Hadoop in cloud (EMR, Dataproc, HDInsight) - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Hadoop Cluster Setup on Cloud

Which cloud service among EMR, Dataproc, and HDInsight automatically handles Hadoop cluster provisioning and scaling with minimal manual configuration?

A Google Dataproc

B Amazon EMR

C Azure HDInsight

D All require manual cluster setup and scaling

💡 Hint

Think about which service is designed for quick cluster startup and auto-scaling with minimal user input.

Google Dataproc is designed to quickly create and scale Hadoop clusters with minimal configuration, automating provisioning and scaling better than EMR and HDInsight.

❓ Predict Output

intermediate

Output of Hadoop Job Status Command on EMR

What is the output of the following AWS CLI command when checking the status of a running Hadoop job on EMR?

aws emr describe-step --cluster-id j-2AXXXXXXGAPLF --step-id s-3XXXXXXXXX

Hadoop

aws emr describe-step --cluster-id j-2AXXXXXXGAPLF --step-id s-3XXXXXXXXX

A {"Step":{"Status":{"State":"RUNNING"}}}

B {"Step":{"Status":{"State":"FAILED"}}}

C {"Error":"Cluster or Step ID not found"}

D {"Step":{"Status":{"State":"COMPLETED"}}}

💡 Hint

Consider the command is run while the job is actively processing.

The command returns the JSON with the step status. If the job is running, the state is "RUNNING".

❓ data_output

advanced

Comparing Storage Options in Cloud Hadoop Services

Given a Hadoop job running on EMR, Dataproc, and HDInsight, which storage system is natively integrated and used by each service for storing input and output data?

A EMR uses HDFS, Dataproc uses HDFS, HDInsight uses HDFS only

B EMR uses Azure Blob Storage, Dataproc uses Amazon S3, HDInsight uses Google Cloud Storage

C EMR uses Amazon S3, Dataproc uses Google Cloud Storage, HDInsight uses Azure Blob Storage

D All use local disk storage only

💡 Hint

Think about the native cloud storage services each cloud provider offers.

Each cloud Hadoop service integrates with its cloud provider's native object storage: EMR with Amazon S3, Dataproc with Google Cloud Storage, and HDInsight with Azure Blob Storage.

🔧 Debug

advanced

Identifying Cause of Hadoop Job Failure on HDInsight

A Hadoop job submitted to Azure HDInsight fails immediately with the error message: "java.lang.ClassNotFoundException: org.apache.hadoop.fs.azure.NativeAzureFileSystem". What is the most likely cause?

A Network connectivity issues between nodes

B Incorrect Hadoop version installed on the cluster

C Insufficient cluster memory for the job

D Missing Azure Blob Storage connector JAR in the classpath

💡 Hint

ClassNotFoundException usually means a required library is missing.

The error indicates the Azure Blob Storage connector class is missing, so the job cannot access Azure storage without the proper JAR file.

🚀 Application

expert

Optimizing Hadoop Job Cost on Cloud Services

You want to minimize cost while running a large Hadoop job on cloud services. Which strategy is best to reduce cost without sacrificing job completion?

A Use only on-demand instances with maximum cluster size for speed

B Use spot/preemptible instances with checkpointing and automatic retries

C Run the job on a single large instance to avoid cluster overhead

D Disable autoscaling and keep cluster running 24/7

💡 Hint

Consider how cloud providers offer cheaper compute options that can be interrupted.

Using spot or preemptible instances reduces cost significantly. Checkpointing and retries handle interruptions, balancing cost and job completion.