Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 2 discussion

Answer: C Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon Simple Storage Service (Amazon S3) using standard SQL. With a few actions in the AWS Management Console, you can point Athena at your data stored in Amazon S3 and begin using standard SQL to run ad-hoc queries and get results in seconds.

Keyword: - Queries will be simple and will run on-demand. - Minimal changes to the existing architecture. A: Incorrect - We have to do 2 step. load all content to Redshift and run SQL query (This is simple query so we can you Athena, for complex query we will apply Redshit) B: Incorrect - Our query will be run on-demand so we don't need to use CloudWatch Logs to store the logs. C: Correct - This is simple query we can apply Athena directly on S3 D: Incorrect - This take 2 step: use AWS Glue to catalog the logs and use Spark to run SQL query

C Correct answer. Athena integrates seamlessly with S3 and allows you to run simple SQL queries in no time. When working with Apache Spark or with SQL in S3, using this service is the best option

Amazon Athena meets the requirements for on-demand log analysis with the least operational overhead. It integrates seamlessly with data in Amazon S3, uses SQL for querying, and does not require managing servers or clusters

Amazon Athena meets the requirements for on-demand log analysis with the least operational overhead. It integrates seamlessly with data in Amazon S3, uses SQL for querying, and does not require managing servers or clusters.

AWS Athena is a serverless, interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL. It is commonly used for querying logs, performing ad hoc analysis, and building dashboards. Key aspects of AWS Athena include: Key Features: 1)Serverless: No infrastructure to manage; pay only for the queries you run. 3)Query S3 Data: Works directly with structured, semi-structured, and unstructured data stored in S3 (e.g., CSV, JSON, ORC, Parquet). 3)Integration: Works seamlessly with AWS Glue for data cataloging, enabling you to query datasets more efficiently. 4)SQL Support: Built on Presto, Athena supports ANSI SQL. 5)Pay-Per-Query: Pricing is based on the amount of data scanned per query.

AWS Athena is an interactive data analytics service from Amazon Web Services (AWS) that allows you to run SQL queries directly on data stored in Amazon S3 without having to manage infrastructure. Athena provides a quick and easy way to analyze data in formats such as CSV, JSON, ORC, Parquet, and Avro without having to move data outside of S3.

not related to this question, but practically, using Redshift Spectrum would be an option here?

Amazon Athena helps you analyze unstructured, semi-structured, and structured data stored in Amazon S3. Examples include CSV, JSON, or columnar data formats such as Apache Parquet and Apache ORC. You can use Athena to run ad-hoc queries using ANSI SQL, without the need to aggregate or load the data into Athena. It uses Amazon QuickSight for data visualization AWS Glue Data Catalog allows you to create tables and query data in Athena based on a central metadata store available

To meet the requirements of analyzing log files stored in JSON format in an Amazon S3 bucket with minimal changes to the existing architecture and minimal operational overhead, the most suitable option would be Option C: Use Amazon Athena directly with Amazon S3 to run the queries as needed. Amazon Athena is a serverless interactive query service that allows you to analyze data directly from Amazon S3 using standard SQL queries. It eliminates the need for infrastructure provisioning or data loading, making it a low-overhead solution. Overall, Amazon Athena offers a straightforward and efficient solution for analyzing log files stored in JSON format, ensuring minimal operational overhead and compatibility with simple on-demand queries.

A. Despite this option can be valid, it implies a bit of operational overhead compared with other options. Additionally, there is no need to aggregate that change to the existing architecture because we are already working in S3, and using other storage services incurs unnecessary costs. B. To collect the logs, we use CloudTrail over CloudWatch. Running SQL queries from the Amazon CloudWatch console is not recommended for this use case, since it is more used for filtering. C. Correct answer. Athena integrates seamlessly with S3 and allows you to run simple SQL queries in no time. When working with Apache Spark or with SQL in S3, using this service is the best option. D. This option incurs elevated operational overhead. Glue is not used to catalog the logs. Analyzing logs with Spark on an EMR cluster is very common, but you can do it faster with the Athena service integrated with S3 directly.

C. Athena lets you analyse S3 data using standard SQL. No other steps needed

C for sure

Using Amazon Athena with Amazon S3 is direct and efficient way for querying JSON log files with minimum operational overhead.

A, B, D operational overhead. C accept all requirement

Option A is correct Amazon Athena is an interactive query service provided by Amazon Web Services (AWS) that enables you to analyze data stored in Amazon S3 (Simple Storage Service) using standard SQL queries.

Answer should be C, Simple approach, Store logs in S3 and use Athena to query. Redshift will be costly approach.Cloudwatch does not store any data. So A and B ruled out.