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.