AWS Certified Data Analytics Specialty 2021 - Hands-On! - [Exercise] Amazon Web Services (AWS) Glue and Athena

The video tutorial explains how to use Amazon Athena with AWS Glue to query data stored in an S3 data lake. It covers setting up a Glue crawler to infer a schema, creating a database, and partitioning data. The tutorial demonstrates querying the data using Athena, highlighting the ease of treating S3 data as a SQL database. It also discusses potential issues with data types and suggests using ETL jobs for data transformation.

What is the primary purpose of using AWS Glue with Amazon Athena?

• To infer a schema from data in S3

• To encrypt data in S3

• To store data in S3

• To create a new database

What is the first step in setting up a crawler in AWS Glue?

• Running a query in Athena

• Configuring security settings

• Adding a crawler

• Creating a new database

Why is it important to exclude certain directories when setting up a crawler?

• To avoid including irrelevant data in the schema

• To enhance security

• To speed up the crawling process

• To save storage space

What should you do if the inferred data types in Glue are not suitable for your needs?

• Delete the schema and start over

• Ignore the data types

• Create an ETL job to transform the data

• Change them directly in the schema

What is a key benefit of using partitions in your data stored in S3?

• They allow for automatic schema updates

• They reduce storage costs

• They improve query performance

• They simplify data encryption

How does Athena interact with AWS Glue to facilitate querying?

• Athena imports data directly from S3

• Athena uses Glue's schema to query data as if it were a database

• Athena requires manual server setup

• Athena does not interact with Glue

What is demonstrated in the practical example using Athena?

• Counting items ordered from a specific country and month

• Creating a new IAM role

• Setting up a new S3 bucket

• Encrypting data in S3