AWS Certified Data Engineer – AssociateData Operations and MonitoringHard
A data engineering team operates a critical data pipeline that ingests sensor data from IoT devices into Amazon S3, processes it with AWS Glue, and then stores the refined data in Amazon Redshift. The pipeline runs hourly. Recently, the team noticed that the Redshift cluster's CPU utilization is consistently high during the Glue job's write phase, leading to increased query latency for downstream analytics users. They need to optimize the Glue job's Redshift write performance to reduce cluster strain and improve overall pipeline efficiency. Which specific Glue job configuration or optimization should they implement?
- AConfigure the Glue job to use a larger number of 'max_redshift_connections' to parallelize writes.
- BIncrease the 'Number of Retries' parameter for the Glue job to handle transient Redshift connection issues.
- CReduce the 'timeout' parameter for the Glue job to fail faster if Redshift is unresponsive.
- DEnable 'use_s3_dist_copy' and set 'num_partitions' for the Redshift target to leverage S3 and parallel COPY.
Show answer & explanationAnswer & explanation
Correct answer: D. Enable 'use_s3_dist_copy' and set 'num_partitions' for the Redshift target to leverage S3 and parallel COPY.
To optimize Glue writes to Redshift, especially for large datasets, enabling 'use_s3_dist_copy' and configuring 'num_partitions' (or 'num_files') is crucial. This leverages Amazon S3 as a staging area and uses Redshift's highly efficient parallel COPY command to load data, significantly reducing the load on the Redshift cluster compared to direct JDBC writes.
Why the other options are wrong
- A. 'max_redshift_connections' controls the number of JDBC connections, which can be inefficient for large data volumes and still puts direct load on Redshift, unlike S3-based COPY.
- B. Increasing retries helps with transient errors but doesn't optimize write performance or reduce CPU strain during successful writes.
- C. Reducing the timeout will cause jobs to fail faster but does not address or optimize the underlying performance issue with Redshift writes.
Glue Redshift Write Optimization
Optimize AWS Glue writes to Amazon Redshift by using 'use_s3_dist_copy' and 'num_partitions' to stage data in S3 and leverage Redshift's parallel COPY command, reducing cluster load.
- S3 staging + Redshift COPY is highly efficient.
- Reduces direct Redshift cluster load.
- Parameters: use_s3_dist_copy, num_partitions/num_files.
Memory trick: Glue's best Redshift friend is S3, for a fast 'COPY' party.