AWS Certified Data Analytics Specialty 2021 - Hands-On! - What is Glue? + Partitioning your Data Lake

The video tutorial covers AWS Glue, a serverless ETL service that plays a crucial role in AWS exams. It explains Glue's ability to automatically handle table definitions and schema discovery, serving as a central metadata repository. The tutorial highlights Glue's use of Apache Spark for ETL jobs and its integration with tools like Athena and Redshift. It also discusses the Glue Crawler and Data Catalog, which infer schemas from unstructured data in S3. Finally, it provides strategies for partitioning data in S3 to optimize query performance.

What is the primary role of AWS Glue in data management?

• To store large amounts of data

• To provide a serverless computing environment

• To serve as a central metadata repository

• To manage network security

Which tool does AWS Glue use for its ETL jobs?

• Apache Spark

• Kubernetes

• TensorFlow

• Hadoop

How does AWS Glue help in querying unstructured data?

• By duplicating data into a new database

• By converting unstructured data into structured data

• By providing a schema for unstructured data

• By compressing data for faster access

What is the purpose of the Glue Crawler?

• To delete unnecessary data

• To scan data and infer schemas

• To manage user access

• To encrypt data

Why is data partitioning important in S3?

• To reduce storage costs

• To improve data retrieval efficiency

• To simplify data backup

• To enhance data security

If you query data primarily by time, how should you partition your S3 data?

• By file size

• By year, month, and date

• By data type

• By device ID first

What should be the top-level partition if you query data primarily by device?

• Date

• Year

• Month

• Device ID