Amazon Redshift COPY Command for Optimal Throughput

DEA-C01 By islever Editorial Last updated

Perform data ingestion.

Short answer

D Use a single COPY command to load the data into the Redshift cluster.

Question

A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of files into a fact table that is in a Redshift cluster. The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the fact table. Which solution will meet these requirements?
  1. Use multiple COPY commands to load the data into the Redshift cluster.
  2. Use S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster.
  3. Use a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.
  4. Use a single COPY command to load the data into the Redshift cluster. Correct Answer

Key Takeaways

This question tests knowledge of Redshift's MPP architecture and its automated parallel ingestion capabilities. The common trap is assuming that manual parallelism (multiple commands) offers more control or speed than the service's built-in optimization.

To achieve the greatest possible throughput when loading data into Amazon Redshift, a single COPY command is the required solution. This page explains why the native COPY command is superior to manual parallelization or external tools.

Common mistake: Candidates often choose Option A (Multiple COPY commands), believing that manually splitting workloads across nodes yields higher performance. However, this ignores the overhead of managing multiple connections and transactions, which Redshift's single COPY command handles natively and efficiently.

Expert Analysis

Why the Answer Is Correct

Option D is correct because the Amazon Redshift COPY command is specifically designed to ingest data in parallel from S3, DynamoDB, or EMR using the cluster's Massively Parallel Processing (MPP) architecture. It automatically distributes the load across all nodes, utilizes optimal concurrency scaling, and manages resource allocation without manual intervention. This results in the highest possible throughput and most efficient use of cluster resources for bulk loads.

Why the Other Options Are Wrong

Option A suggests using multiple COPY commands; while Redshift supports concurrent queries, using multiple distinct COPY commands for the same table can lead to contention on system tables and locks, potentially reducing throughput compared to a single optimized command. Option B involves Hadoop and HDFS, which adds unnecessary latency and complexity for direct S3-to-Redshift loading. Option C suggests using INSERT statements, which are row-by-row operations and are extremely slow compared to the batch-oriented COPY command, making them unsuitable for high-throughput requirements.

Community Comment Notes

The community consensus strongly favors Option D. Users like antun3ra highlight that a single COPY command automatically parallelizes the load across all nodes, ensuring optimal resource usage. Others, such as Shanmahi, reference official documentation confirming that Redshift's copy command leverages MPP architecture to read and load data in parallel. While some initially considered multiple commands (Option A), the prevailing view is that the native COPY command is the industry-standard best practice for this scenario.

Official Reference

https://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html

This link opens the official vendor documentation in a new tab.

Exam Strategy

When asked about bulk data loading into Redshift, always prioritize the COPY command over INSERTs or manual parallelization. Remember that Redshift is optimized for parallel execution within a single command context.

Frequently Asked Questions

Why not use multiple COPY commands for parallel loading?

A single COPY command is optimized by Redshift to distribute work across all nodes internally. Multiple commands can cause lock contention and management overhead.

Is INSERT ever faster than COPY in Redshift?

No. INSERT is a row-level operation and is significantly slower than the bulk-load capability of the COPY command, which is designed for high-throughput ingestion.

More DEA-C01 FAQ →

Community Votes

D
100%

100% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 5 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 5

  1. canace 👍 5
    D? https://docs.aws.amazon.com/redshift/latest/dg/t_Loading-data-from-S3.html
  2. antun3ra 👍 3 Answered D
    A single COPY command automatically parallelizes the load operation across all nodes in the Redshift cluster. This ensures optimal use of cluster resources.
  3. Shanmahi 👍 2 Answered D
    Agree with canace; Redshift's copy command uses MPP architecture to read and load in parallel from files into DWH.
  4. phkhadse 👍 2
    A - Using multiple COPY commands allows parallel loading of data, which maximizes throughput.
  5. cas_tori 👍 1 Answered D
    this is D

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