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

Apache Iceberg query performance depends heavily on metadata optimization and file layout efficiency. Generating column-level statistics allows query engines such as AWS Glue, Amazon Athena, and Amazon Redshift to perform predicate pushdown and data skipping. These statistics help avoid scanning unnecessary Parquet files and significantly reduce query execution time.

Real-time streaming workloads often generate many small files, which degrades query performance. Table compaction consolidates small Parquet files into fewer, larger files, improving scan efficiency and reducing metadata overhead. Iceberg supports compaction as a core optimization technique, and Glue can schedule these operations automatically.

Iceberg does not use traditional indexes, so index optimization is not applicable. Copy-on-write affects write semantics and consistency, not query speed. Views do not improve physical query performance.

Therefore, generating statistics and compacting files are the two most effective optimizations.

Aurora zero-ETL integration with Amazon Redshift Serverless is specifically designed to provide near real-time analytics on Aurora transactional data without the need to build or manage data pipelines. This integration continuously replicates data changes from Aurora MySQL into Redshift Serverless automatically.

The solution is fully serverless, requires no infrastructure provisioning, and eliminates the need for schema conversion, replication tasks, or streaming frameworks. Data is kept in sync with low latency, making it immediately available for analytical queries in Redshift Serverless.

AWS DMS-based solutions require task configuration, monitoring, and maintenance. Amazon MSK with Debezium introduces significant operational complexity and is not serverless. Kinesis-based replication pipelines require additional services and ongoing operational management.

Aurora zero-ETL integration is the lowest-maintenance, highest-efficiency solution and is explicitly recommended by AWS for this use case.

AWS Glue DataBrew is purpose-built for data profiling and data preparation with minimal operational overhead. A DataBrew profile job automatically analyzes datasets stored in Amazon S3 and generates detailed statistics such as column completeness, distinct values, data types, min/max values, and potential data quality issues. This is exactly what is required before data preparation steps.

The dataset size (100 MB) and Parquet format are fully supported by DataBrew, and no infrastructure provisioning, cluster management, or SQL development is required. The profiling results are produced automatically and can be reviewed directly in the AWS Management Console, making this the most efficient and lowest-effort solution.

Amazon Managed Service for Apache Flink is designed for real-time streaming analytics and introduces unnecessary complexity. Amazon Redshift Spectrum requires schema definition, SQL development, and additional setup, which is excessive for simple profiling. Amazon QuickSight is a visualization and BI service, not a data profiling tool.

Therefore, creating a DataBrew profile job is the most operationally efficient and exam-aligned solution.