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

AWS Database Migration Service (AWS DMS) is a service that helps you migrate databases to AWS quickly and securely. You can use AWS DMS to migrate the Hive metastore from the on-premises Hadoop clusters into Amazon S3, which is a highly scalable, durable, and cost-effective object storage service. AWS Glue Data Catalog is a serverless, managed service that acts as a central metadata repository for your data assets. You can use AWS Glue Data Catalog to scan the Amazon S3 bucket that contains the migrated Hive metastore and create a data catalog that is compatible with Apache Hive and other AWS services. This solution meets the requirements of migrating the data catalog into a persistent storage solution and using a serverless solution. This solution is also the most cost-effective, as it does not incur any additional charges for running Amazon EMR or Amazon Aurora MySQL clusters. The other options are either not feasible or not optimal. Configuring a Hive metastore in Amazon EMR (option B) or an external Hive metastore in Amazon EMR (option C) would require running and maintaining Amazon EMR clusters, which would incur additional costs and complexity. Using Amazon Aurora MySQL to store the company’s data catalog (option C) would also incur additional costs and complexity, as well as introduce compatibility issues with Apache Hive. Configuring a new Hive metastore in Amazon EMR (option D) would not migrate the existing data catalog, but create a new one, which would result in data loss and inconsistency. References:

Using AWS Database Migration Service

Populating the AWS Glue Data Catalog

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.2: AWS Glue Data Catalog

A “No space left on device” error typically results from data skew or large shuffle stages. The best actions are:

B. Monitor using Spark UI and Glue metrics to find skewed partitions or executor issues.

D. Use --write-shuffle-files-to-s3 to offload intermediate data to S3 instead of local disk, and apply salting to reduce skew.

“You can reduce the impact of data skew and large shuffle operations by monitoring with Spark UI and enabling the --write-shuffle-files-to-s3 option. Salting can help rebalance the skewed keys.”

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

Scaling out workers (A, C) is more costly and less efficient if the root cause (skew) is not fixed.

For high-performance analytics in Athena and EMR Spark, the columnar storage formats Parquet and ORC deliver the shortest query times. These formats minimize I/O, enable predicate pushdown, and support efficient compression and parallel reads.

When integrated with the AWS Glue Data Catalog, schema evolution is automatically tracked and managed across queries.

This combination (Parquet/ORC + Glue Catalog) is explicitly recommended by AWS for analytical workloads on S3 data lakes.

“For analytical workloads using Amazon EMR and Athena, store data in columnar formats such as Parquet or ORC. These formats provide optimized compression and query performance.”

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

“Use the AWS Glue Data Catalog to manage table schemas and schema evolution for columnar data formats such as Parquet and ORC.”

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

Therefore, using ORC or Parquet along with the Glue Data Catalog achieves the shortest Athena query times and seamless schema management.