Data Engineering Course
Learn Data Engineering through practical training in Python, SQL, ETL, data pipelines, data warehousing, cloud platforms, and big data technologies.
Data Science Beginner to Job-ReadyData Engineering
Learn Data Engineering through practical training in Python, SQL, ETL, data pipelines, data warehousing, cloud platforms, and big data technologies.
- Hands-On Data Engineering Projects
- Python & SQL Practice
- ETL & ELT Workflow Training
Course Overview
Build skills that go beyond theory
Data Engineering course in Agra at AI Scholars is designed for suitable for beginners, students, graduates, and working professionals who want structured, mentor-led training with practical work, certification guidance, and career preparation.
Build practical Data Engineering skills through hands-on training in Python, SQL, ETL and ELT workflows, data pipelines, data warehousing, Apache Spark, Hadoop, Kafka, cloud platforms, and workflow orchestration. Work on real-world data projects to understand how modern data systems are designed, processed, and managed.
Learning Outcomes
What you will be able to build
Turn guided lessons into practical abilities you can explain clearly in projects, internships, and interviews.
Build data pipelines
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Develop ETL and ELT workflows
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Work with cloud data platforms
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Process large datasets
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Design data warehouses
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Use Apache Spark
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Work with Kafka pipelines
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Automate data workflows
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Apply data engineering practices
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Build real-world data projects
Practice this through guided work using Python, SQL, Apache Spark and course projects.
Course Curriculum
A clear path from foundations to final project work
Modules are structured from the available course curriculum, keeping the learning sequence easy to scan without changing the underlying course data.
- Python for Data Engineering
- SQL & Advanced Database Concepts
- Data Modeling & Data Warehousing
- ETL & ELT Processes
- Apache Hadoop Fundamentals
- Apache Spark for Big Data Processing
- Data Pipeline Development
- Cloud Data Engineering (AWS & Azure)
- Apache Kafka
- Workflow Orchestration
- Data Governance & Security
- Real-World Data Engineering Projects
Tools & Technologies
Learn the tools used in modern workflows
The Data Engineering learning path includes practical exposure to the technologies listed in the course curriculum.
Live Project Experience
Build projects you can actually show in interviews
Transform what you learn into portfolio-ready work, documented demos, and practical explanations for professional conversations.
Portfolio Application
Hands-On Data Engineering Projects
Dashboard Workflow
Python & SQL Practice
API or Automation Project
ETL & ELT Workflow Training
Final Project Demo
Data Pipeline Development
Career Outcomes
Turn your skills into career-ready proof
The program connects classroom learning with project review, portfolio development, interview communication, and course counseling.
Industry-Relevant Skills
Understand how modern teams apply technical skills in practical workflows and project decisions.
Mentor Reviews
Receive guidance on project clarity, presentation quality, and gaps to improve before interviews.
Portfolio Proof
Use projects, demos, documentation, and explanations to demonstrate what you can build.
Interview Preparation
Practice explaining your project choices, tools, problem-solving process, and learning path.
Placement Roadmap
From learning to interviews
A simple progression helps students move from fundamentals to project confidence and career conversations.
Skill Foundation
Build strong technical fundamentals through guided training.
Guided Projects
Apply skills through real-world project work.
Portfolio Polish
Organize your strongest work into an interview-ready portfolio.
Mock Interviews
Practice technical explanations and professional communication.
Career Counseling
Receive guidance for internships, job profiles, and next career steps.
Mentor-Led Learning
Learn with guidance, feedback, and real project reviews
AI Scholars mentors help learners connect concepts with practical execution through live training, project checkpoints, portfolio improvement, and interview preparation.
Course Inquiry
Get batch timings, fee details, and demo class guidance
Share your details and the AI Scholars team will contact you about Data Engineering, curriculum, certification, and admission next steps.
Data Engineering
Request a callback for course counseling.
FAQ
Questions before joining Data Engineering?
A few quick answers before you speak with the counseling team.
Yes. The program starts with fundamentals and moves toward practical projects, portfolio work, and interview readiness.
Internship guidance is available with project tracks, mentor reviews, and certification options where applicable.
Yes. Project work is part of the learning path so students can demonstrate practical skills, not just complete theory.
AI Scholars supports resume improvement, mock interviews, portfolio review, and career counseling.
Helpful Next Pages
Explore connected programs and guides
Useful internal resources for students comparing programs, services, and local training options.
Start Your Learning Journey
Build practical skills with AI Scholars
Speak with our team about batches, curriculum, demo classes, certification, and career guidance.