A company wants to build a real-time analytics application that uses streaming data from social media. An ML engineer must implement a solution that ingests and transforms 5 GB of data each minute. Th

The correct answer is D. Use Amazon Kinesis Data Streams to ingest the social media data. Use Amazon Managed. Amazon Kinesis Data Streams is purpose-built for high-throughput, real-time data ingestion - it scales horizontally via shards to handle 5 GB/minute with sub-second latency, making it the only option here that fits both the volume and real-time requirements. The complete option D

Data Preparation for Machine Learning

Question

A company wants to build a real-time analytics application that uses streaming data from social media. An ML engineer must implement a solution that ingests and transforms 5 GB of data each minute. The solution also must load the data into a data store that supports fast queries for the real-time analytics. Which solution will meet these requirements?

Options

AUse Amazon EventBridge to ingest the social media data. Use AWS Glue to transform the data.
BUse Amazon Simple Queue Service (Amazon SQS) to ingest the social media data. Use AWS
CUse Amazon Simple Notification Service (Amazon SNS) to ingest the social media data. Use
DUse Amazon Kinesis Data Streams to ingest the social media data. Use Amazon Managed

How the community answered

(44 responses)

A
2% (1)
B
7% (3)
C
11% (5)
D
80% (35)

Explanation

Amazon Kinesis Data Streams is purpose-built for high-throughput, real-time data ingestion - it scales horizontally via shards to handle 5 GB/minute with sub-second latency, making it the only option here that fits both the volume and real-time requirements. The complete option D likely pairs it with Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) for in-flight transformation and loads into a fast query store like DynamoDB or Redshift - a proven real-time analytics stack on AWS.

Option A fails because EventBridge is an event router for service integration, not a high-throughput streaming ingestor, and AWS Glue is a batch ETL service - neither is suited for continuous 5 GB/min ingestion and real-time transformation.

Option B fails because SQS is a decoupling queue designed for discrete messages, not continuous high-volume data streams; it lacks the throughput guarantees and stream-processing integrations needed here.

Option C fails because SNS is a pub/sub notification service for fan-out delivery - it has no mechanism for stream processing, transformation, or loading data into an analytics store at this scale.

Memory tip: Associate Kinesis = Continuous streams (think kinetic energy - always in motion). If an exam question mentions real-time, high-throughput, or streaming data ingestion, Kinesis Data Streams is almost always the answer over SQS, SNS, or EventBridge, which are for discrete messages/events and routing respectively.

Topics

#Streaming Data Ingestion#Real-time Analytics#AWS Kinesis#Data Pipelines

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