Data Firehose can be used to ingest streaming data in real-time, but may not be the best choice for large batch loads. Loading the .csv files in an unsorted key order and vacuuming the table can help optimize the table for query performance but may not improve the data loading performance.

Correct answer is B as splitting the large file into multiple files can help improve the data loading performance using the COPY command. Option A is wrong as the COPY command would provide the best benefit. Option C is wrong as Kinesis Data Firehose cannot move data from S3 to Redshift. Kinesis Data Firehose delivers your data to your S3 bucket first and then issues an Amazon Redshift COPY command to load the data into your Amazon Redshift cluster. So it doesn't still improve the load performance. Option D is wrong as vacuuming will free up space but does not improve the load performance.

S3 to Redshift upload can be done through copy command. To utilize parallelism, large files are recommended to split into small chunk of files.

Don't know why B? here is uncompressed csv file, so no need to split file (Redshift will do automatically? In contrast, when you load delimited data from a large, uncompressed file, Amazon Redshift makes use of multiple slices. These slices work in parallel, automatically. This provides fast load performance. In https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-use-multiple-files.html

Answer = B

Answer is B

Answer is B

Ans:B split large files will help loading in performance. Having one large file will load in serialized manner which lowers performance

B is the right answer

B is correct for me

B for sure.

It's already in S3 so answer is B 100%

Answer is B, A compress is a good practice but then copy command not insert, Kinesis Firehose will not improve performance, the vacuum will help freeing space not improve perf too.