Data Engineering Course
60 Hours
Live Online
Placement Assistance
Become a job-ready data engineer with hands-on pipeline projects, a real-world capstone build, and a recognized data engineering certification training course.
About the Data Engineering Full Course
Akira Global Technologies data engineering course is designed to take you from foundational concepts to real-world, industry-relevant data pipeline and cloud practices in just 60 hours. This data engineering full course covers the tools and workflows modern data teams rely on daily, including SQL and database fundamentals, Python for data processing, data modeling and warehousing, ETL/ELT pipeline development, the big data ecosystem with Hadoop and Spark, cloud data engineering across AWS, Azure, and GCP, workflow orchestration with Apache Airflow, and data quality and CI/CD practices.
Whether you’re a fresher, a software developer, a BI or analytics professional, or a QA engineer transitioning into data roles, this data engineering certification training course is built around live instructor-led sessions and 25+ hands-on labs so you graduate with practical, job-ready skills rather than theory alone. The program follows a balanced 40% theory and 60% hands-on structure, covering the complete data engineering lifecycle from ingestion and transformation to storage, orchestration, and monitoring.
This isn’t just a data engineering certification course on paper, it’s a practical, tool-focused program that culminates in an end-to-end capstone project mirroring real industry pipelines, so you finish ready to apply for Data Engineer, ETL Developer, and Big Data Engineer roles.
| Course Details | Information |
|---|---|
| Duration | 60 Hours (20-24 Sessions) |
| Mode | Online / Live Instructor-Led |
| Session Length | 2-3 Hours per Session |
| Level | Beginner to Intermediate/Advanced |
| Hands-On Labs | 25+ Practical Labs and Exercises |
| Capstone | 1 End-to-End Real-World Pipeline Project |
| Certification | Certificate of Completion + Capstone Project Evaluation |
| Tools Covered | SQL, Python, Hadoop, Spark, Kafka, Airflow, AWS/Azure/GCP, Docker & more |
Who Should Enroll?
What You'll Get
Training
- Core SQL & Database Training (8 Hrs)
- Python for Data Engineering (8 Hrs)
- Big Data & Cloud Training (17 Hrs)
- Orchestration, Quality & CI/CD Training (8 Hrs)
Hands-on Experience
- 25+ Practical Labs and Exercises
- Real Datasets Used Throughout
- 1 End-to-End Capstone Project
Get Certified
- Certificate of Completion
- Capstone Project Evaluation
- Project Experience Certificate
Interview Preparation
- Resume Building
- LinkedIn Profiling
- Mock Interviews from Industry Experts
Placements
- Placement Assistance Provided
- Interview Calls
- Guidance for Data Engineer, BI/Analytics, and Cloud roles
Career Assistance
- Placement Support & Resume Building
- Resumes Shared with Hiring Partners
- Interview Preparation Tips
Data Engineering Course Syllabus
Prerequisites:
- Basic programming logic
- No prior data engineering experience required
| Module | Duration | Topics Covered | Tools/Project |
|---|---|---|---|
| Module 1: Introduction to Data Engineering | 3 Hours |
|
- |
| Module 2: SQL & Database Fundamentals | 8 Hours |
|
Hands-on: Query optimization lab, schema design exercise |
| Module 3: Python for Data Engineering | 8 Hours |
|
Hands-on: Build a data ingestion script from an API to a local database |
| Module 4: Data Modeling & Data Warehousing | 6 Hours |
|
Hands-on: Design a data warehouse schema for a retail use case |
| Module 5: ETL/ELT Pipeline Development | 8 Hours |
|
Hands-on: Build an end-to-end ETL pipeline using Python and SQL |
| Module 6: Big Data Ecosystem - Hadoop & Spark | 9 Hours |
|
Hands-on: Process a large dataset using PySpark; build a Kafka producer-consumer |
| Module 7: Cloud Data Engineering (AWS/Azure/GCP) | 8 Hours |
|
Hands-on: Build a cloud-based ingestion-to-warehouse pipeline |
| Module 8: Workflow Orchestration - Apache Airflow | 5 Hours |
|
Hands-on: Orchestrate the Module 5 ETL pipeline using Airflow |
| Module 9: Data Quality, Testing & CI/CD | 3 Hours |
|
Hands-on: Add data validation checks to an existing pipeline |
| Module 10: Capstone Project & Wrap-Up | 2 Hours |
|
Capstone: Complete data pipeline project |
Data Engineering Admission Process
1
Fill Inquiry Form
Share your details and course interest
2
Counselling Call
Speak with our admissions team about your goals and fit
3
Get Course Access
Receive login credentials and join the orientation session
Requirements
Eligibility Criteria:
- Basic programming logic is preferred but no prior data engineering experience is required
- Open to freshers, software developers, BI/analytics professionals, and QA engineers from any technical background
Learn From Data Engineering Industry Experts
DET
Data Engineering Trainer
years of experience in Data Engineering and Cloud technologies.Expert in SQL, Python, Spark, Airflow, and AWS. Focuses on real-time pipeline projects and hands-on training to make students job-ready.
AWS
SQL
Python
Spark
Airflow
Tools & Technologies You'll Master
Testimonials
What Our Data Engineering Learners Say
Data Engineer at a fintech startup
"I always knew SQL but had no idea how a production pipeline actually came together. The ETL and data modeling modules connected the dots, and within two months I was confident talking about star schemas and CDC in interviews."
Business Intelligence Analyst moving into Data Engineering
"Good pace for someone coming from a dev background. The PySpark and big data sessions were the highlight, though I'd have liked a bit more depth on Kafka. Still landed a solid role right after finishing."
Software Developer, now Data Engineer
"Coming from testing, I was worried the big data and cloud modules would be too much, but the trainer broke everything down step by step. The recorded sessions were a lifesaver when I needed to revisit a topic."
QA Engineer transitioning into Data Engineering
"No professional experience going in, just some Python basics from college. The way the modules built on each other, from SQL to Python to Spark to Airflow, made it easy to keep up, and the resume support helped me get shortlisted fast."
Fresh Graduate, now Junior Data Engineer
"Genuinely comprehensive syllabus, Hadoop, Spark, Kafka, Airflow, and cloud all in one program. The live format meant I could get my doubts cleared immediately instead of waiting around like with self-paced courses I'd tried before."
Data Engineering Trainee
"The capstone project is what set this course apart for me. Building and orchestrating a full pipeline end to end gave me something real to walk into interviews with, not just theory on a slide."