A Data Engineer designs, builds, and maintains data pipelines to collect, transform, and store large volumes of data. They use tools like SQL, Python, Azure Data Factory, Databricks, and PySpark to ensure data is accurate, reliable, and ready for analytics and business intelligence.
Data Engineering is one of the most valuable fields in today’s data-driven world. Organizations rely on data to make informed decisions, improve business performance, and develop innovative solutions. OnlineITVidhya’s Data Engineering Training Program is designed to help learners build the practical skills required to work with modern data technologies.
Our experienced trainers provide industry-oriented training with hands-on exercises, real-time projects, and personalized guidance. Throughout the course, students learn essential concepts such as SQL, Python, Azure Data Factory, Azure Data Lake, Databricks, and PySpark. We focus on practical learning to help students understand data pipelines, data transformation, and data processing workflows. Our team supports learners throughout their journey and helps them build confidence for their future careers in Data Engineering.
OnlineITVidhya’s Data Engineering Training Program is suitable for fresh graduates, postgraduates, working professionals, and anyone interested in building a career in data technology.
Freshers who want to start a career in Data Engineering.
Software developers who want to expand their skills in data technologies.
IT professionals looking to transition into data-related roles.
Data analysts who want to learn data pipelines and data processing.
Working professionals seeking to upgrade their technical skills.
Basic computer knowledge and an interest in programming and data are helpful. The course starts with fundamental concepts and gradually progresses to practical applications, making it suitable for learners who are new to the field.
No advanced Data Engineering experience is required to get started. Basic computer knowledge and a willingness to learn are sufficient for beginners. Having a basic understanding of SQL or programming can be helpful, but learners can develop these skills during their training.
The program introduces key concepts step by step, including SQL, Python, data processing, data integration, Azure Data Factory, Azure Data Lake, Databricks, and PySpark. Through practical exercises and projects, students gain an understanding of how data is collected, transformed, processed, and prepared for analytics.
At OnlineITVidhya, we aim to help learners prepare for Data Engineering career opportunities through structured training and career guidance. Our placement assistance includes resume preparation, mock interviews, technical interview practice, and guidance on presenting your skills confidently to potential employers.
Students gain practical exposure through project-based learning and receive support in understanding industry expectations and interview preparation. Our team also provides guidance on communication skills and professional development to help learners approach recruitment opportunities with greater confidence.
Our goal is to help students strengthen their technical knowledge, build job-ready skills, and prepare for opportunities in the Data Engineering field. With consistent practice, project experience, and dedicated learning, learners can work toward building a successful career in Data Engineering.
Introduction to the world of Data Engineering
Overview of Data Engineering and its importance
Data Engineer roles and responsibilities
Data Engineering lifecycle
Structured, semi-structured, and unstructured data
Introduction to data warehouses and data lakes
Introduction to SQL
Database concepts and relational databases
DDL, DML, DQL, and DCL commands
Filtering, sorting, and grouping data
Joins and subqueries
Aggregate functions and window functions
Common Table Expressions (CTEs)
Stored procedures and query optimization
Introduction to Python programming
Variables, data types, and operators
Conditional statements and loops
Functions and modules
Lists, tuples, dictionaries, and sets
File handling and exception handling
Working with CSV, JSON, and other data formats
Introduction to Pandas and data processing
Introduction to ETL and ELT concepts
Data extraction from multiple sources
Data transformation and data cleansing
Data loading techniques
Building automated data pipelines
Batch processing and incremental loading
Data validation and error handling
Introduction to workflow orchestration
Introduction to Microsoft Azure
Introduction to Microsoft Azure
Azure Data Factory (ADF)
Creating pipelines and linked services
Datasets, triggers, and activities
Azure Data Lake Storage (ADLS)
Data ingestion and transformation
Pipeline monitoring and troubleshooting
Introduction to Databricks
Apache Spark architecture and fundamentals
Working with PySpark
DataFrames and Spark SQL
Data transformation and processing
Partitioning and performance optimization
Introduction to Delta Lake
Building scalable data processing pipelines
Introduction to data warehousing
OLTP vs. OLAP
Fact and dimension tables
Star schema and snowflake schema
Data modeling concepts
Data warehouse loading strategies
Introduction to modern cloud data platforms
Introduction to batch and streaming data
Real-time data processing concepts
Event-driven data pipelines
Introduction to message queues and streaming platforms
Handling real-time data ingestion
Data processing and monitoring
Building an end-to-end ETL pipeline
Integrating SQL, Python, and Azure Data Factory
Processing data using Databricks and PySpark
Storing data in Azure Data Lake
Transforming and preparing data for analytics
Pipeline testing, monitoring, and troubleshooting
Data Engineering interview questions
SQL and Python coding practice
Scenario-based technical questions
Resume preparation
Mock interviews
Project explanation and interview practice
| Course | Upcoming Batch | Enroll |
|---|---|---|
| AWS | October 22, 2026 5:00 pm | Enroll Now |
| Data Analyst | October 22, 2026 5:00 pm | Enroll Now |
| Data Engineer | October 22, 2026 6:00 pm | Enroll Now |
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| DevOps | October 22, 2026 6:00 pm | Enroll Now |
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| Power BI | October 22, 2026 7:00 pm | Enroll Now |
| Python | October 22, 2026 5:30 pm | Enroll Now |
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You will be provided with lifetime access to presentations, quizzes, installation guides and notes.
You will be provided with lifetime access to presentations, quizzes, installation guides and notes.
You will be provided with lifetime access to presentations, quizzes, installation guides and notes.
You will be provided with lifetime access to presentations, quizzes, installation guides and notes.
You will be provided with lifetime access to presentations, quizzes, installation guides and notes.
You will be provided with lifetime access to presentations, quizzes, installation guides and notes.
The Trainer was full of knowledge and excellent skills. The classes were also regular as they promised.
The Trainer was full of knowledge and excellent skills. The classes were also regular as they promised.
The Trainer was full of knowledge and excellent skills. The classes were also regular as they promised.
Data Engineering is the process of collecting, transforming, storing, and managing large amounts of data to make it available for analytics and business intelligence.
You will learn SQL, Python, Azure Data Factory, Databricks, PySpark, ETL/ELT processes, data pipelines, data warehousing, and real-time data processing through practical projects.
Freshers, graduates, software developers, database professionals, data analysts, and IT professionals who want to build a career in data engineering can join this course.
Basic programming knowledge is helpful but not mandatory. You can start with the fundamentals of SQL and Python and gradually learn advanced data engineering concepts.
Basic computer knowledge and an interest in databases, programming, and data technologies are useful. Beginners can start with foundational concepts.
Yes. The course can help freshers build foundational knowledge, gain practical experience, and prepare for entry-level data engineering roles.
Yes, practical exercises and projects can help you understand data pipelines, ETL workflows, data transformation, and cloud-based data processing.
The curriculum may cover SQL, Python, Azure Data Factory, Azure Databricks, PySpark, cloud data storage, ETL/ELT tools, and data warehousing concepts.
You can explore roles such as Data Engineer, Junior Data Engineer, Azure Data Engineer, ETL Developer, Big Data Engineer, and Data Pipeline Developer, depending on your skills and experience.
A Data Analyst interprets data and creates reports to support business decisions. A Data Engineer builds and maintains the systems, pipelines, and infrastructure that make data available for analysis.
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