Questions and Answers
What is the primary advantage of using AWS Glue’s Python Shell for simple ETL tasks?
What is a characteristic of the high-memory DPU (M-DPU)?
In the context of AWS Glue, why is using Ray for small data processing generally considered inappropriate?
Which AWS Glue option is least cost-effective for processing small files under 30 MB?
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How does allocating 1/16th of a DPU benefit the data engineering team?
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What could be a disadvantage of using AWS Glue with Apache Spark for ETL tasks involving small files?
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Which of the following statements about vCPUs and memory configuration is true?
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Why might PySpark be considered a less effective option for small file processing?
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Which AWS Glue engine is best suited for complex ETL jobs that require processing large volumes of data?
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What is the primary advantage of using AWS Glue's Data Catalog?
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Which AWS Glue engine is optimized for highly parallel and compute-intensive tasks?
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What type of tasks is AWS Glue’s Python Shell primarily best suited for?
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How is AWS Glue priced for the use of its ETL jobs?
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Which aspect does AWS Glue NOT manage or charge for?
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What is the main benefit of using the visual interface provided by AWS Glue for ETL workflows?
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Which of the following statements about AWS Glue is true?
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Study Notes
AWS Glue Overview
- AWS Glue is a serverless data integration service facilitating data discovery, preparation, and combination.
- It accelerates analytics, machine learning, and application development by enabling faster data analysis—possible in minutes rather than months.
- Offers both visual and code-based interfaces for seamless data integration.
Data Catalog and ETL Workflows
- Features the AWS Glue Data Catalog for easy data discovery and access.
- Data engineers and ETL developers can visually create, run, and monitor ETL (Extract, Transform, Load) workflows.
Processing Engines in AWS Glue
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Spark:
- Designed for complex ETL jobs that process large volumes of data.
- Utilizes distributed computing for high-performance data transformation across computer clusters.
- Ideal for big data handling and extensive data manipulation.
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Ray:
- Optimized for highly parallel and compute-intensive tasks.
- Suitable for machine learning workflows and real-time data processing.
- Supports easy scaling from local environments to clusters.
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Python Shell:
- Best for light to medium data transformation tasks not requiring distributed computing.
- Allows simpler, script-based ETL jobs without the overhead of Spark or Ray environments.
Cost Structure
- Charges are based on the duration of ETL jobs with no resource management or upfront costs.
- AWS charges an hourly rate depending on the number of data processing units (DPUs) utilized.
- Standard DPU provides 4 vCPUs and 16 GB RAM; high-memory DPU (M-DPU) offers 4 vCPUs and 32 GB RAM.
Cost-Effective Data Processing
- For simple ETL tasks, such as processing small gzip files, the Python Shell option is highly cost-effective.
- Data engineering teams can allocate only 1/16th of a DPU for these tasks, minimizing costs while meeting processing needs.
Incorrect Options for Transformation
- Ray for Data Transformation: Inefficient for files under 30 MB; more suitable for large-scale parallel processing, increasing costs.
- Spark with PySpark: Not cost-effective for small files under 30 MB due to unnecessary overhead.
- Spark with Scala: Similar disadvantages as PySpark; excessive for small file processing and may raise costs unnecessarily.
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Description
Explore the key features of AWS Glue, a serverless data integration service designed to streamline data discovery, preparation, and combination. Learn about the Data Catalog, ETL workflows, and the processing engines like Spark and Ray that enhance data handling capabilities for analytics and machine learning.
