A company captures clickstream data from multiple websites and analyzes it using batch processing. The data is loaded nightly into Amazon Redshift and is consumed by business analysts. The company wants to move towards near-real-time data processing for timely insights. The solution should process the streaming data with minimal effort and operational overhead.
Which combination of AWS services are MOST cost-effective for this solution? (Choose two.)

A. Amazon EC2

B. AWS Lambda

C. Amazon Kinesis Data Streams

D. Amazon Kinesis Data Firehose

E. Amazon Kinesis Data Analytics






 

Suggested Answer: BD

Community Answer: DE

Kinesis Data Streams and Kinesis Client Library (KCL) ג€" Data from the data source can be continuously captured and streamed in near real-time using Kinesis
Data Streams. With the Kinesis Client Library (KCL), you can build your own application that can preprocess the streaming data as they arrive and emit the data for generating incremental views and downstream analysis. Kinesis Data Analytics ג€" This service provides the easiest way to process the data that is streaming through Kinesis Data Stream or Kinesis Data Firehose using SQL. This enables customers to gain actionable insight in near real-time from the incremental stream before storing it in Amazon S3.
 
Reference: alt="Reference Image" />
Reference:
https://d1.awsstatic.com/whitepapers/lambda-architecure-on-for-batch-aws.pdf


This question is in SAA-C02 AWS Certified Solutions Architect – Associate Exam
For getting AWS Certified Solutions Architect – Associate Certificate




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