Data Engineering Course in Chennai

825 Ratings

Build the skills needed to work with modern data platforms through han ds-on Data Engineering training. Learn how to collect, process, store, and analyze data using Python, SQL, MongoDB, Apache Spark, Kafka, Azure, AWS, and Power BI.

This course is designed for students, freshers, job seekers, and working professionals who want to start or advance their careers in Data Engineering. Through practical exercises, real-world projects, and expert guidance, you will gain the knowledge required to build data pipelines, manage large datasets, and work with cloud-based data solutions.

Why Choose EduBrights?

Industry-focused Data Engineering curriculum
Hands-on projects based on real business scenarios
Training on Python, SQL, Spark, Kafka, Azure, and AWS
Live instructor-led classes with practical sessions
Resume building and interview preparation support
Placement assistance for eligible learners
Flexible learning options for students and professionals
Course completion certification

Career Opportunities

After completing the Data Engineering Course, you can explore opportunities such as:

Data Engineer
Big Data Engineer
ETL Developer
Cloud Data Engineer
Data Analyst
Analytics Engineer
Data Integration Engineer
Business Intelligence Developer

Start your Data Engineering journey with practical training and gain the skills employers are looking for in today's data-driven industry.

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₹26000

₹30000

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Case Studies and Projects

Hours of Training

40+

Placement Assurance

100%

Expert Support

Yes

Support & Access

Lifetime

Certification

Yes

Skill Level

All

Language

All

Course Objectives

1. Transform Data into Business Insights Through Visualization

Learn how to connect, analyze, and visualize data using Power BI. Create interactive dashboards and reports that help stakeholders understand business performance, identify trends, and make data-driven decisions using insights generated from engineered data pipelines.

2. Learn Real-Time Data Streaming and Event Processing

Develop the skills required to build real-time data pipelines using Apache Kafka and modern streaming technologies. Understand how organizations process live data from applications, websites, IoT devices, and business systems to enable real-time analytics, monitoring, and decision-making.

3. Master Azure Data Engineering Fundamentals

Gain a strong understanding of Azure's data ecosystem, including data storage, processing, transformation, and analytics services used in modern enterprise environments.

4. Design and Implement Data Storage Solutions

Learn how to build scalable data storage architectures using Azure Data Lake Storage Gen2, Azure Blob Storage, Azure SQL Database, and Azure Synapse Analytics.

5. Develop and Optimize Data Processing Pipelines

Create, manage, and optimize data integration workflows using Azure Data Factory, Azure Synapse Pipelines, and Azure Databricks for efficient data movement and transformation.

6. Implement Big Data Processing Solutions

Work with large-scale datasets using Apache Spark, Azure Databricks, and Synapse Analytics to process structured and unstructured data efficiently.

Popular Techniques Covered in This Course

1.Azure Data Lake Storage and Blob Storage

2.Azure Synapse Analytics

3.Azure Databricks (Apache Spark)

4.Azure Data Factory and Pipeline Orchestration

5.Azure Event Hubs and Stream Analytics

6.Data security and compliance in Azure

7.SQL and data query design

8.Performance tuning and cost management

9.Monitoring and logging with Azure Monitor

10.Data modeling and transformation techniques

Get Hands-on Knowledge about Real-Time Projects

Project 1

1.End-to-End Data Pipeline:

Build a data pipeline that ingests raw data into Azure Data Lake, transforms data using Azure Databricks, then loads processed data into Azure Synapse Analytics for reporting.

Project 2

2.Real-Time Stream Processing Solution:

Implement a solution that captures streaming data via Azure Event Hubs, processes it with Azure Stream Analytics, and triggers alerts via Azure Functions.

Project 3

3. Real-Time Social Media Analytics Platform

This project focuses on building a real-time analytics platform capable of processing social media data as it is generated. Students will use Apache Kafka to ingest streaming data from various social media feeds and Apache Spark to process and analyze the incoming information in real time.

Project 4

4. Cloud-Based Data Lake and Data Warehouse Solution

In this project, students will design and implement a cloud-based data architecture that consolidates information from multiple business systems into a single analytics platform. Data from applications such as CRM systems, ERP platforms, and external sources will be collected and stored in cloud data lakes using Azure Blob Storage or Amazon S3.

Project 5

5. Healthcare Data Integration and Reporting System

This project simulates a healthcare environment where patient records, appointments, treatments, and billing information are collected from multiple systems. Students will develop ETL pipelines that integrate these datasets into a centralized repository using Python, SQL, PostgreSQL, and MongoDB.

Key Features

Edubrights offers Data Engineering Training in virtual mode with expert trainers. Here are the key features,

40 Hours Course Duration

100% Job Oriented Training

Industry Expert Faculties

Free Demo Class Available

Completed 500+ Batches

Certification Guidance

Curriculum

Module 1: Introduction to Data Engineering

What is Data Engineering? Roles vs. Data Science
Data types: Structured, Semi-structured, Unstructured
The Data Lifecycle: Capture → Store → Process → Analyze → Visualize
Big Data characteristics (Volume, Velocity, Variety)
Real-world use cases and career paths

Module 2: Python for Data Engineering

Python fundamentals: variables, data types, operators
Control flow (if/else, loops), functions
Data structures: lists, tuples, dictionaries, sets
Libraries for data manipulation: NumPy, Pandas
Writing scripts for data extraction and cleaning

Module 3: SQL & Relational Databases

SQL fundamentals: SELECT, INSERT, UPDATE, DELETE
JOIN operations (INNER, LEFT, RIGHT, FULL)
WHERE clauses, filtering, aggregation
Database design: normalization, primary/foreign keys
Hands-on with MySQL or PostgreSQL

Module 4: NoSQL Databases

SQL vs. NoSQL: when to use each
MongoDB: document-oriented database
CRUD operations in MongoDB
JSON data format, querying with MongoDB Query Language
Hands-on with MongoDB Compass

Module 5: Big Data Technologies

Introduction to distributed computing
Apache Hadoop ecosystem (HDFS, YARN, MapReduce – high level)
Apache Spark for large-scale data processing
Spark basics: RDDs, DataFrames, Spark SQL
Batch vs. real-time processing

Module 6: Data Warehousing & ETL/ELT

What is a Data Warehouse? vs. Data Lake
ETL/ELT processes and workflows
Dimensional modeling, star/snowflake schemas
Tools: Apache Airflow for workflow orchestration
Building simple ETL pipelines

Module 7: Cloud Platforms for Data Engineering

Cloud benefits for data engineering
Introduction to Microsoft Azure and AWS
Cloud storage, compute, and networking basics
Azure Data Services
Azure Data Factory (ETL/ELT orchestration)
Azure Synapse Analytics (data warehousing)
Azure Blob Storage
Azure Databricks (Spark-based processing)
Azure SQL Database
AWS Data Services
Amazon S3 (object storage)
Amazon Redshift (data warehousing)
AWS Glue (ETL jobs)
Amazon EMR (Hadoop/Spark)

Module 8: Real-Time Data Streaming

Introduction to Apache Kafka
Producers, consumers, topics, partitions
Real-time data ingestion use cases
High-level overview of streaming pipelines
Module 9: Data Visualization
Introduction to Power BI
Connecting Power BI to data sources
Creating reports and interactive dashboards
Sharing insights with stakeholders

Module 10: Advanced Topics (Optional)

Snowflake (cloud data warehouse)
Informatica (enterprise ETL)
Apache Hive (data warehouse on Hadoop)
Machine Learning fundamentals for Data Engineers (supervised/unsupervised)

Module 11: Capstone Project

Build an end-to-end data pipeline:
Extract data from APIs/files
Clean and transform using Python/SQL
Store in SQL/NoSQL or data lake
Orchestrate with Airflow or Data Factory
Visualize insights in Power BI

Receive Training From Our Skilled and Effective Trainers

Experience in the Industry Gain knowledge from experts with practical Data Engineering project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture and cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.

Certified Data Engineering Professional

Our institution offers a certified Data Engineering program validating your ability to design, build, and maintain complex data solutions. The certification equips you with practical skills in data storage, batch and real-time processing, workflow orchestration, and securing data pipelines—all essential for modern data engineering roles.

Course FAQs

Q1. What does a Data Engineer do?

A Data Engineer designs, develops, and maintains data pipelines that collect, transform, and store data for analytics, reporting, and business decision-making.

Q2. Is Data Engineering a good career in 2026?

Yes. Data Engineering continues to be one of the fastest-growing technology careers as organizations increasingly rely on big data, cloud platforms, and analytics.

Q3. What is the average salary of a Data Engineer in India?

Data Engineers are among the highest-paid professionals in the data domain. Salaries vary based on experience, skills, certifications, cloud expertise, and project exposure.

Q4. Can freshers become Data Engineers?

Yes. Freshers can start a career in Data Engineering by learning Python, SQL, databases, cloud technologies, and data pipeline development through structured training and projects.

Q5. Which companies hire Data Engineers?

Data Engineers are hired by IT services companies, product-based organizations, banks, healthcare companies, e-commerce businesses, consulting firms, and cloud service providers.

Q6. Is Python mandatory for Data Engineering?

Python is one of the most widely used programming languages in Data Engineering and is highly recommended for data processing, automation, and pipeline development.

Q7. What is the difference between a Data Engineer and a Data Scientist?

Data Engineers build and manage data infrastructure, while Data Scientists analyze data and create predictive models to generate business insights.

Q8. What tools will I learn in this Data Engineering Course?

You will learn Python, SQL, MongoDB, Hadoop, Spark, Airflow, Kafka, Azure Data Factory, Azure Databricks, AWS Glue, Amazon Redshift, Power BI, and other industry-standard tools.

Q9. Does the course include real-time projects?

Yes. The course includes hands-on projects involving data ingestion, ETL pipelines, cloud data platforms, data warehousing, and dashboard development using real-world datasets.

Q10. Do I need coding experience to learn Data Engineering?

No prior coding experience is required. The course starts with Python fundamentals and gradually progresses to advanced data engineering concepts and tools.

Q11: What Azure services are covered?

Azure Synapse Analytics, Azure Databricks, Azure Data Factory, Azure Data Lake Storage, Event Hubs, Cosmos DB, and SQL Database.

Q12: Do I need programming skills?

Yes, knowledge of SQL, Python, or Scala is recommended.

Q13: Is experience with big data required?

Understanding big data concepts and architectures helps, though some foundational topics are covered.

Q14: How long is the exam?

Around 120 minutes with 40-60 questions.

Q15: What types of questions are on the exam?

Multiple choice, drag-and-drop, case studies, and scenario-based questions.