Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.

“RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.

In AWS Glue’s SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.

The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.

“A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

 AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don’t have to manage any infrastructure.

AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.

AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.

Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:

AWS Glue Workflows

AWS Step Functions

AWS Glue Studio

Amazon MWAA

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide