You work as a machine learning specialist for a research department at a large university. Your team of machine learning specialists is responsible for all aspects of the machine learning lifecycle, including creating the data repositories used by your research scientists for their data science work. Your team has built a SageMaker infrastructure for your data scientists where you stream in data from many sources, such as satellite feeds, IoT devices like underwater sensors, and many others. You have recently implemented SageMaker Feature Store, and you are now implementing the ingestion of batch data from your streaming data sources. Which of the following options are viable approaches to streaming data into your SageMaker Feature Store? (Select TWO)

Answer options:

A.Stream your data sources through Apache Kafka into Feature Store.
B.Stream your data sources through Kinesis Data Analytics and a Lambda function into Feature Store.
C.Stream your data sources through Apache Spark Streaming into Feature Store.
D.Stream your data sources through Apache Spark ML Serving into Feature Store.
E.Stream your data sources through Apache Flink into Feature Store.

Correct Answers: A and B
Option A is correct. Apache Kafka can be used as a streaming data source where features are directly fed to the online feature store for feature creation.
Option B is correct. Kinesis Data Analytics, together with the use of a Lambda function, can be used as a streaming data source where features are directly fed to the online feature store for feature creation.
Option C is incorrect. Apache Spark Streaming is not supported as a direct streaming feed into SageMaker Feature Store.
Option D is incorrect. Apache Spark ML Serving is not supported as a direct streaming feed into SageMaker Feature Store.
Option E is incorrect. Apache Flink is not supported as a direct streaming feed into SageMaker Feature Store.
References:
Please see the Amazon SageMaker developer guide titled Data Sources and Ingestion (https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-ingest-data.html),
The AWS Machine Learning blog titled Using streaming ingestion with Amazon SageMaker Feature Store to make ML-backed decisions in near-real time (https://aws.amazon.com/blogs/machine-learning/using-streaming-ingestion-with-amazon-sagemaker-feature-store-to-make-ml-backed-decisions-in-near-real-time/)