You are a machine learning specialist working for an oil and gas company.
Your company's oil and gas drilling sites around the world are equipped with sensors that stream site equipment status and external conditions like weather.You are responsible for building a machine learning model that predicts equipment failures at the sites.
The streaming data from the sites needs to be ingested, transformed and stored in Apache Parquet files for exploration and analysis before you use the data in your model. Which of the following options would ingest, transform, and store your data in the parquet format with the least amount of effort on your part?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: C.
Option A is incorrect.
While you could use Kinesis Data Streams to ingest your sensor data, you would have to write a Kinesis Client Library application or Lambda function to transform the sensor data to the parquet format.
This involves more work than using Kinesis Data Firehose.
Option B is incorrect.
You cannot stream data directly into Kinesis Data Analytics.
You would have to stream your sensor data into either Kinesis Data Streams or Kinesis Data Firehose first and then send your data downstream to Kinesis Data Analytics.
This involves more work than using Kinesis Data Firehose.
Option C is CORRECT.With Kinesis Data Firehose, you can stream your sensor data directly to Kinesis Data Firehose, use its built-in parquet transform, then write the parquet files to S3
This approach requires the least amount of work on your part.
Option D is incorrect.
While Kafka could be used to stream your sensor data and transform it, this option requires creating an MSK cluster, creating a client machine, creating a topic, and other effort-consuming tasks.
This involves more work than using Kinesis Data Firehose.
Reference:
Please see the Amazon Kinesis Data Streams developer guide titled What Is Amazon Kinesis Data Streams?.
Please refer to the Amazon Kinesis Data Firehose developer guide titled What Is Amazon Kinesis Data Firehose?.
Please refer to the Amazon Kinesis Data Analytics for SQL Applications developer guide titled What Is Amazon Kinesis Data Analytics for SQL Applications?
The most suitable service to ingest, transform, and store streaming data in the Apache Parquet format with the least amount of effort would be Amazon Kinesis Data Firehose (Option C).
Kinesis Data Firehose is a fully managed service that allows you to capture, transform, and load streaming data into AWS services, such as Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service. It is designed to simplify the process of loading streaming data into AWS by eliminating the need to build and maintain your own data processing infrastructure.
To ingest, transform, and store the data in Apache Parquet format, you can configure Kinesis Data Firehose to transform the incoming data using AWS Glue Data Catalog and Apache Spark. With these services, you can write transformations using Spark SQL and save the output in Apache Parquet format.
Kinesis Data Streams (Option A) is another AWS service that can be used to ingest and process streaming data. However, it requires you to build and manage your own data processing infrastructure, which can be complex and time-consuming.
Kinesis Data Analytics (Option B) is a service that allows you to analyze streaming data using SQL queries. It can ingest data from Kinesis Data Streams and Kinesis Data Firehose, but it does not provide data storage capabilities.
Managed Streaming for Apache Kafka (MSK) (Option D) is a fully managed service that allows you to run Apache Kafka clusters in AWS. While MSK can be used to ingest and process streaming data, it requires you to build and manage your own data processing infrastructure, which can be complex and time-consuming.
In summary, Kinesis Data Firehose is the most suitable option for ingesting, transforming, and storing streaming data in Apache Parquet format with the least amount of effort on your part.