Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 56 discussion

Option A, creating an AWS Glue Data Catalog with Glue Schema Registry and orchestrating data ingestion into Amazon Redshift Serverless using AWS Glue, appears to be the most cost-effective and suitable solution. It offers a serverless approach to manage the evolving data schema of the IoT data and efficiently supports data analytics needs without the overhead of managing a provisioned database cluster or complex orchestration setups.

Athena is not able to create new data catalog

Option C Cost-effectiveness: Amazon Athena allows you to query data directly from Amazon S3 without the need for any infrastructure setup or management. You pay only for the queries you run, making it cost-effective, especially for sporadic or exploratory analysis. Flexibility: Since the data structure can change with IoT device upgrades, using Athena allows for flexibility in querying and analyzing the data regardless of its structure. You don't need to define a fixed schema upfront, enabling you to adapt to changes seamlessly. Apache Spark Support: Athena supports querying data using Apache Spark, which is powerful for processing and analyzing large datasets. This capability ensures that the analytics department can leverage Spark for more advanced analytics if needed. https://www.youtube.com/watch?v=Q93NZJBFSWw

The correct solution is A. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless. Option C (Amazon Athena and Apache Spark) is suitable for ad-hoc querying and exploration but may not be the best choice for the analytics department's ongoing data analysis needs, as Athena is designed for interactive querying rather than complex data transformations.

The objective is to create a data catalog that includes the IoT data and AWS Glue Data Catalog is the best option for this requirement. https://docs.aws.amazon.com/glue/latest/dg/catalog-and-crawler.html C is incorrect. While Athena makes it easy to read from S3 using SQL, it does not crawl the data source and create a data catalog.

Why Option C is the Most Cost-Effective Serverless and Pay-as-you-go: Athena is a serverless query service, meaning you only pay for the queries the analytics department runs. No need to provision and manage always-running clusters. Flexible Schema Handling: Athena works well with semi-structured data like JSON and can handle schema evolution on the fly. This is perfect for the scenario where IoT data structures might change. Spark Integration: Integrating Apache Spark with Athena provides rich capabilities for data processing and transformation. Ease of Use for Analytics: Athena's familiar SQL-like interface and ability to directly query S3 data make it convenient for the analytics department.

Options A, B, and D involve setting up additional infrastructure (e.g., AWS Glue, Redshift clusters, Lambda functions) which may incur unnecessary costs and complexity for the given requirements. Option C, on the other hand, utilizes a serverless and scalable solution directly querying data in S3, making it the most cost-effective choice.