Modern Data Engineering

Modern Data Engineering is a live, instructor-led program designed to build strong skills in data infrastructure, data pipelines, and scalable data systems using modern data engineering frameworks and cloud platforms.

  • This 24-week structured journey includes real data engineering projects, 10+ hands-on workshops, 30+ real-world data pipeline use cases, and masterclasses with continuous mentorship.
  • You will work with industry tools such as Python, SQL, Apache Spark, ETL frameworks, cloud data platforms, and distributed processing systems, then showcase your capabilities through a portfolio-ready capstone where you design and deploy scalable data pipelines and modern data platforms.
  • The program also supports certification preparation for tracks like Google Professional Data Engineer, AWS Data Analytics Specialty, Azure Data Engineer Associate, and Databricks Data Engineer.
Format Live Instructor-Led
Duration 24 Weeks | 180 Hours
Admission Deadline 30 April 2026
Case Studies & Projects 30+
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Key Program Takeaways

Build real data engineering capability through guided pipeline development labs, scalable data workflows, and project-based learning designed for modern data platforms and analytics infrastructure.

Data Pipeline Development

ETL/ELT Workflows, Data Ingestion, Pipeline Orchestration

Big Data Processing

Apache Spark, Distributed Computing, Data Transformation

Database & Storage Systems

SQL, Data Warehousing, Data Lakes

Data Integration

Batch Processing, Streaming Data, API Data Pipelines

Cloud Data Platforms

AWS, Azure, Google Cloud Data Services

Capstone Portfolio

End-to-End Data Engineering Pipeline Deployment

List of Modules in this Program

01

Foundations of Data Engineering

  • Introduction to modern data architecture and the role of data engineering.
  • Understanding data pipelines, data lakes, and data warehouses.
  • Overview of batch and real-time data processing systems.
02

Python for Data Engineering

  • Use Python for building data pipelines and automation workflows.
  • Work with libraries for data processing and transformation.
  • Develop scripts for data ingestion, processing, and integration.
03

SQL & Database Systems

  • Design and manage relational databases and large-scale data systems.
  • Write advanced SQL queries for analytics and data transformation.
  • Work with database optimization and performance tuning.
04

Data Pipelines & ETL Frameworks

  • Build ETL and ELT workflows for data ingestion and transformation.
  • Implement pipeline orchestration using modern data engineering tools.
  • Integrate APIs, streaming data, and external data sources.
05

Big Data Processing

  • Work with distributed data processing frameworks such as Apache Spark.
  • Process large datasets using scalable computing architectures.
  • Understand batch and real-time data processing pipelines.
06

Capstone Project

  • Design and implement a scalable end-to-end data pipeline.
  • Build data ingestion, transformation, and storage workflows.
  • Deploy a portfolio-ready data engineering project supporting analytics and AI systems.

Hands-on Roadmap

Weeks 1–4

Data Engineering Foundations & Environment Setup

  • Install Python, SQL environments, and data engineering tools
  • Learn data ingestion, transformation, and storage concepts
  • Hands-on builds: Data Ingestion Pipeline, Dataset Processing Tool
Weeks 5–8

Data Processing with Python & SQL

  • Use Python and SQL for data transformation and cleaning
  • Build scripts for extracting and processing datasets
  • Hands-on builds: Automated Data Cleaning Pipeline, Data Transformation Engine
Weeks 9–12

Data Pipelines & ETL Workflows

  • Design ETL and ELT workflows for ingestion and processing
  • Integrate APIs and external data sources into pipelines
  • Hands-on builds: API Data Ingestion Pipeline, Automated ETL Workflow
Weeks 13–16

Big Data Processing

  • Process data using Apache Spark and distributed frameworks
  • Understand batch and streaming data pipelines
  • Hands-on builds: Large Dataset Processing Pipeline, Streaming Data Processor
Weeks 17–20

Cloud Data Platforms & Data Infrastructure

  • Build scalable pipelines on AWS, Azure, or Google Cloud
  • Implement data lakes and data warehouse architectures
  • Hands-on builds: Cloud Data Warehouse Pipeline, Scalable Data Lake System
Weeks 21–24

Capstone Data Engineering Project

Build and deploy a production-ready data engineering platform. Capstone options include:
  • Real-Time Data Processing Pipeline
  • Enterprise Data Warehouse Architecture
  • Analytics-Ready Data Platform

Top Companies Hiring Data Engineers

Leading technology companies, cloud platforms, consulting firms, and digital enterprises actively hire data engineers to build scalable data pipelines, data platforms, and analytics infrastructure.

Amazon Google Microsoft Meta Apple Netflix Uber Airbnb IBM Oracle Snowflake Databricks McKinsey BCG Bain & Company Deloitte Accenture Flipkart Paytm Razorpay Swiggy Zomato PhonePe Infosys TCS Wipro Cognizant

Some of our exceptional outcomes with top companies.

Sai Arya

Sai Arya

₹65 LPA CTC

Nitish

Nitish

₹65 LPA CTC

Manurbhav A

Manurbhav A

₹21 LPA CTC

Aditya T

Aditya T

₹21 LPA CTC

Om Nakrani

Om Nakrani

₹65 LPA CTC

Yuvana T

Yuvana T

₹22 LPA CTC

Pardhiv A

Pardhiv A

₹21 LPA CTC

Phanindra B

Phanindra B

₹19 LPA CTC

Sai Gokul

Sai Gokul

₹65 LPA CTC

Abhigyan N

Abhigyan N

₹65 LPA CTC

Aryan T

Aryan T

₹21 LPA CTC

Devendra A

Devendra A

₹65 LPA CTC

Mahesh Reddy

Mahesh Reddy

₹65 LPA CTC

Prakul K

Prakul K

₹21 LPA CTC

Prashanth Reddy K

Prashanth K

₹65 LPA CTC

Rajeev T

Rajeev T

₹21 LPA CTC

Master Technologies

Core data engineering, big data processing, and modern data platform technologies used throughout the program.

Python logoPython
SQL logoSQL
Apache Spark logoApache Spark
Hadoop logoHadoop
Apache Kafka logoApache Kafka
Apache Airflow logoApache Airflow
dbt
Snowflake logoSnowflake
Databricks logoDatabricks
Data Lakes
Data Warehousing logoData Warehousing
Docker logoDocker
Git logoGit
GitHub logoGitHub
AWS Data Services
Azure Data Platform
Google BigQuery logoGoogle BigQuery

Eligibility & Admission

A structured admissions process with advisor support for modern data engineering learners.

Who Can Apply Eligibility and baseline coding readiness
  • Graduates with a Bachelor's degree in any discipline.
  • Final-year undergraduate students completing their degree before the program concludes.
  • Learners with basic SQL or Python understanding who want to transition into data engineering roles.
Admission Process Simple, structured steps from application to onboarding
  1. Application Submission: Complete a short online application with academic/professional details.
  2. Profile Review: Selected applicants receive official admission confirmation based on profile fit.
  3. Seat Confirmation: Reserve your seat with INR 10,000.
  4. Fee Completion: Pay the remaining fee within 7 days of confirmation or before program start, whichever is earlier.
  5. EMI Option: Financing starts from INR 8,125* via external lending partners, subject to eligibility and partner terms.
Learner Assistance Advisor support before and during onboarding

Program advisors are available 7 days a week, 10:00 AM to 7:00 PM.

Email: hello@42learn.com

Phone: 080 4736 3406

EMI Support: Starts from INR 8,125* via partner financing (eligibility and partner terms apply).

Disclaimer: Outcome, career progression, and salary information is indicative only; individual results vary by background, experience, and market conditions. Certificates/credits are governed by the issuing institution's policies where external partners are involved. Financing/EMI options are provided via external partners and subject to their terms; fee benefits and savings vary by eligibility and promotional window.