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

The company needs to load data warehouse tables into Amazon S3 and perform incremental synchronization with daily updates. The most efficient solution is to use AWS Database Migration Service (AWS DMS) with a combination of full load and change data capture (CDC) to handle the initial load and daily incremental updates.

Option A: Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.DMS is designed to migrate databases to AWS, and the combination of full load plus CDC is ideal for handling incremental data changes efficiently. AWS Glue can then be used to append the incremental data to the full data set in S3. This solution is highly operationally efficient because it automates both the full load and incremental updates.

Options B, C, and D are less operationally efficient because they either require writing custom logic to handle bookmarks manually or involve unnecessary daily full loads.

Problem Analysis:

The company uses Kinesis Data Streams for both inventory management and reordering.

The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.

Duplicate records were observed in the inventory reordering system.

Key Considerations:

Kinesis streams are designed for durability but may produce duplicates under certain conditions.

Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.

Solution Analysis:

Option A: Network-Related Timeouts

If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.

Option B: High IteratorAgeMilliseconds

High iterator age suggests delays in processing but does not directly cause duplication.

Option C: Changes in Shards or Processors

Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.

Option D: AggregationEnabled Set to True

AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.

Option E: High max_records Value

A high max_records value increases batch size but does not lead to duplication.

Final Recommendation:

Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.

Amazon Kinesis Data Streams Best Practices

Kinesis Producer Library (KPL) Overview

Kinesis Client Library (KCL) Overview

For near real-time processing of new files in S3, event-driven ingestion is optimal. S3 Event Notifications can trigger AWS Lambda to immediately load data into Amazon Redshift, eliminating latency associated with batch scheduling.

“Event-based triggers using S3 notifications and Lambda functions are effective for near real-time ingestion pipelines into Amazon Redshift.”

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

Option C (Glue bookmarks) is best for batch jobs, and zero-ETL applies to Aurora to Redshift, not S3.