Migration from Hadoop to cloud-native - Step-by-Step Execution

Concept Flow - Migration from Hadoop to cloud-native

Start: Existing Hadoop Cluster

↓

Assess Data & Workloads

↓

Choose Cloud Provider & Services

↓

Plan Migration Strategy

↓

Data Transfer & Validation

↓

Rebuild or Adapt Workloads

↓

Test & Optimize in Cloud

↓

Switch Production to Cloud

↓

Decommission Hadoop Cluster

This flow shows the main steps to move data and workloads from Hadoop to cloud-native platforms.

Execution Sample

Hadoop

1. Assess data size and types
2. Select cloud storage (e.g., S3)
3. Transfer data using tools (e.g., DistCp)
4. Adapt processing to cloud services
5. Validate and optimize workloads

This sequence outlines the key actions in migrating Hadoop data and jobs to cloud-native services.

Execution Table

Step	Action	Details	Result
1	Assess Data & Workloads	Check data size, formats, and job types	Migration plan tailored to data
2	Choose Cloud Provider	Select AWS, Azure, or GCP and services	Cloud environment ready
3	Plan Migration	Decide lift-and-shift or refactor	Clear migration approach
4	Data Transfer	Use DistCp or cloud transfer tools	Data copied to cloud storage
5	Adapt Workloads	Rewrite jobs for cloud-native tools	Workloads compatible with cloud
6	Test & Optimize	Run jobs, monitor performance	Validated and efficient workloads
7	Switch Production	Redirect users and jobs to cloud	Production runs on cloud
8	Decommission Hadoop	Shutdown old cluster	Cost savings and cleanup
9	End	Migration complete	All workloads on cloud-native platform

💡 Migration ends after decommissioning Hadoop and running workloads on cloud

Variable Tracker

Variable	Start	After Step 4	After Step 7	Final
Data Location	On-prem Hadoop HDFS	Copied to Cloud Storage	Cloud Storage active	Cloud Storage active
Workloads	Hadoop MapReduce/Spark	Partially adapted	Fully adapted to cloud-native	Fully adapted to cloud-native
Cluster Status	Running Hadoop Cluster	Running Hadoop Cluster	Running Cloud Environment	Hadoop Cluster Decommissioned

Key Moments - 3 Insights

Why do we need to assess data and workloads before migration?

Assessing data and workloads helps choose the right cloud services and migration strategy, as shown in execution_table step 1.

What happens if workloads are not adapted for cloud-native services?

Workloads may not run efficiently or at all in the cloud, causing failures or poor performance, as indicated in step 5 of execution_table.

Why is decommissioning the Hadoop cluster important after migration?

Decommissioning saves costs and avoids confusion by shutting down old systems, as shown in step 8 of execution_table.

Visual Quiz - 3 Questions

Look at the execution_table, what is the result after Step 4 (Data Transfer)?

A Data copied to cloud storage

B Workloads fully adapted

C Hadoop cluster decommissioned

D Migration plan tailored

💡 Hint

Check the 'Result' column for Step 4 in execution_table

At which step does the production switch to cloud-native workloads?

A Step 5

B Step 7

C Step 3

D Step 8

💡 Hint

Look for 'Switch Production' action in execution_table

If data is not validated after transfer, which step's result would be affected?

A Step 2

B Step 4

C Step 6

D Step 8

💡 Hint

Validation and optimization happen in Step 6 according to execution_table

Concept Snapshot

Migration from Hadoop to cloud-native:
1. Assess data and workloads
2. Choose cloud provider and services
3. Plan migration strategy
4. Transfer data (e.g., DistCp)
5. Adapt workloads for cloud-native tools
6. Test, optimize, and switch production
7. Decommission old Hadoop cluster

Full Transcript

This visual execution shows the step-by-step process of migrating from Hadoop to cloud-native platforms. It starts with assessing data and workloads to understand what needs moving. Then, you select a cloud provider and plan how to migrate, either by lifting and shifting or refactoring workloads. Data is transferred using tools like DistCp. Workloads are adapted to run on cloud-native services. After testing and optimizing, production switches to the cloud environment. Finally, the old Hadoop cluster is decommissioned to save costs. Tracking variables like data location, workload type, and cluster status helps see progress. Key moments include understanding why assessment is needed, the importance of adapting workloads, and why decommissioning matters. The quizzes check understanding of each step's results and timing.

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