Migration from Hadoop to cloud-native - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to list files in a Hadoop directory.

Hadoop

hadoop fs -ls [1]

Drag options to blanks, or click blank then click option'

A/data/input

B/user/hadoop

C/tmp

D/var/log

Attempts:

3 left

💡 Hint

Common Mistakes

Using a directory path that does not exist.

Forgetting the leading slash in the path.

The command hadoop fs -ls /data/input lists files in the input directory.

2fill in blank

medium

Complete the code to copy a file from Hadoop to local filesystem.

Hadoop

hadoop fs -[1] /data/input/file.txt ./

Drag options to blanks, or click blank then click option'

Amove

Bput

Cget

DcopyFromLocal

Attempts:

3 left

💡 Hint

Common Mistakes

Using put which uploads files instead.

Using copyFromLocal which copies local to Hadoop.

The get command copies files from Hadoop to local.

3fill in blank

hard

Fix the error in the command to remove a Hadoop directory recursively.

Hadoop

hadoop fs -rm -[1] /data/old_data

Drag options to blanks, or click blank then click option'

AR

Bf

Crf

Dr

Attempts:

3 left

💡 Hint

Common Mistakes

Using -rf which is a Unix command, not Hadoop fs.

Using -f without recursion.

The -r flag is used to remove directories recursively in Hadoop.

4fill in blank

hard

Fill both blanks to create a cloud-native command to copy data from Hadoop to AWS S3.

Hadoop

hadoop distcp [1] [2]

Drag options to blanks, or click blank then click option'

Ahdfs://namenode:8020/data/input

Bs3a://mybucket/data/input

Cs3n://mybucket/data/input

Dhdfs://namenode:9000/data/input

Attempts:

3 left

💡 Hint

Common Mistakes

Using s3n:// which is deprecated.

Mixing up source and destination paths.

The distcp command copies data from Hadoop HDFS to AWS S3 using the hdfs:// and s3a:// paths.

5fill in blank

hard

Fill all three blanks to write a Spark command to read data from cloud storage and show the first 5 rows.

Hadoop

spark.read.format([1]).load([2]).[3]

Drag options to blanks, or click blank then click option'

A"parquet"

B"s3a://mybucket/data/input"

Cshow(5)

Dshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using show without parentheses or row count.

Using incorrect format strings like "csv" instead of "parquet".

This Spark code reads parquet files from S3 and shows the first 5 rows using show(5).

Practice

1. What is the main benefit of migrating Hadoop workloads to cloud-native platforms?

easy

A. Better scalability and easier management

B. Reduced data storage capacity

C. Elimination of all data processing

D. Increased hardware maintenance

Solution

1. Step 1: Understand Hadoop migration goals

   Migration aims to improve how Hadoop workloads scale and are managed.

2. Step 2: Identify benefits of cloud-native platforms

   Cloud platforms offer flexible scaling and easier cluster management compared to on-premises Hadoop.

3. Final Answer:

   Better scalability and easier management -> Option A

4. Quick Check:

   Cloud migration = scalability + management [OK]

Hint: Think why cloud is popular for big data [OK]

Common Mistakes:

• Confusing storage reduction with scalability
• Assuming migration removes data processing
• Thinking hardware maintenance increases

2. Which of the following is the correct syntax to start a Hadoop job on AWS EMR using the AWS CLI?

easy

A. aws emr run-job --id j-2AXXXXXXGAPLF --type Spark --file myapp.jar

B. aws emr start-job --cluster j-2AXXXXXXGAPLF --job Spark --args s3://mybucket/myapp.jar

C. aws emr add-steps --cluster-id j-2AXXXXXXGAPLF --steps Type=Spark,Name="Spark Program",ActionOnFailure=CONTINUE,Args=[--class,org.apache.spark.examples.SparkPi,s3://mybucket/myapp.jar,10]

D. aws emr submit-step --cluster j-2AXXXXXXGAPLF --step Spark --jar myapp.jar

Solution

1. Step 1: Review AWS EMR CLI syntax for adding steps

   The correct command uses aws emr add-steps with detailed step parameters.

2. Step 2: Match syntax with options

   aws emr add-steps --cluster-id j-2AXXXXXXGAPLF --steps Type=Spark,Name="Spark Program",ActionOnFailure=CONTINUE,Args=[--class,org.apache.spark.examples.SparkPi,s3://mybucket/myapp.jar,10] matches the official syntax with cluster ID, step type, name, failure action, and arguments.

3. Final Answer:

   aws emr add-steps --cluster-id j-2AXXXXXXGAPLF --steps Type=Spark,Name="Spark Program",ActionOnFailure=CONTINUE,Args=[--class,org.apache.spark.examples.SparkPi,s3://mybucket/myapp.jar,10] -> Option C

4. Quick Check:

   Use add-steps with detailed args [OK]

Hint: Remember AWS EMR uses add-steps command [OK]

Common Mistakes:

• Using non-existent commands like start-job
• Omitting required step parameters
• Incorrect option names or flags

3. Given the following Python snippet using Google Cloud Dataproc API to submit a Hadoop job, what will be the output if the job completes successfully?

from google.cloud import dataproc_v1
client = dataproc_v1.JobControllerClient()
job = {
  "placement": {"cluster_name": "my-cluster"},
  "hadoop_job": {"main_jar_file_uri": "gs://my-bucket/my-job.jar"}
}
response = client.submit_job(project_id="my-project", region="us-central1", job=job)
print(response.status.state)

medium

A. JobStatus.State.DONE

B. JobStatus.State.ERROR

C. JobStatus.State.RUNNING

D. JobStatus.State.PENDING

Solution

1. Step 1: Understand the job submission and response

   The submit_job method returns a job response with a status field indicating job state.

2. Step 2: Interpret the status.state value after submission

   The submit_job returns immediately with initial state JobStatus.State.PENDING. The print occurs right after submission, before completion, even if the job succeeds later.

3. Final Answer:

   JobStatus.State.PENDING -> Option D

4. Quick Check:

   submit_job initial state = PENDING [OK]

Hint: submit_job returns PENDING immediately [OK]

Common Mistakes:

• Thinking the code waits for DONE on completion
• Confusing initial PENDING with final state
• Assuming RUNNING or ERROR on submission

4. You tried migrating a Hadoop job to AWS EMR but got an error: AccessDeniedException. What is the most likely cause?

medium

A. The EMR cluster is not running

B. The IAM role lacks permissions to access S3 bucket

C. The Hadoop job jar file is corrupted

D. The AWS CLI version is outdated

Solution

1. Step 1: Analyze the error type AccessDeniedException

   This error usually means permission issues, especially with AWS IAM roles.

2. Step 2: Identify common permission problems in EMR migration

   AccessDeniedException often occurs if the EMR cluster's IAM role cannot read the S3 bucket containing job files.

3. Final Answer:

   The IAM role lacks permissions to access S3 bucket -> Option B

4. Quick Check:

   AccessDenied = permission issue [OK]

Hint: AccessDenied usually means permission problem [OK]

Common Mistakes:

• Blaming corrupted jar without checking permissions
• Assuming cluster state causes access errors
• Ignoring IAM role permissions

5. You want to migrate a large Hadoop dataset to a cloud-native platform but want to minimize downtime. Which approach is best?

hard

A. Use incremental data replication with tools like DistCp to sync data continuously

B. Copy all data once during a maintenance window and stop all jobs

C. Manually upload data files via FTP to cloud storage

D. Run Hadoop jobs locally without migration

Solution

1. Step 1: Understand downtime minimization in migration

   Minimizing downtime requires continuous syncing rather than one-time full copy.

2. Step 2: Identify best tool for incremental replication

   DistCp supports incremental copying of Hadoop data to cloud storage, enabling near-zero downtime migration.

3. Final Answer:

   Use incremental data replication with tools like DistCp to sync data continuously -> Option A

4. Quick Check:

   Incremental sync = less downtime [OK]

Hint: Incremental sync tools reduce downtime [OK]

Common Mistakes:

• Doing one-time full copy causing long downtime
• Using manual FTP uploads for big data
• Avoiding migration by running locally