Amazon Redshift Spectrum

5 minutes 5 Questions

Amazon Redshift Spectrum is a feature of Amazon Redshift that enables you to run SQL queries directly against the vast amount of data stored in Amazon S3. It allows you to harness the power of Redshift's parallel processing capabilities without the need to load or transform the data stored in S3. R…

Guide for Amazon Redshift Spectrum

Importance: Amazon Redshift Spectrum is a key feature within the Amazon Web Services (AWS) cloud platform that provides powerful analytics capabilities. It enables you to directly run SQL queries against exabytes of unstructured data in Amazon S3 storage. With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake”.
What it is: Amazon Redshift Spectrum is a feature that enables users to run Amazon Redshift SQL queries against data that is stored in Amazon S3. This allows you to store your data where you want, in the format you want, and have it available for processing when you need it.
How it works: First, you create an external table in your Amazon Redshift cluster for each dataset stored in Amazon S3. Your external tables schema should map to your data. After that, just issue SQL queries in Amazon Redshift to query the data in Amazon S3. Amazon Redshift Spectrum scales out to thousands of instances if required, so queries run incredibly quickly, regardless of the size of your dataset.
Exam Tips: When answering questions about Amazon Redshift Spectrum in an exam, remember the following points:
- Amazon Redshift Spectrum allows you to directly run SQL queries against large amounts of unstructured data stored in Amazon S3.
- Amazon Redshift Spectrum scales out to thousands of instances to ensure that queries run quickly, regardless of the size of the dataset.
- To use Amazon Redshift Spectrum, one must create an external table in the Amazon Redshift cluster for each dataset stored in Amazon S3.
- Remember that Amazon Redshift Spectrum is a paid service and pricing is based on the amount of data scanned, not the amount of data returned.

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Question 1

A company has a 100TB data lake on Amazon S3 and wants to query data using Amazon Redshift Spectrum. The data on S3 is partitioned by year, month, and day. The Redshift cluster querying the data has 10 nodes. What is the best practice for optimizing performance in this situation?

Set the number of Redshift Spectrum nodes to match the current date Set the number of Redshift Spectrum nodes to the maximum allowed Set the number of Redshift Spectrum nodes to 1 Set the number of Redshift Spectrum nodes to 10

Correct Answer: Set the number of Redshift Spectrum nodes to 10

To optimize performance, the best practice is to set the number of Redshift Spectrum nodes equal to the number of nodes in the Redshift cluster. This will maximize parallel processing, which is essential for managing large-scale data sets efficiently.

Question 2

A company frequently queries for specific information in a large dataset stored on Amazon S3 using Redshift Spectrum. Users have reported slow response times for certain queries. As the AWS Solutions Architect, what action would be most effective for improving performance?

Create optimized views for the data in Amazon S3 Regularly update the Redshift Spectrum metadata Enable caching for all Redshift Spectrum queries Disable Redshift Spectrum's automatic vacuum feature

Correct Answer: Create optimized views for the data in Amazon S3

By creating optimized views, users will be querying from a smaller and more organized dataset, which will improve query performance. Other actions, such as updating metadata, disabling vacuuming, or enabling caching, will not directly address the issue of slow queries for specific information.

Question 3

An e-commerce company uses Amazon Redshift with Redshift Spectrum for analytics. The sales team needs self-service BI dashboards and ad hoc reports that will query additional data sources, but these workloads must not impact the performance of the existing production analytics on Redshift. What is the best architectural approach?

Configure Redshift Workload Management (WLM) queues to prioritize reporting queries on the same cluster Add more nodes to the existing Redshift cluster Decrease concurrency scaling usage limit Utilize a separate reporting instance of the Redshift cluster

Correct Answer: Utilize a separate reporting instance of the Redshift cluster

Creating a separate Amazon Redshift cluster for BI/reporting cleanly isolates compute and concurrency from the production analytics workload. The reporting cluster can consume production data via Redshift Data Sharing (read-only) and join with additional sources using Redshift Spectrum or federated queries, ensuring no impact on the primary cluster’s performance. This approach enables independent scaling, WLM configuration, and maintenance. Simply adding nodes or relying on concurrency scaling does not guarantee workload isolation, and prioritizing queries with WLM on the same cluster still risks resource contention.

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