Q73Design High-Performing Architectures
A media company gathers and analyzes user activity data on premises and wants to move this capability to AWS. The user activity data store will keep growing and will be **petabytes** in size. The company must build a **highly available** data ingestion solution that supports **on-demand analytics** of both **existing data** and **new data** by using **SQL**. Which solution will meet these requirements with the **LEAST operational overhead**?
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Discussion · 22
B 13
Petabyte scale- Redshift
B 8
Data ingestion through Kinesis data streams will require manual intervention to provide more shards as data size grows. Kinesis firehose will ingest data with the least operational overhead.
B 7
This solution meets the requirements as follows:
• Kinesis Data Firehose can scale to ingest and process multiple terabytes per hour of streaming data. This can easily handle the petabyte-scale data volumes.
• Firehose can deliver the data to Redshift, a petabyte-scale data warehouse, enabling on-demand SQL analytics of the data.
• Redshift is a fully managed service, minimizing operational overhead. Firehose is also fully managed, handling scalability, availability, and durability of the streaming data ingestion.
6
1- Kinesis Data Stream provides a fully managed platform for custom data processing and analysis. Or we can say that used for custom data processing and analysis which required more manual intervention.
2- Kinesis Data Firehose simplifies the delivery of streaming data to various destinations without the need for complex transformations.
Option B is more suitable for the given scenario.
B 5
Petabyte scale- Redshift
B 3
always if you have a service that is meant for a specific job, it the correct answer, is logic. "A" is not good enough for this situation
B 3
Petabyte Scale sounds like Redshift!
B 3
B. Send activity data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Redshift cluster.
B 3
B: The answer is certainly option "B" because ingesting user activity data can easily be handled by Amazon Kinesis Data streams. The ingested data can then be sent into Redshift for Analytics.
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Amazon Redshift Serverless lets you access and analyze data without all of the configurations of a provisioned data warehouse.
https://docs.aws.amazon.com/redshift/latest/mgmt/welcome.html
B 3
1- Kinesis Data Stream provides a fully managed platform for custom data processing and analysis. Or we can say that used for custom data processing and analysis which required more manual intervention.
2- Kinesis Data Firehose simplifies the delivery of streaming data to various destinations without the need for complex transformations.
Option B is more suitable for the given scenario.
3
petabytes in size => redshift
B 3
Kinesis data stream cannot detined to s3
B 3
B provides a fully managed and scalable solution for data ingestion and analytics. KDF simplifies the data ingestion process by automatically scaling to handle large volumes of streaming data. It can directly load the data into an Redshift cluster, which is a powerful and fully managed data warehousing solution.
A. While Kinesis can handle streaming data, it requires additional processing to load the data into an analytics solution.
C. Although S3 and Lambda can handle the storage and processing of data, it requires more manual configuration and management compared to the fully managed solution offered by KDF and Redshift.
D. This option involves more operational overhead, as it requires managing and scaling the EC2 instances and RDS database infrastructure manually.
Therefore, option B with KDF delivering the data to Redshift cluster offers the most streamlined and operationally efficient solution for ingesting and analyzing the user activity data in the given scenario.
2
https://aws.amazon.com/streaming-data/ a good explanation of either option. firehose appears to be an option for Least operational overhead, as the streams product requires some building of apps etc.
2
It's A. Data Stream is better in this case, and you can query data in S3 with Athena
B 2
Option B is correct answer.
C 1
C. Place activity data in an Amazon S3 bucket. Configure Amazon S3 to run an AWS Lambda function on the data as the data arrives in the S3 bucket.
S3 + Lambda provides serverless ingestion, no servers to manage.
S3 can store petabytes of data cheaply.
Querying the data can be done with Amazon Athena, which allows SQL queries directly on S3 objects.
Fully managed → least operational overhead.
Supports highly available ingestion, as S3 is multi-AZ and Lambda scales automatically.
1
Answer A… key phrase’ least operational overhead’
KDF can write to S3 … https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
1
Copy-paste from A1975's answer
1
Data Stream Can't write to S3. That's why B is only left correct answer.
A 1
Why Option A works best:
Amazon Kinesis Data Streams provides a durable, scalable, and highly available ingestion layer.
Data is streamed to an Amazon S3 bucket, which:
Supports virtually unlimited storage
Is cost-effective for storing petabytes of data
Once in S3, you can use:
Amazon Athena to run on-demand SQL queries
Amazon Redshift Spectrum or Amazon EMR for advanced analytics
Fully managed, minimal maintenance, and scales automatically
Why the other options are less ideal:
B. Kinesis Data Firehose to Amazon Redshift
❌ Amazon Redshift is more expensive and suited for structured data with known schema
❌ Petabyte-scale data could require frequent manual scaling and maintenance
❌ Less cost-effective than S3 + Athena for large-scale, intermittent querying
1
Why this is the best solution:
The requirements are:
Highly available data ingestion, Supports petabyte-scale data, Enables on-demand analytics with SQL and Minimal operational overhead