Loading Data from Your Hadoop Cluster into SageMaker
Question
You work for a financial services company where you have a large Hadoop cluster hosting a data lake in your on-premises data center.
Your department has loaded your data lake with financial services operational data from your corporate actions, order management, cash management, reconciliations, and trade management systems.
Your investment management operations team now wants to use data from the data lake to build financial prediction models.
You want to use data from the Hadoop cluster in your machine learning training jobs.
Your Hadoop cluster has Hive, Spark, Sqoop, and Flume installed. How can you most effectively load data from your Hadoop cluster into your SageMaker model for training?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: C.
Option A is incorrect.
The Hadoop distcp utility is used for inter/intra cluster data movement.
It is not an efficient method to get data into your SageMaker training instance.
(See the Apache Hadoop distcp guide)
Option B is incorrect.
The HadoopActivity command is used to run a job on a cluster.
You would have to write the job to extract and load the data onto S3
This would not be the most efficient method of the options listed.
(See AWS Data Pipeline developer guide titled HadoopActivity)
Option C is correct.
The SageMaker Spark library makes it; so you can easily train models using data frames in your Spark clusters.
This is the most efficient method of the options listed.
(See the Amazon SageMaker developer guide titled Use Apache Spark with Amazon SageMaker)
Option D is incorrect.
The Sqoop export command is used for exporting files from HDFS to an RDBMS.
This would not help you load your data into your SageMaker training instance.
(See the Sqoop User Guide)
Reference:
Please see the Amazon SageMaker developer guide titled Use Machine Learning Frameworks with Amazon SageMaker.
The most effective way to load data from the Hadoop cluster into the SageMaker model for training would be to use the distcp utility to copy the dataset from the Hadoop platform to the S3 bucket where the SageMaker training job can use it. Option A is the correct answer.
Distcp is a distributed data transfer tool that is optimized for transferring large datasets between Hadoop clusters. Distcp can be used to copy data between two Hadoop clusters, or between a Hadoop cluster and an S3 bucket. Since the data is already in the Hadoop cluster, using distcp to copy the data to an S3 bucket is a straightforward and efficient option.
Option B suggests using the HadoopActivity command with AWS Data Pipeline to move the dataset from the Hadoop platform to the S3 bucket. However, AWS Data Pipeline is a service used to orchestrate data movement and processing activities across AWS services and on-premises data sources. It is not optimized for moving large datasets and may not be as efficient as using distcp.
Option C suggests using the SageMaker Spark library to train the model. While this is a valid option, it requires additional configuration and setup to integrate the Spark cluster with SageMaker. If the data is already in the Hadoop cluster, it would be more efficient to use distcp to copy the data to an S3 bucket and then use SageMaker to train the model.
Option D suggests using the Sqoop export command to export the dataset from the Hadoop cluster to the S3 bucket. Sqoop is a tool used to transfer data between Hadoop and relational databases. While Sqoop can export data to S3, it may not be as efficient as using distcp since Sqoop is optimized for transferring data between Hadoop and relational databases.
In summary, the most effective way to load data from the Hadoop cluster into the SageMaker model for training would be to use the distcp utility to copy the dataset from the Hadoop platform to the S3 bucket where the SageMaker training job can use it.
