Efficient Data Loading into Redshift
Question
A company is looking towards using a Redshift cluster for hosting their data warehouse.
CSV files are being generated from their on-premise location and then will be stored in S3
The company needs to ensure the Loading process into Redshift is as fast and efficient as possible.
Which of the following can help ensure this? Choose 2 answers from the options given below.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - B and D.
These recommendations are given in the AWS Documentation.
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Note.
We strongly recommend that you divide your data into multiple files to take advantage of parallel processing.
Split your data into files so that the number of files is a multiple of the number of slices in your cluster.
That way Amazon Redshift can divide the data evenly among the slices.
The number of slices per node depends on the node size of the cluster.
For example, each DS1.XL compute node has two slices, and each DS1.8XL compute node has 32 slices.
For more information about the number of slices that each node size has, go to About Clusters and Nodes in the Amazon Redshift Cluster Management Guide.
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Options A and C are incorrect since these would go against AWS recommendations.
For more information on data files for Redshift, please refer to the below URL.
https://docs.aws.amazon.com/redshift/latest/dg/t_splitting-data-files.htmlTo ensure a fast and efficient loading process of CSV files from S3 into Redshift, the following steps can be taken:
Compress the CSV files: Compressing the CSV files can help in reducing the file size, which in turn can lead to faster data transfer from S3 to Redshift. The compressed file can be in gzip or bzip2 format, which can be easily uncompressed by Redshift during the loading process. Therefore, option A is correct.
Use a large file size: Uploading multiple small files to S3 can lead to slower data transfer rates and longer loading times. It is recommended to upload large files to S3, which can be easily processed by Redshift. In addition, a larger file size can reduce the number of files that need to be uploaded to S3, which in turn can improve the loading process. Therefore, option B is also correct.
Use an appropriate node type: The node type of the Redshift cluster plays an important role in the loading process. It is recommended to use a node type with a higher instance size, as it can handle large amounts of data more efficiently. Therefore, option D is correct, while option C is incorrect.
In conclusion, to ensure a fast and efficient loading process of CSV files from S3 into Redshift, it is recommended to compress the CSV files and upload large files to S3. Additionally, using a node type with a higher instance size can also improve the loading process.
