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 for data lakes. The tutorial highlights Glue's integration with Apache Spark for ETL jobs, the functionality of the Glue Crawler and Data Catalog, and strategies for efficient data partitioning in S3. The importance of organizing unstructured data for optimal performance is emphasized, with examples of partitioning by time or device.

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

• To provide a serverless computing environment

• To store large amounts of data

• To serve as a central metadata repository for data lakes

• To manage network security

Which tool does AWS Glue use for distributed data processing?

• Kubernetes

• Apache Spark

• Hadoop

• TensorFlow

What is the function of the Glue crawler?

• To encrypt data in S3

• To delete outdated data

• To scan data in S3 and infer schemas

• To move data from one S3 bucket to another

How does AWS Glue make unstructured data available for analysis?

• By converting it into a CSV file

• By inferring a schema and using a data catalog

• By compressing it into a zip file

• By copying it into a database

Why is data partitioning important in S3 when using AWS Glue?

• It reduces storage costs

• It enhances query efficiency

• It simplifies data backup

• It improves data security

If you are querying data primarily by time, how should you organize your S3 buckets?

• By region, then device and date

• By year, then month, date, and device

• By device ID, then year, month, and date

• By data type, then year and month

What should be the primary partition if you frequently query by device?

• Month

• Year

• Date

• Device ID