Certified Data Engineering Course
Full Stack Data Engineering and Big Data Analytics Course
200 Hours
24 Classes
Learning Modes
Interactive Live Online sessions
Live, Interactive classes—Right on your screen, with instant doubt support.
Blended eLearning​
Your Learning, Your Pace—backed by real-time interaction and doubts support.
Courses Included
Artificial Intelligence Engineering
₹ 20000/-
Artificial Intelligence Engineering
₹ 20000/-
Artificial Intelligence Engineering
₹ 20000/-
₹ 65000/-
₹ 48000*/-
Optional Course
Data Science using R
₹ 20,000
₹ 4000/-
Learning Modes
Classroom Sessions
INR 68,440/-*
(Pay in Easy Installments)
Interactive Live Online
INR 59,000/-*
(Pay in Easy Installments)
Blended eLearning
INR 53,100/-*
(Pay in Easy Installments)
Program Fees
INR 25,000/-*
No of classes X Hours
24 x 3 = 72 hours
Assignment & Project Work:
4 hours
Total Student Workload:
~200 hours
No of classes X Hours
18 x 3 = 54 hrs
+ 18 hrs eLearning
Self Study Hours
120 hrs (8-10 hrs/week)
12 Assignments & Projects
Overview
Big Data Engineering used to be about processing large volumes of data. That’s no longer enough. Modern data engineering is about building reliable, scalable data systems that power analytics, AI, and real-time decision-making. Companies today need engineers who can design end-to-end data platforms across cloud, streaming, and machine learning workloads.
This data engineering course is a 13-week, future-ready program built for professionals who want to go beyond Hadoop clusters and batch jobs. Whether you’re coming from a big data background, a software role, or analytics, the course is structured to match what hiring managers actually look for in 2026: cloud-native pipelines, lakehouse architecture, streaming with Kafka, orchestration with Airflow, and production-grade data quality.
If you’re searching for a data engineer online course that teaches systems thinking over tool-hopping, this program is designed for you. Available online and at our centers in Bangalore, Gurgaon, and Noida.
Data Engineering Course Syllabus
The data engineering course syllabus is structured to mirror how data engineering teams actually operate in real organizations. Instead of teaching tools in isolation, every module connects to the one before it so you’re building a working data platform by the end of the program.
This goes well beyond what traditional big data engineering courses cover. Here’s what the 13 weeks look like:
| Module | What You Learn |
|---|---|
| Foundations | Python for data engineering, SQL (basics to performance tuning), Linux command line, cloud setup |
| Data Modeling | Dimensional modeling, star and snowflake schemas, modeling for analytics and ML feature stores |
| ETL & ELT Pipelines | Batch and incremental pipelines, data extraction from APIs and databases, transformation patterns |
| Spark & PySpark | Distributed data processing, DataFrames, performance optimization, working with large-scale datasets |
| Lakehouse Architecture | Delta Lake, data versioning, merge operations, building a unified lakehouse on cloud |
| Cloud Data Warehousing | Snowflake: architecture, loading, querying, cost management, integrating with BI tools |
| Streaming Pipelines | Apache Kafka for real-time data ingestion, event-driven architectures, stream processing patterns |
| Orchestration | Apache Airflow: DAG design, scheduling, retries, SLAs, backfills, and alerting |
| Data Quality & Observability | Testing frameworks, data contracts, lineage tracking, monitoring pipeline health |
| CI/CD & Deployment | Infrastructure as code, version control for pipelines, automated testing, cost optimization |
| Capstone Project | End-to-end system: ingest, transform, orchestrate, test, deploy, and present |
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Capstone Projects
This python data engineer course includes a capstone project as part of the final evaluation. Every enrolled student is required to complete it to earn their certification.
Your capstone is graded on how well you handle the full lifecycle of a data engineering project, not just code. Here’s the evaluation breakdown:
This python data engineer course includes a capstone project as part of the final evaluation. Every enrolled student is required to complete it to earn their certification.
Your capstone is graded on how well you handle the full lifecycle of a data engineering project, not just code. Here’s the evaluation breakdown:
| Evaluation Criteria | Weightage |
|---|---|
| Architecture and design choices | 20% |
| Code quality and reproducibility (CI/CD) | 20% |
| Data quality checks and testing | 20% |
| Pipeline orchestration and scheduling | 20% |
| Documentation and presentation | 20% |
Data Engineering Course Fees
Interactive Live Online
EMI starts @ 8,260
EMI Options
*Pay in easy EMIs starting at INR ₹6387 per month.
₹ 25,000/- plus taxes
Blend the dynamic experience of traditional classroom with engaging, real-time interactive sessions, carefully tailored to meet the demand of busy schedules. This innovative approach ensures effective learning, fostering a deeper understanding and retention of knowledge.
Blended eLearning
EMI starts @ 8,260
EMI Options
*Pay in easy EMIs starting at INR ₹6387 per month.
₹ 20,000/- plus taxes
Fuse the rich atmosphere of classroom instruction with the flexibility and accessibility of eLearning modules, meticulously integrated to accommodate learning preferences. This unique blend ensures an optimal learning experience, empowering participants to delve into subjects deeply.
Who Is This Course For?
This data engineer online course expects a basic working knowledge of Python, SQL, Linux command line, and core database concepts. If you’re not there yet, the program includes a 1-week pre-course bootcamp and self-study checklist to get you ready before classes begin.
This course works well for:
- Data Analysts, SQL developers, or BI professionals looking to move into engineering roles
- Software Engineers or backend developers who want to specialize in data infrastructure
- ETL or Data Warehouse developers ready to work with modern cloud-native tools
- Cloud or DevOps engineers looking to shift toward data platform work
- Python developers exploring a career path in data engineering
- Final-year STEM students who want to enter the field with production-relevant skills
Job Roles
After completing this data engineering course, you’ll be prepared for roles like:
- Junior Data Engineer
- Analytics Engineer
- Cloud Data Engineer
- Data Engineer
- Modern Data Engineer
- Data Platform Engineer
- Big Data Engineer
- ML / Feature Engineer
Key Skills
- Design end-to-end data pipelines (batch + streaming)
- Build scalable, fault-tolerant ETL and streaming pipelines
- Model data for BI, analytics, and ML feature pipelines
- Orchestrate workflows with retries, SLAs, backfills, and alerts
- Implement data quality checks, lineage, and observability
- Deploy pipelines using CI/CD and infrastructure-as-code
- Optimize performance and cloud costs proactively
Assignments
- Exercises on Hadoop ecosystem
- Foundation modules exercises
- Exercises on Hadoop ecosystem
- Exercises on cloud computing
- Exercises on cloud computing
Projects
- Creating database in MongoDB using Pymongo
- Store Sales Prediction (Spark)
- SQL DB Creation (Cloud)
- ML Studio Project (Cloud)
- SQL DB Creation (Cloud)
- ML Studio Project (Cloud)
- Analysing HVAC (IOT) data (Hadoop Ecosystem)
- Analysing online retail data (Hadoop Ecosystem)
- Analysing lending club data (Hadoop Ecosystem)
- Analyzing Mobile App Data (Spark)
- Predicting customer Churn (Spark)
Programming Tools and Languages
Training Modes
Classroom & Bootcamp
Skill mastery with immersive, hands-on learning guided by mentors. Opt for either our intensive weekday bootcamps or flexible weekend classes to suit your schedule. Experience firsthand the power of in-person mentorship, available in weekday classroom bootcamps, ensuring a rich, experiential learning environment.
Fully Interactive Live Online
Blend the dynamic experience of traditional classroom with engaging, real-time interactive sessions, carefully tailored to meet the demand of busy schedules. This innovative approach ensures effective learning, fostering a deeper understanding and retention of knowledge.
Classroom Blended with eLearning
Fuse the rich atmosphere of classroom instruction with the flexibility and accessibility of eLearning modules, meticulously integrated to accommodate learning preferences. This unique blend ensures an optimal learning experience, empowering participants to delve into subjects deeply.
Career Support
Profile Building
A team of seasoned professionals will provide you personalized help for preparing CV and online profiles, based on your educational background and experience.
Interview Preparation
This will be followed by one to one interview preparation along with mock interviews (if required).
Continuous Support
There will be continuous support from our side for as long as you need it. Most of our students do get multiple interview calls and good career options based on the skills they learn during the course.
Big Data Course Certification
Owing to our well established domain expertise and prestigious clientele in India and overseas, AnalytixLabs certification is highly regarded in the industry. Being India’s top ranked Data Science institute it is imperative that we uphold the sanctity of our certification process.
Certification is awarded after the fair evaluation of mandatory case studies, assignments, MCQs, and viva included in the course.
Certification must be completed within one year of course registration.
In case the assignments and projects are not up-to-the-mark, trainees are welcome to take help and support for improvisation. But no kind plagiarism will be tolerated during evaluation.
Our objective is to ensure that trainees get vital hands-on experience so that they are well-prepared for job interviews along with a performance at their jobs.
How To Apply
select your course, complete the registration form, and make payment.
Submit your application
Submit your application by filling out the form, providing necessary details, and clicking submit to complete the process.
Give a selection test
Take the selection test to demonstrate your skills and knowledge. This will help us assess your readiness for the course.
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Payment
Complete your payment to finalize your enrollment in the course. Choose your preferred payment method and follow the prompts to proceed.
What Students Say About Us?
True Stories, Transformative Career Experience
Piyush Ganar, Class of 2012 IIM Ahmedabad
(Director of Operations, Kenty.AI)
The course material is very easy to understand and the case studies were based on real time business problems. What I love the most about Sumeet and his team is that they never operated the institute like a typical commercial enterprise but more like a temple for learning. The gates of Alabs are always open for students for any kind of help and guidance. I would recommend ALabs to all.
Raajeev Kumar Sahu
(Senior Manager – Data Science, FinTech Startup)
I am from Advance Big Data Science course of Nov 2016 batch. The course was very structured and got real world problems to practice during the final case studies. It has also boosted my skills to start participating in the hackathons. Chandra sir was really helped me in preparing my resume right from very beginning of the course. Also, the placement team was very well organized and connected to industry leaders so that people get the right opportunity right after completing the course. I really recommend this institute, who are interested to transition into analytics field.
Sumit Asthana
(Assistant Manager, Senior Data Scientist)
The rapid pace at which the institutes are mushrooming all over India and Facebook newsfeed being inundated with thousands of options for analytics training, there are only handful of training institutes or rather say only couple of places where they prepare you to foray into rapidly growing analytics vertical. I have been working into legacy systems for past 5 years and was quite apprehensive if I will ever break into data science successfully.
Surbhi Sultania
(Analytics Manager, Mastercard Data & Services)
I took three months Big Data training from Analytix labs. Before joining this course, I had so many questions and doubts that will this course be worthy enough but later I found myself lucky to join this program. All trainers are amazing and always ready to help. Live examples and Case Studies are given which helps in understanding the concept and hands on practice.Not only they help in quality in depth knowledge transfer but also tend to give right direction to your career.
Vibhu Gautam
(Professor of Practice – Computer Science, UPES)
I had been associated with AnalytixLabs since 2012. This has been possible because of the mentor ship and support you get from faculty there specially from Sumeet and Chandra.I did a Business Analytic course with a focus on Marketing Analytic. The course deliver had been great and most importantly you never feel like you are part of formal classroom teaching. You are mentored there at every step, from Code Errors to Understanding Statistics behind it.Â
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Frequently Asked Questions
Is this course suitable for beginners in big data?
Yes. Whether you’ve worked with Hadoop, batch ETL, or legacy big data tools, or you’re completely new to the ecosystem, this course is built for you. It starts with foundational concepts like Python, SQL, and cloud basics before moving into modern practices like lakehouse architecture, streaming, orchestration, and CI/CD.
It’s a good fit if you’re:
- New to big data and want a structured, ground-up learning path
- Already working in big data engineering roles and looking to upgrade your skills for modern platforms
- Moving beyond Hadoop-centric job descriptions toward cloud-native data engineering
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How is Data Engineering different from Data Science?
Data Engineering and Data Science solve different problems. Data Engineering focuses on building and maintaining data systems: pipelines, storage, streaming platforms, and data reliability. Data Science focuses on analyzing data and building models once clean, reliable data is available.
This course is not a data science engineering course. It focuses on:
- Data ingestion, transformation, orchestration, and deployment
- Cloud data platforms, streaming systems, and data quality
- Supporting analytics and ML teams by delivering production-ready data
If your goal is to build the data foundation that analytics and AI depend on, this is the right path. [Read more on how Data Science is different from Data Engineering.]
Do I need a prior coding background?
A deep software engineering background is not mandatory. Basic familiarity with logic or scripting is enough to get started.
The course covers Python fundamentals before moving into PySpark, SQL from basics to performance-aware analytics queries, and hands-on labs with real pipelines. If you’re looking for a python data engineer course that balances fundamentals with production use cases, this program takes you from basics to applied data engineering without assuming advanced coding skills.
How do I choose the best data engineering course for my career goals?
The right data engineering courses focus on systems, not just tools. When evaluating courses to become a data engineer, look for:
- End-to-end pipeline design covering both batch and streaming
- Cloud-native architectures including lakehouse and warehouse patterns
- Orchestration, CI/CD, and data quality as part of the core curriculum
- Real-world projects, not isolated tool tutorials
This course is designed for learners who want production readiness, not just a certificate. That’s what makes it one of the best courses for data engineering if your goal is long-term career growth.
What is included in the data engineering course syllabus?
The syllabus mirrors how data engineering teams work in real organizations. Key areas include: data engineering foundations (Python, SQL, cloud basics), data modeling for analytics and ML, ETL/ELT pipelines, big data processing with Spark and PySpark, lakehouse architecture with Delta Lake, cloud data warehousing with Snowflake, streaming pipelines with Kafka, workflow orchestration with Airflow, data quality and observability, CI/CD and infrastructure as code, and a capstone project with end-to-end system ownership.
This goes well beyond what traditional big data analytics courses cover, where the focus is usually limited to processing frameworks alone.
What are the recommended courses to become a data engineer if I’m a beginner?
For beginners, the best data engineering courses are those that start with fundamentals, teach modern industry tools, and focus on real-world system design rather than theory.
This course fits that profile. It works well if you’re exploring data engineering from scratch, upskilling from analytics, BI, or software roles, or looking for a data engineer course in Bangalore, Noida, or Gurgaon that matches what companies are hiring for today.
Unlike older big data engineering courses in India that focused heavily on Hadoop, this program is built around modern data platforms, cloud-native pipelines, and production practices used in 2026.
Can this course help me transition from Big Data roles to Data Engineering and AI-ready careers?
Absolutely. This course is designed specifically for that transition. Traditional big data engineering courses focused on batch processing, Hadoop ecosystems, and offline analytics. In 2026, organizations expect data engineers to build cloud-native pipelines, support real-time analytics and ML systems, and deliver reliable, production-grade data platforms.
From Big Data to Modern Data Engineering:
You move from tool-centric batch jobs and Hadoop-heavy ecosystems to end-to-end pipeline design, lakehouse and cloud data platforms, and full ownership of streaming, orchestration, and data quality.
From Data Engineering to AI-Ready Roles:
You extend your skills to support AI and ML by building feature pipelines for training and inference, serving data reliably to models and real-time applications, and managing data quality, lineage, and reproducibility that AI systems depend on.
Whether you’re currently in a big data engineering role, exploring courses to become a data engineer, or planning your next step toward AI-aligned data careers, this program bridges that gap.
Who should NOT take this course?
This course may not be the right fit if any of the following apply to you:
Looking for pure Data Science or AI modeling?
This is not a data science engineering course. The focus is on data pipelines, platforms, and reliability, not model training. We have other courses that cover Data Science, AI Modeling, and no-code/low-code AI. Explore them here.
Want quick tool tutorials without depth?
This course prioritizes system design and production readiness. It’s not a collection of copy-paste scripts.
Uncomfortable with coding or technical problem-solving?
Data Engineering requires hands-on work with Python, SQL, and distributed systems. A minimum comfort level with code is essential.
Looking for legacy Hadoop-only training?
If your goal is traditional big data training focused only on Hadoop, this course has moved past that. It covers Hadoop context but centers on modern tools and architecture.
Expecting results without putting in the work?
This program prepares you for real roles. But it requires genuine commitment and consistent practice.
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