Questions and Answers
What characterizes AWS Glue Flex in terms of compute resources?
Which AWS Glue execution option is specifically designed for active development and testing of ETL scripts?
Which statement is true regarding AWS Glue Standard compared to AWS Glue Flex?
What is a primary disadvantage of using AWS Glue Flex for data integration workloads?
Signup and view all the answers
Which AWS Glue service is NOT designed for non-urgent workloads requiring flexibility in start and runtimes?
Signup and view all the answers
Study Notes
AWS Glue Flex Overview
- Designed for non-urgent data integration workloads
- Does not require fast job startup times or consistent runtimes
- Operates on spare compute capacity rather than dedicated hardware
Cost-Effectiveness
- More cost-effective for workloads that can tolerate delays
- Variable start and runtimes due to reliance on available spare resources
- Resources can be reclaimed during job execution, affecting runtimes
Job Capabilities
- Offers access to custom connectors
- Includes a visual authoring interface for job design
- Provides job scheduling features
- Supports Glue Auto Scaling for dynamic resource management
Execution Class
- Available specifically for AWS Glue 3.0 Spark jobs
- Different from other execution options due to its flexible resource allocation
Comparison with Other AWS Glue Services
- AWS Glue Standard:
- Designed for time-sensitive tasks
- Requires fast startup and consistent runtimes through dedicated resource allocation
- More expensive and less flexible
- AWS Glue Development Endpoint:
- Specifically for active development and testing of ETL scripts
- Not suitable for production workloads
- AWS Glue Streaming ETL:
- Designed for real-time processing of streaming ETL jobs
- Not intended for non-urgent workloads with variable runtimes
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
This quiz covers the key concepts of AWS Glue Flex, focusing on its use for non-urgent data integration workloads. Learn about its operational characteristics, including the use of spare compute capacity and its cost-effectiveness. Understand the trade-offs regarding job startup times and resource availability.
