Hadoop in cloud (EMR, Dataproc, HDInsight) - Step-by-Step Execution

Concept Flow - Hadoop in cloud (EMR, Dataproc, HDInsight)

User submits Hadoop job

↓

Cloud service receives job

↓

Provision cluster resources

↓

Run Hadoop job on cluster

↓

Monitor job progress

↓

Job completes

↓

Results stored in cloud storage

↓

User accesses results

This flow shows how a Hadoop job runs in the cloud: user submits job, cloud sets up cluster, runs job, stores results, and user retrieves them.

Execution Sample

Hadoop

hadoop jar example.jar WordCount input output
# Monitor job status
# Access output in cloud storage

This example runs a Hadoop WordCount job on cloud, monitors it, and accesses results.

Execution Table

Step	Action	System State	Output/Result
1	User submits Hadoop job	Job received by cloud service	Job queued for execution
2	Cloud provisions cluster	Cluster nodes allocated and started	Cluster ready for job
3	Job starts running	Map and Reduce tasks executing	Partial progress logs available
4	Job running	Tasks processing data	Intermediate data generated
5	Job completes	All tasks finished successfully	Final output stored in cloud storage
6	User accesses results	Output files available in storage	Results ready for download or analysis
7	Job ends	Cluster may auto-terminate or stay active	Resources freed or retained

💡 Job completes successfully and results are stored for user access

Variable Tracker

Variable	Start	After Step 2	After Step 5	Final
Job Status	Not submitted	Queued	Completed	Completed
Cluster State	Not running	Running	Running	Running or Terminated
Output Data	None	None	Generated	Available in storage

Key Moments - 3 Insights

Why does the cloud service provision a cluster before running the job?

Because Hadoop jobs need a cluster of machines to process data in parallel, the cloud must allocate these resources first (see execution_table step 2).

What happens if the job fails during execution?

The job status would change to failed and no final output would be stored. This is not shown here but would stop the flow before step 5.

Can the cluster stay running after the job completes?

Yes, depending on settings, the cluster can stay active for more jobs or auto-terminate to save costs (see execution_table step 7).

Visual Quiz - 3 Questions

Look at the execution table, what is the system state after step 3?

A Map and Reduce tasks executing

B Job queued for execution

C Cluster nodes allocated and started

D All tasks finished successfully

💡 Hint

Check the 'System State' column for step 3 in the execution_table

At which step does the final output get stored in cloud storage?

A Step 2

B Step 4

C Step 5

D Step 6

💡 Hint

Look for 'Final output stored in cloud storage' in the 'Output/Result' column

If the cluster auto-terminates after job completion, which variable changes in variable_tracker?

A Job Status changes to 'Not submitted'

B Cluster State changes to 'Terminated'

C Output Data changes to 'None'

D Job Status changes to 'Running'

💡 Hint

Refer to 'Cluster State' row in variable_tracker for final state

Concept Snapshot

Hadoop in cloud runs jobs by provisioning clusters on demand.
User submits job, cloud sets up cluster, runs MapReduce tasks.
Results save to cloud storage for easy access.
Clusters can auto-terminate to save cost or stay active.
Services: EMR (AWS), Dataproc (GCP), HDInsight (Azure).

Full Transcript

This visual execution shows how Hadoop jobs run in cloud services like EMR, Dataproc, and HDInsight. First, the user submits a Hadoop job. The cloud service receives it and provisions a cluster of machines to run the job. Once the cluster is ready, the job starts running with Map and Reduce tasks processing data. Progress can be monitored during execution. When the job finishes successfully, the final output is stored in cloud storage. The user can then access these results for analysis or download. After job completion, the cluster may either stay running for more jobs or auto-terminate to save costs. Variables like job status, cluster state, and output data change through these steps, showing the job lifecycle in the cloud environment.