AWS Certified Solutions Architect - Associate
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A media company collects and analyzes user activity data on premises. The company wants to migrate this capability to AWS. The user activity data store will continue to grow and will be petabytes in size. The company needs to build a highly available data ingestion solution that facilitates on-demand analytics of existing data and new data with SQL.
Which solution will meet these requirements with the LEAST operational overhead?
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A
Send activity data to an Amazon Kinesis data stream. Configure the stream to deliver the data to an Amazon S3 bucket.
B
Send activity data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Redshift cluster.
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.
D
Create an ingestion service on Amazon EC2 instances that are spread across multiple Availability Zones. Configure the service to forward data to an Amazon RDS Multi-AZ database.
Explanation:
Explanation
Correct Answer: B - Send activity data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Redshift cluster.
Why this is correct:
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Petabyte-scale data handling: Amazon Redshift is designed for petabyte-scale data warehousing and analytics, making it suitable for the growing data store requirement.
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SQL-based analytics: Amazon Redshift provides SQL-based querying capabilities, which directly addresses the requirement for "on-demand analytics of existing data and new data with SQL."
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Least operational overhead: Kinesis Data Firehose is a fully managed service that automatically scales to handle data throughput and delivers data to destinations like Amazon Redshift, S3, or Elasticsearch with minimal operational overhead.
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High availability: Both Kinesis Data Firehose and Amazon Redshift are managed services that provide built-in high availability and durability.
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Real-time data ingestion: Kinesis Data Firehose can ingest streaming data in real-time and batch it for delivery to Redshift, supporting both existing and new data analytics.
Analysis of other options:
A. Send activity data to an Amazon Kinesis data stream. Configure the stream to deliver the data to an Amazon S3 bucket.
- While Kinesis Data Streams can handle streaming data, storing data in S3 alone doesn't provide SQL-based analytics capabilities without additional services like Amazon Athena or Redshift Spectrum.
- Requires more operational overhead to set up analytics on S3 data.
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 is good for storage but doesn't provide SQL analytics natively.
- Lambda functions would need to process and transform data, adding complexity and operational overhead.
- Doesn't directly support SQL-based analytics on petabytes of data.
D. Create an ingestion service on Amazon EC2 instances that are spread across multiple Availability Zones. Configure the service to forward data to an Amazon RDS Multi-AZ database.
- RDS is not designed for petabyte-scale analytics; it's optimized for transactional workloads.
- Managing EC2 instances for ingestion adds significant operational overhead.
- RDS doesn't scale well to petabytes and is expensive for analytics workloads.
Key AWS Services Understanding:
- Amazon Kinesis Data Firehose: Fully managed service for loading streaming data into data stores and analytics tools.
- Amazon Redshift: Petabyte-scale data warehouse service with SQL-based analytics.
- Amazon S3: Object storage service, good for data lakes but requires additional services for SQL analytics.
- Amazon RDS: Relational database service for transactional workloads, not analytics at petabyte scale.
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