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Key Highlights

  • Access to Big Data Labs & Hadoop Clusters (cloud-based or physical)
  • Aligned to industry NOS/QP & NSQF Level 7 standards
  • Delivered by Senior Data Engineers from industry
  • Includes major project work + placement support
  • Mapping to AICTE Digital Skilling Framework

Course Highlights

  • Program Duration: 10 Months
  • Number of Projects: 6 Applied Projects + 1 Capstone
  • Live Sessions: 160 Hours (Instructor-Led)
  • Self-Paced Learning: 80 Hours of structured assignments
  • Credit Load: 18 Academic Credits
  • Mode of Learning: Online ILT + Virtual Labs (Hybrid Optional)
  • Language of Instruction: English

About Program

Course Curriculum

Modules designed to meet current industry standards.

01

Introduction to Data Engineering & Data Lifecycle

02

Relational & NoSQL Databases for Big Data

03

Hadoop Ecosystem: HDFS, Hive, MapReduce

04

Apache Spark & Distributed Processing

05

ETL Frameworks: Airflow, Kafka, Flume

06

Cloud Platforms: AWS/GCP Data Tools

07

Capstone Project – End-to-end pipeline from ingestion to analytics

What You’ll Learn

Essential Skills & Tools for Leading Projects in the Digital Age

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Executive Certification in Network Security & Ethical Hacking
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Executive Certification in Network Security & Ethical Hacking
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Need to know more?

Get to know the course in depth by downloading the course brochure
Download Brochure

Real People. Real Results​

Real stories of career growth, skill mastery, and success after MSM Grad programs.

30% Salary Hike

Ritika P.

Retail Data Engineer and ETL Developer

For years, I had been doing nightly ETL, but I had trouble scaling. The Hadoop/Spark blocks were the key, and the 168 hours of live sessions and 12-month cadence held me responsible. I rebuilt a Hive job in Spark, added appropriate partitioning, and scheduled it using Airflow based on input from my mentor. Our data freshness is no longer a daily battle, the pipeline is more straightforward, and it operates consistently. Good engineering practices are practiced in the labs; there are no magic bullets.

40% Salary Hike

Arun M.

FinTech Data Platform Engineer → BI Developer

To stop shipping dashboards with shaky data, I joined. I was able to construct a lake-to-warehouse path with checkpoints and quality gates thanks to the Kafka modules on storage design, governance, and streaming. Although it felt laborious to document lineage and access controls, audits now proceed more quickly. I was able to take pieces directly to production because the capstone project—end-to-end ingestion to analytics—mirrored my day job so closely. The senior data engineers who taught the course were realistic and pushed for quantifiable results.

25% Salary Hike

Shreya N.

A final-year computer science student

I wanted evidence that I was capable of more than just classwork. I processed in Spark, stored Parquet in a lake, set up an ingestion pipeline with Kafka, and exposed it to a warehouse for reporting in the big-data labs. My capstone’s GitHub repository includes the Airflow DAGs, tests, and README with run steps; in fact, interviewers inquired about choices like partition keys and file layout. Even though I’m just starting out, I can explain why my pipeline looks the way it does.

25% Salary Hike

Meera K.

Aspiring Data Engineer, Recent ECE Graduate

I was concerned about Hadoop and SQL depth because I come from the electronics industry. It was manageable because of the order: data lifecycle → databases → Hadoop → Spark → cloud tools. I learned when a NoSQL store made sense, set up basic monitoring, and constructed a small batch pipeline before adding a straightforward streaming path. Although I’m not “senior” yet, I can create and manage a dependable pipeline on a cluster and am aware of where I need to make improvements. The NSQF Level-7 alignment helped me get through HR screening.

Real People. Real Results​

Real stories of career growth, skill mastery, and success after MSM Grad programs.

30% Salary Hike

Ritika P.

Retail Data Engineer and ETL Developer

For years, I had been doing nightly ETL, but I had trouble scaling. The Hadoop/Spark blocks were the key, and the 168 hours of live sessions and 12-month cadence held me responsible. I rebuilt a Hive job in Spark, added appropriate partitioning, and scheduled it using Airflow based on input from my mentor. Our data freshness is no longer a daily battle, the pipeline is more straightforward, and it operates consistently. Good engineering practices are practiced in the labs; there are no magic bullets.

40% Salary Hike

Arun M.

FinTech Data Platform Engineer → BI Developer

To stop shipping dashboards with shaky data, I joined. I was able to construct a lake-to-warehouse path with checkpoints and quality gates thanks to the Kafka modules on storage design, governance, and streaming. Although it felt laborious to document lineage and access controls, audits now proceed more quickly. I was able to take pieces directly to production because the capstone project—end-to-end ingestion to analytics—mirrored my day job so closely. The senior data engineers who taught the course were realistic and pushed for quantifiable results.

25% Salary Hike

Shreya N.

A final-year computer science student

I wanted evidence that I was capable of more than just classwork. I processed in Spark, stored Parquet in a lake, set up an ingestion pipeline with Kafka, and exposed it to a warehouse for reporting in the big-data labs. My capstone’s GitHub repository includes the Airflow DAGs, tests, and README with run steps; in fact, interviewers inquired about choices like partition keys and file layout. Even though I’m just starting out, I can explain why my pipeline looks the way it does.

25% Salary Hike

Meera K.

Aspiring Data Engineer, Recent ECE Graduate

I was concerned about Hadoop and SQL depth because I come from the electronics industry. It was manageable because of the order: data lifecycle → databases → Hadoop → Spark → cloud tools. I learned when a NoSQL store made sense, set up basic monitoring, and constructed a small batch pipeline before adding a straightforward streaming path. Although I’m not “senior” yet, I can create and manage a dependable pipeline on a cluster and am aware of where I need to make improvements. The NSQF Level-7 alignment helped me get through HR screening.

Designed for Ambitious Professionals

Roles Offered
  • Data Engineer
  • Big Data Analyst
  • ETL Developer
  • Cloud Data Engineer
  • Data Platform Administrator
Salary Growth Potential
30- 60% Average Hike
Post Course Completion
Top Salary Bracket

Entry Level: ₹8–12 LPA

Mid Level: ₹15–24 LPA

Top Hiring Companies

Designed for Ambitious Professionals

Roles Offered
  • Data Engineer
  • Big Data Analyst
  • ETL Developer
  • Cloud Data Engineer
  • Data Platform Administrator
Salary Growth Potential
30- 60% Average Hike
Post Course Completion
Top Salary Bracket

Entry Level: ₹8–12 LPA

Mid Level: ₹15–24 LPA

Top Hiring Companies

You Asked, We Answered

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