Module 1: Introduction to Data Lakes
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In this post, I provide complete, accurate, and detailed explanations for the answers to Module 1: Introduction to Data Lakes of Course 3: Building Data Lakes on AWS
Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question!
Knowledge Check
Graded Assignment
1. Which operation describes the function of a data lake as a centralized repository?
- Store unstructured data from a single data source
- Store structured data from any data source
- Store structured and unstructured data from any source ✅
- Store structured and unstructured data from a single source
Explanation:
A data lake stores both structured and unstructured data from multiple sources in one central repository.
2. What is the most cost-effective storage option for your data lake?
- Amazon Elastic Block Store (Amazon EBS)
- Amazon S3 ✅
- Amazon RDS
- Amazon Redshift
Explanation:
Amazon S3 is highly scalable, durable, and cost-effective for storing large volumes of data in a data lake.
3. Which services are used in the processing layer of a data lake architecture? (Select TWO.)
- AWS Snowball
- AWS Glue ✅
- Amazon EMR ✅
- Amazon QuickSight
- Amazon Athena
Explanation:
AWS Glue is a serverless ETL service for data preparation, and Amazon EMR is used for large-scale data processing using frameworks like Spark and Hadoop.
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