AWS Certified Solutions Architect – ProfessionalDesign for New SolutionsMedium
A global financial services company is developing a new real-time fraud detection system. The system needs to ingest high volumes of transactional data from various sources, perform complex analytics to identify anomalies, and respond with low latency to prevent fraudulent activities. The solution must be highly available, scalable, and cost-effective. Which AWS service combination would best meet these requirements?
- AAmazon S3, AWS Glue, Amazon Redshift
- BAmazon SQS, AWS Lambda, Amazon RDS for PostgreSQL
- CAmazon Kinesis Data Streams, Amazon Kinesis Data Analytics, Amazon DynamoDB
- DAWS Batch, Amazon EMR, Amazon Aurora
Show answer & explanationAnswer & explanation
Correct answer: C. Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, Amazon DynamoDB
This combination provides real-time ingestion with Kinesis Data Streams, real-time processing and analytics with Kinesis Data Analytics, and low-latency, highly available storage for fraud decisions with DynamoDB.
Why the other options are wrong
- A. S3, Glue, and Redshift are suitable for batch processing and analytical data warehousing, not for real-time, low-latency fraud detection.
- B. SQS and Lambda can handle ingestion and processing, but RDS is not optimized for low-latency writes and reads at scale required for real-time fraud decisions.
- D. AWS Batch and Amazon EMR are primarily for batch processing and big data analytics, which do not meet the low-latency, real-time requirements of a fraud detection system.
Real-time Fraud Detection on AWS
An architecture leveraging AWS services for ingesting, processing, and analyzing high-volume transactional data in real-time to identify and prevent fraudulent activities with low latency.
- Requires real-time data ingestion and processing.
- Needs low-latency decision storage and retrieval.
- Must be highly scalable and available.
Memory trick: Fraudsters Flee Fast, Kinesis Keeps Kicking.