Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 27 discussion

Key word here is near real-time. If it's involve S3 and COPY, it's not gonna be near real-time

The answer is C. It can provide near real-time insight analysis. Refer the article from AWS - https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/

D could be the most standard way to handle this case. How to use C to implement it is questionable for me.

Amazon Redshift can automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have the autorefresh option. Amazon Redshift autorefreshes materialized views as soon as possible after base tables changes. https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html

Firehose is Near-Real time, you can set your buffer size and stream to either Redshift or S3 directly. Since Redshift is not in the option, use s3...

Kinesis Data Streams , option D using Kinesis Data Firehose is a fully managed service that automatically handles the ingestion of data

Here’s why D is the best choice: Kinesis Data Firehose is a fully managed service that automatically handles the ingestion of data from Kinesis Data Streams and stages it in S3, which significantly reduces operational overhead compared to managing custom data ingestion pipelines. S3 as a staging area: Using Amazon S3 as a staging location allows for flexible data management, high durability, and direct loading into Redshift without needing to manage complex buffering or data handling processes. COPY command: The COPY command in Amazon Redshift is highly optimized for loading large datasets efficiently, making it a common and effective method to load bulk data from S3 into Redshift for near real-time analysis. Firehose to Redshift: Firehose can automatically buffer, batch, and transform data before loading it into Redshift, reducing manual intervention and ensuring data is readily available for real-time analytics.

Option C has an issue: Redshift does not natively support direct querying or mapping of Kinesis Data Streams. D is the only correct option.

Option D

Option A (using Kinesis Data Streams to stage data in Amazon S3 and loading it directly into Amazon Redshift) is the most straightforward and efficient approach. It minimizes operational overhead and ensures immediate availability of data for analysis. Options B and C introduce additional complexity and may not provide the same level of efficiency

MVs in Redshift with auto refresh is the best option for near real time.

Using materialized views with auto-refresh directly on a Redshift external schema of Kinesis Data Stream offers the most streamlined and efficient approach for near real-time insights using existing BI tools.

https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion-getting-started.html C is correct. (KDS -> Redshift) D is wrong as it has more operational overhead (KDS -> KDF -> S3 -> Redshift)

1. Amazon Kinesis Data Firehose: It's designed to reliably load streaming data into data lakes and data stores with minimal configuration and management overhead. It handles tasks like buffering, scaling, and delivering data to destinations like Amazon S3 and Amazon Redshift automatically. 2. Amazon S3 as a staging area: Storing data in Amazon S3 provides a scalable and durable solution for data storage without needing to manage infrastructure. It also allows for easy integration with other AWS services and existing BI and analytics tools. 3. Amazon Redshift: While Redshift requires some setup and management, loading data from Amazon S3 using the COPY command is a straightforward process. Once data is loaded into Redshift, existing BI and analytics tools can query the data directly, enabling near real-time insights. 4. Minimal operational overhead: This solution minimizes operational overhead because much of the management tasks, such as scaling, buffering, and delivery of data, are handled by Amazon Kinesis Data Firehose. Additionally, using Amazon S3 as a staging area simplifies data storage and integration with other services.

By considering the characteristics and capabilities of each AWS service and approach, along with insights from AWS documentation, it becomes evident that option D offers the most streamlined and operationally efficient solution for the scenario described. This idea/concept is also straight out of the Amazon Solutions Architect course material.

Point: "Which solution will meet these requirements with the LEAST operational overhead?" C. - This approach involves creating an external schema in Amazon Redshift to map data from Kinesis Data Streams, which adds complexity compared to directly loading data from Amazon S3 using Amazon Kinesis Data Firehose. - While materialized views with auto-refresh can provide near real-time insights, managing them and ensuring proper synchronization with the streaming data source may require more operational effort. - AWS documentation for Amazon Redshift primarily focuses on traditional data loading methods and querying, with limited guidance on integrating with real-time data sources like Kinesis Data Streams.

I think D. It could be C but because of "LEAST operational overhead" I will go with D.