Migration from Hadoop to cloud-native - Mini Project: Build & Apply

Migration from Hadoop to Cloud-Native Data Processing

📖 Scenario: You work in a company that currently uses Hadoop to process large data sets. The company wants to move to a cloud-native solution for better scalability and cost efficiency.Your task is to simulate a simple data migration process by creating a dataset, setting a migration threshold, filtering data based on this threshold, and then displaying the filtered data.

🎯 Goal: Build a simple data migration script that filters Hadoop data entries based on a cloud migration readiness score.

📋 What You'll Learn

Create a dictionary with Hadoop data entries and their readiness scores

Set a migration threshold score

Filter the data entries that meet or exceed the threshold

Print the filtered data entries

💡 Why This Matters

🌍 Real World

Companies often need to decide which parts of their big data stored in Hadoop are ready to move to cloud-native platforms for better performance and cost savings.

💼 Career

Data engineers and data scientists use filtering and data selection techniques to prepare data for migration and processing in modern cloud environments.

1

Create Hadoop data readiness dictionary

Create a dictionary called hadoop_data with these exact entries: 'dataset1': 75, 'dataset2': 60, 'dataset3': 85, 'dataset4': 50, 'dataset5': 90

Hadoop

# Create the hadoop_data dictionary with the specified entries
# Your code here

Hint

Use curly braces to create a dictionary with keys as dataset names and values as readiness scores.

2

Set migration threshold

Create a variable called threshold and set it to 70 to represent the minimum readiness score for migration.

Hadoop

hadoop_data = {'dataset1': 75, 'dataset2': 60, 'dataset3': 85, 'dataset4': 50, 'dataset5': 90}
# Set the migration threshold to 70
# Your code here

Hint

Just assign the number 70 to the variable named threshold.

3

Filter datasets ready for migration

Create a new dictionary called ready_for_migration that includes only the entries from hadoop_data where the readiness score is greater than or equal to threshold. Use a dictionary comprehension with dataset and score as variables.

Hadoop

hadoop_data = {'dataset1': 75, 'dataset2': 60, 'dataset3': 85, 'dataset4': 50, 'dataset5': 90}
threshold = 70
# Filter datasets with score >= threshold
# Your code here

Hint

Use a dictionary comprehension with for dataset, score in hadoop_data.items() and an if condition.

4

Display datasets ready for migration

Print the ready_for_migration dictionary to show which datasets are ready to move to the cloud.

Hadoop

hadoop_data = {'dataset1': 75, 'dataset2': 60, 'dataset3': 85, 'dataset4': 50, 'dataset5': 90}
threshold = 70
ready_for_migration = {dataset: score for dataset, score in hadoop_data.items() if score >= threshold}
# Print the ready_for_migration dictionary
# Your code here

Hint

Use print(ready_for_migration) to display the filtered dictionary.

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