Learn how to efficiently process millions of JSON files stored in Amazon S3 and load them into Amazon Redshift tables using AWS Glue dynamic frames.
Table of Contents
Question
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?
A. Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.
B. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.
C. Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.
D. Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.
Answer
The best solution to reduce data processing time in this scenario is:
B. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.
Explanation
AWS Glue provides a feature called dynamic frame file grouping which allows you to efficiently process a large number of small files. When you enable this option, Glue will automatically group the small JSON files together into larger files before processing them. This significantly reduces the overhead and time required to open, read and process each individual small file.
By leveraging dynamic frames, the data engineer can directly ingest the millions of 1KB JSON files stored in S3. Glue will intelligently group them into larger files behind the scenes. The grouped files can then be processed and converted into the Parquet format within Glue before being loaded into Redshift tables.
This approach is much more efficient than using Lambda to manually group files and write them back to S3 (Option A), using the Redshift COPY command to load the raw JSON (Option C) which doesn’t allow for easy Parquet conversion, or switching to Amazon EMR (Option D) which would require rewriting the existing Glue-based processing pipeline.
So in summary, utilizing the built-in file grouping capability of AWS Glue dynamic frames, without changing the overall architecture, will provide the most straightforward and effective way to reduce the data processing time for the growing number of JSON test result files.
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