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

Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.

Amazon Athena Federated Queries:

Athena’s federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena’s support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.

The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.

The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the query will return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:

Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors)

When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)

Problem Analysis:

The company needs to migrate both an application and an on-premises Apache Kafka server to AWS.

Incremental updates from an on-premises Oracle database are processed by Kafka.

The solution must follow a replatform migration strategy, prioritizing minimal changes and low management overhead.

Key Considerations:

Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.

The solution must provide a managed Kafka service to minimize operational burden.

Low overhead solutions like serverless services are preferred.

Solution Analysis:

Option A: Kinesis Data Streams

Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.

This option would require significant application refactoring, which does not align with the replatform strategy.

Option B: MSK Provisioned Cluster

Managed Kafka service with fully configurable clusters.

Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.

Option C: Amazon Kinesis Data Firehose

Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.

Not suitable for Kafka-based applications.

Option D: MSK Serverless

MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.

Automatically scales based on workload, reducing operational overhead.

Ideal for replatform migrations, as it requires minimal changes to the application.

Final Recommendation:

Amazon MSK Serverless is the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.

Amazon MSK Serverless Overview

Comparison of Amazon MSK and Kinesis