🔥 Excellent.
Now we move from “I can run Spark on AWS” → “I can build production-grade pipelines”.
This is where your profile becomes senior / interview-ready.

🔄 LESSON 6 — Orchestration & Automation on AWS

(Cron / Oozie → Airflow & EMR Steps | Production Level)

Amazon EMR
Amazon MWAA

🎯 Lesson Outcome

By the end of this lesson, you will:

• Automate PySpark jobs using EMR Steps
• Understand Airflow (MWAA) architecture
• Design retry + failure handling
• Build resume-grade orchestration
• Answer senior-level interview questions

🧠 First: Why Orchestration Matters

Without orchestration:

• ❌ Manual Spark runs
• ❌ No retries
• ❌ No dependency control
• ❌ No monitoring

With orchestration:

• ✅ Automated pipelines
• ✅ Retry on failure
• ✅ Clear dependencies
• ✅ Production-ready workflows

🧩 Architecture: Real Production Flow

S3 (raw)
   ↓
EMR Spark Job
   ↓
S3 (curated)
   ↓
Athena / Downstream

Controlled by:

• EMR Steps (simple)
• Airflow (advanced)

1️⃣ EMR Steps (MOST USED IN REAL PROJECTS)

🧠 What is an EMR Step?

A step = automated Spark job submitted to EMR.

Equivalent to:

spark-submit job.py

🔹 Add EMR Step (Hands-On)

Go to:
EMR → Your Cluster → Steps → Add step

Choose:

• Step type: Spark application
• Deploy mode: Cluster
• Action on failure:

Continue / Cancel and wait

🔹 Example Spark Step

spark-submit \
--master yarn \
s3://rajeev-data-lake-2026/scripts/sales_etl.py

📌 Spark code lives in S3, not local

🧪 TASK 1 (Conceptual – Reply YES)

Understood EMR Steps concept: YES

2️⃣ Retry & Failure Handling (INTERVIEW GOLD)

Options in EMR Steps

• Continue (non-critical job)
• Cancel and wait (manual fix)
• Terminate cluster (cost save)

🧠 Interview line:

“We configured EMR steps with failure actions and retries to ensure pipeline reliability.”

3️⃣ Airflow on AWS (MWAA) — BIG PICTURE

🧠 What is MWAA?

Apache Airflow on AWS, fully managed.

You get:

• DAGs
• Scheduling
• Dependency management
• Monitoring

Without managing servers.

Airflow vs EMR Steps

📌 Most teams use BOTH

4️⃣ Sample Airflow DAG (Read Only – Important)

from airflow import DAG
from airflow.providers.amazon.aws.operators.emr import EmrAddStepsOperator
from airflow.utils.dates import days_ago

with DAG(
    dag_id="sales_etl_pipeline",
    start_date=days_ago(1),
    schedule_interval="@daily"
) as dag:

    run_spark = EmrAddStepsOperator(
        task_id="spark_etl",
        job_flow_id="j-XXXXXXXX",
        steps=[{
            'Name': 'Sales ETL',
            'ActionOnFailure': 'CONTINUE',
            'HadoopJarStep': {
                'Jar': 'command-runner.jar',
                'Args': [
                    'spark-submit',
                    's3://rajeev-data-lake-2026/scripts/sales_etl.py'
                ]
            }
        }]
    )

📌 You don’t need to master DAG coding now
You need to understand architecture & flow

🧠 On-Prem → AWS Mapping (MUST REMEMBER)

💡 Interview GOLD (Senior Level)

✔ Why EMR clusters are often ephemeral
✔ Why orchestration should be decoupled
✔ Difference between workflow and job
✔ How retries & alerts are implemented

🧾 Resume Bullets You Earn (IMPORTANT)

Automated PySpark pipelines on AWS EMR using EMR Steps and Airflow (MWAA)
Implemented retry logic, failure handling, and scheduling for production ETL workflows

🚀 YOUR AWS PROJECTS (FINALIZED FOR RESUME)

🏗 Project 1 — AWS Cloud Data Lake (CORE)

Tech: S3, Glue, Athena, EMR, PySpark
Highlights:

• Raw → Cleansed → Curated
• Glue Catalog
• Spark on EMR
• Athena queries

🔁 Project 2 — On-Prem to AWS Migration

Tech: S3, EMR, IAM
Highlights:

• Migrated Spark jobs from HDFS to S3
• Replaced Hive with Glue
• IAM-based security

⚙ Project 3 — Orchestrated ETL Pipeline

Tech: EMR, MWAA, Spark
Highlights:

• Airflow DAG
• Retry & monitoring
• Cost-optimized ephemeral clusters

☁ Project 4 — Serverless Python Data Validation

Tech: Lambda, S3, CloudWatch
Highlights:

• Python validation on S3 upload
• Logging & alerts

⏭ NEXT LESSON (FINAL CORE)

📊 LESSON 7 — Monitoring, Logging & Cost Optimization

You’ll learn:

• CloudWatch logs & metrics
• Spark job debugging
• Cost control strategies
• What interviewers REALLY ask

🔔 Quick Reply (Just YES)

Ready for Lesson 7

After Lesson 7, we’ll:
✅ Convert projects into perfect resume bullets
✅ Prepare AWS + PySpark interview Q&A
✅ Align with AWS Data Engineer roles

You’re doing GREAT 🚀