Cost-Effective SQL Analysis of S3 Parquet Logs

SAA-C03 By islever Editorial Last updated Oct 14, 2026

Design cost-optimized database solutions.

Short answer

C Create an AWS Glue crawler to store and retrieve table metadata from the S3 bucket. Use Amazon Athena to run SQL statements directly on the data in the S3 bucket.

Question

A company has stored 10 TB of log files in Apache Parquet format in an Amazon S3 bucket. The company occasionally needs to use SQL to analyze the log files. Which solution will meet these requirements MOST cost-effectively?

Create an Amazon Aurora MySQL database. Migrate the data from the S3 bucket into Aurora by using AWS Database Migration Service (AWS DMS). Issue SQL statements to the Aurora database.
Create an Amazon Redshift cluster. Use Redshift Spectrum to run SQL statements directly on the data in the S3 bucket.
Create an AWS Glue crawler to store and retrieve table metadata from the S3 bucket. Use Amazon Athena to run SQL statements directly on the data in the S3 bucket. Correct Answer
Create an Amazon EMR cluster. Use Apache Spark SQL to run SQL statements directly on the data in the S3 bucket.

Key Takeaways

The core concept is using AWS Athena for ad-hoc SQL analysis on S3 data, with the common trap being the selection of heavy infrastructure like Redshift or Aurora for simple, occasional queries.

This question explores the most cost-effective method to run SQL queries on 10 TB of Apache Parquet logs stored in Amazon S3. The correct solution leverages serverless querying to avoid data migration and infrastructure management costs.

Common mistake: Candidates often choose Amazon Redshift Spectrum (Option B) because it also allows querying S3 data, but they overlook that Athena is generally more cost-effective for occasional use due to its simpler pay-per-query model without cluster overhead.

Expert Analysis

Why the Answer Is Correct

Amazon Athena is a serverless interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. Since the data is already in Apache Parquet format, Athena can query it efficiently. Option C includes creating an AWS Glue crawler, which is the standard mechanism to catalog the Parquet files and infer schema metadata into the AWS Glue Data Catalog, enabling Athena to understand the table structure. This approach incurs no upfront cost, requires no maintenance, and charges only for the bytes scanned per query, making it the most cost-effective for occasional use.

Why the Other Options Are Wrong

Option A (Aurora MySQL) is incorrect because migrating 10 TB of data involves significant EBS/storage costs, compute costs for the database instance, and operational overhead for DMS and management, which is overkill for occasional analysis. Option B (Redshift Spectrum) allows querying S3 data, but maintaining a Redshift cluster (even if not used) or paying for spectrum queries can be more expensive and complex than Athena for infrequent use cases. Option D (EMR/Spark SQL) requires provisioning and managing a cluster, which adds significant cost and operational burden compared to a fully managed serverless service like Athena.

Community Comment Notes

The community consensus strongly supports Option C, with users highlighting that 'S3 + SQL = Athena' is a fundamental pattern for this scenario. One user noted that while Redshift Spectrum is viable, 'costs are really high especially for infrequent use,' reinforcing Athena's cost advantage. Another commenter simply pointed out that 'Apache Parquet => Glue Crawler' is the necessary step to make the data queryable by Athena, confirming the technical accuracy of the combination.

Exam Strategy

When asked for the 'most cost-effective' solution for ad-hoc SQL queries on S3 data, always consider Amazon Athena first. If the data needs schema discovery, pair it with AWS Glue. Avoid provisioning databases (Aurora, Redshift) or clusters (EMR) unless the workload is continuous or requires specific features that serverless options cannot provide.

Frequently Asked Questions

Why not just use Redshift Spectrum instead of Athena?

While both query S3, Athena is typically more cost-effective for occasional use as it has no cluster maintenance and a simpler pay-per-query model compared to Redshift Spectrum.

Is the Glue Crawler strictly necessary for Athena?

For Parquet files, a crawler helps automatically discover the schema and partitioning, saving manual table definition time. However, you can also create tables manually in the Glue Data Catalog.

More SAA-C03 FAQ →

Community Votes

100%

100% of anonymous learners picked answer C. 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

Kezuko 👍 3 Answered C

Apache Parquet => Glue Crawler
LeonSauveterre 👍 1 Answered C

A - Aurora is cool but migrating 10 TB of data incurs significant costs and operational overhead. B - Redshift Spectrum allows querying data directly in S3 without loading it into Redshift, but costs are really high especially for infrequent use. C - Athena is serverless and charges only for the data scanned by queries. Glue Crawler automatically extracts metadata and schema information from the Parquet files. No need to migrate anything. D - Just by the look of it I know I'll go bankrupt if I choose that.
kempes 👍 4 Answered C

Option C
sandordini 👍 2 Answered C

S3 + SQL = Athena
asdfcdsxdfc 👍 2 Answered C

c is correct

Reader comments & corrections

No reader corrections yet — spotted an error or have a note? Share it below.

Practice All SAA-C03 Questions

Access 120 questions with complete answers and detailed explanations.

View Full SAA-C03 Practice Test →

← Back to SAA-C03 Study Guide