Amazon Redshift Data Sharing for Multi-Cluster Scenarios

DEA-C01 By islever Editorial Last updated

Automate data processing by using AWS services. Orchestrate data pipelines.

Short answer

A Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.

Question

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?
  1. Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing. Correct Answer
  2. Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
  3. Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
  4. Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

Key Takeaways

Data Sharing executes consumer queries on the consumer's nodes, leaving the producer's resources untouched.

Learn how Amazon Redshift Data Sharing enables secure, low-cost data exchange between clusters without impacting producer performance.

Common mistake: Choosing Option D because it seems to 'isolate' resources, but it fails on timeliness and complexity compared to native Data Sharing.

Expert Analysis

The correct answer is A. This scenario highlights the specific capabilities of Amazon Redshift Data Sharing versus traditional data replication or direct access methods.

Redshift Data Sharing allows a producer cluster (the ETL cluster) to share live, up-to-date data with consumer clusters (the BI cluster) without copying the data. The key constraint here is minimizing usage of the ETL cluster's computing resources. When using Data Sharing, queries run by the consumer cluster are executed on the consumer's compute nodes, not the producer's. Therefore, the critical analysis tasks in the ETL cluster remain completely unaffected by the sales team's queries.

Options B and C involve granting direct access to the ETL cluster. This would force the sales team's queries to consume the ETL cluster's CPU and memory, directly violating the requirement to minimize resource usage and potentially interrupting critical ETL tasks. While views can restrict what data is seen, they do not isolate where the query processing happens.

Option D suggests unloading data to S3 and using Redshift Spectrum. While this isolates compute resources, it introduces significant latency and operational overhead for a "weekly" task, whereas Data Sharing provides near real-time access with zero ETL compute impact. Furthermore, joining data across different storage mechanisms (Spectrum tables vs. native Redshift tables) is more complex than the seamless federation offered by Data Sharing.

Official Reference

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

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

Exam Strategy

When you see requirements to 'minimize impact' on a primary workload while allowing other teams to query that data, look for services that separate compute from storage or enable federated querying. In Redshift, prioritize Data Sharing over direct access or materialized views if real-time or frequent updates are needed, as it offloads query execution to the consumer cluster.

Frequently Asked Questions

Does Redshift Data Sharing copy data?

No, Redshift Data Sharing does not copy data. It creates a logical link, allowing consumers to query producer data in place without duplication.

Can I join shared data with local data?

Yes, consumer clusters can seamlessly join shared data from the producer cluster with their own local data using standard SQL syntax.

More DEA-C01 FAQ →

Community Votes

A
67%
D
33%

67% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 15 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 15

  1. lucas_rfsb 👍 7
    Sorry I wanted to select A but did D
  2. arvehisa 👍 6 Answered A
    A: redshift data sharing: https://docs.aws.amazon.com/redshift/latest/dg/data_sharing_intro.html With data sharing, you can securely and easily share live data across Amazon Redshift clusters. B: materialized view is only within 1 redshift cluster, across different tables
  3. lucas_rfsb 👍 5 Answered D
    In my opinion using Redshift Data Sharing will consume less resources. 'D' envolves using a S3 bucket.
  4. GiorgioGss 👍 5 Answered A
    Initially I would go with B but that definitely will use more resource.
  5. Anonymous learner 👍 4 Answered A
    To share data between Redshift clusters and meet the requirements of sharing ETL cluster data with the sales team without interrupting critical analysis tasks and minimizing the usage of the ETL cluster's computing resources, Redshift Data Sharing is the way to go https://docs.aws.amazon.com/redshift/latest/dg/data_sharing_intro.html "Supporting different kinds of business-critical workloads – Use a central extract, transform, and load (ETL) cluster that shares data with multiple business intelligence (BI) or analytic clusters. This approach provides read workload isolation and chargeback for individual workloads. You can size and scale your individual workload compute according to the workload-specific requirements of price and performance"
Show 10 more comments
  1. d8945a1 👍 4 Answered A
    Typical usecase of datasharing in Redshift. The question mentions that - 'team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.' This is possible with datashare.
  2. jasango 👍 3 Answered D
    The spectrum table is accessed from the sales cluster with zero impact on the ETL cluster.
  3. VerRi 👍 3 Answered A
    Typetical Redshift data sharing use case
  4. motk123 👍 2
    Seems that the performance of the critical ETL cluster should not be affected when using data sharing, so the answer is likely A: https://docs.aws.amazon.com/redshift/latest/dg/data_sharing_intro.html Supporting different kinds of business-critical workloads – Use a central extract, transform, and load (ETL) cluster that shares data with multiple business intelligence (BI) or analytic clusters. This approach provides read workload isolation and chargeback for individual workloads. You can size and scale your individual workload compute according to the workload-specific requirements of price and performance. https://docs.aws.amazon.com/redshift/latest/dg/considerations.html The performance of the queries on shared data depends on the compute capacity of the consumer clusters.
  5. certplan 👍 2
    Overall, while both options offer ways to share data between the ETL and BI clusters, Option D offers a more robust and scalable solution that minimizes the impact on the ETL cluster's resources and provides greater flexibility and independence for the sales team's analysis tasks. By unloading a copy of the data from the ETL cluster to Amazon S3 and leveraging Redshift Spectrum for querying, the solution aligns with AWS best practices for managing data and resource usage in Amazon Redshift clusters. It ensures that critical analysis tasks are not interrupted while providing the sales team with the necessary access to perform their analysis tasks efficiently.
  6. valuedate 👍 2
    key words: "weekly" "The solution must minimize usage of the computing resources of the ETL cluster." Answer:D
  7. certplan 👍 1
    Options A, B, and C involve granting the sales team direct access to the ETL cluster, which could potentially impact the performance of the ETL cluster and interfere with its critical analysis tasks. Option D provides a more isolated and scalable approach by leveraging Amazon S3 and Redshift Spectrum for data sharing while minimizing the usage of the ETL cluster's computing resources. https://docs.aws.amazon.com/redshift/latest/dg/c-using-spectrum-sharing-data.html https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-design-tables.html
  8. wimalik 👍 1
    A as Redshift data sharing allows you to share live data across Redshift clusters without having to duplicate the data. This feature enables the sales team to access the data from the ETL cluster directly without interrupting the critical analysis tasks or overloading the ETL cluster's resources. The sales team can join this shared data with their own data in the BI cluster efficiently.
  9. San_Juan 👍 1 Answered D
    "The solution must minimize usage of the computing resources of the ETL cluster." That is key. You shouldn't use ETL cluster, so unload data to S3 and run queries in a separate Redshift Spectrum database. ETL cluster do nothing meanwhile.
  10. Deno_Dennis 👍 1 Answered A
    The answer is A. Use Amazon Redshift data sharing to share data with the sales team. This is the most cost-effective way that minimizes usage of the computing resources of the ETL cluster.

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