Data Engineering Job Oriented Program 
Jenny
Cloud Engineer at Walmart Labs
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Prepzee's Data Engineering and Cloud Masters program changed my career from SysAdmin to Cloud Expert in just 6 months. Thanks to dedicated mentors, I now excel in AWS, Terraform, Ansible, and Python.
Vishal Purohit
Product Manger at Icertis
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Great learning experience through the platform. The data engineering course curriculum is updated and covers all the topics. The trainers are experts in their respective fields and follow more of a practical approach.
Aman Yadav
Senior Associate Product at Amazon
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Nice experience, I will recommend the data engineering course with Prepzee to all the learners who are willing to join the data engineer course and learn IT skills. I was able to switch my domain from non-IT to IT in a reputed MNC.
You’re an IT Professional who is looking for a career in Data Engineering, especially dealing with Cloud-based solutions, and can explore data engineering training programs to build relevant skills.
You’re looking to switch domains into the Future Proof Data Industry without going into Statistics and coding, and may start in Data Engineering through a data engineer bootcamp.
You’re a DBA, with experience in database management and SQL, and can transition into data engineering roles with ease by enrolling in data engineering online courses.
You’re a Data Analyst/ Scientist who wants to work with data at a larger scale and manage data pipelines, which may transition into data engineering with the help of a data engineer bootcamp.
Aligned with a complete Data Engineer Roadmap, covering the top 3 data engineering tools most in demand on LinkedIn Jobs so learners progress with a clear, industry-mapped learning path.
Learn by doing multiple labs in your data engineering online training journey, following a structured data engineer roadmap from fundamentals to advanced concepts.
Get a feel for Data Engineering professionals by doing real-time projects during data engineering online courses, mapped to key stages of the data engineer roadmap.
Call us or email us whenever you get stuck during your learning journey.
Instructors are Microsoft Certified Trainers providing data engineer online training, with guidance aligned to an industry-relevant data engineer roadmap.
Attend multiple batches until you achieve your Dream Goal with the online data engineer master course, progressing confidently through the data engineer roadmap.
Get Mock Interview Sessions
Get guidance to show Projects & Experience in your resume
Get Sample Exam Papers for Certifications
Build ATS Friendly Resume for better Reach
14+ Years of experience
15+ Years of experience
12+ Years of experience
17+ Years of experience
The primary role involves designing, building, and maintaining data pipelines and infrastructure to support data-driven decision-making, aligned with a data engineer roadmap.
Responsible for integrating data from various sources, ensuring data quality, and creating a unified view of data for analysis.
Designing and managing data warehouses for efficient data storage and retrieval, often using technologies like Databricks, Snowflake and Azure.
Specializing in data engineering within cloud platforms like AWS, Azure leveraging cloud-native data services.
Providing expertise to organizations on data-related issues, helping them make informed decisions and optimize data processes.
Your work will involve leveraging Microsoft Fabric tools like OneLake, Data Factory, Eventstreams, and Data Warehouses for data integration, transformation
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Embark on your journey towards a thriving career in data engineering with best Data Engineering courses. This comprehensive program is meticulously crafted to empower you with the skills and expertise needed to excel in the dynamic world of data engineering. Learn Data Engineering with Prepzee, throughout the program, you’ll explore a wide array of essential tools and technologies, including industry favorites like Databricks, Snowflake, PySpark, Azure, Fabric, One Lake, DP-700 Certification and more.Dive into industry projects, elevate your CV and LinkedIn presence, and attain mastery in Data Engineer technologies under the mentorship of seasoned experts.
Introduction to Python
Overview of Python and its role in Data Engineering workflows
Installation and Setup
Setting up Python environment and development tools
Running Python Scripts
Executing Python programs and understanding script structure
Variables and Data Types
Understanding how data is stored and manipulated
Lists, Tuples, Sets, and Dictionaries
Working with different data structures used in data processing
Conditional Statements
Writing logic using if-else conditions
Loops
Iterating over data efficiently
Functions
Writing reusable and modular code
Introduction to Python Libraries
Overview of commonly used libraries in data workflows
NumPy Basic Overview for Understanding array-based operations (foundation level only)
Pandas for Working with tabular data, filtering, and simple transformations
Introduction to cloud computing
Types of Cloud Models
Types of Cloud Service Models
IAAS
SAAS
PAAS
Creation of Microsoft Azure Account
Microsoft Azure Portal Overview
Introduction to IAM in Azure
Why secure identity management is critical for data engineering
What is Microsoft Entra ID (Azure AD)
Understand Tenants, Users, Groups
What is an SPN and when to use it
Differences: SPN vs user vs managed identity
How to create and authenticate with SPN
What is a Managed Identity and how it differs from SPN
What is an Azure Storage Account?
Common use cases in Data Engineering
Use cases: data lakes, file ingestion, backup
Blob types: Block, Append, Page
Folder structure & containers
Use cases: metadata storage, audit logs, config tables
Table schema: PartitionKey, RowKey, Timestamp
Azure Queue Storage
Introduction to Azure Data Lake Gen2
File systems, directories, and files
Creating and Configuring ADLS Gen2
Ingesting Data into ADLS Gen2
Accessing Data from ADLS Gen2
What is a REST API?
REST vs SOAP
Tools to Work with APIs
Understanding CRUD Operations
What is ADF and why is it used?
Key components: Pipelines, Datasets, Linked Services, Triggers, Integration Runtime
Azure Data Factory Architecture and Pipeline execution flow
Creating Linked Services by Connecting to Azure Storage, SQL, Data Lake and REST APIs
Creating and Managing Dataset Types like DelimitedText, Parquet, Binary, JSON, SQL Tables
Understand the Data Transformation Pipeline, Movement Pipeline and Activities
Data Pipeline Scheduling and Triggers
Monitoring and Debugging Pipelines
Best Practices to follow in Real World Environment
What is Microsoft Fabric?
How it is different from Microsoft Azure Data Engineering
Microsoft Fabric Components
Understand Dataflows Gen 2 in Microsoft Fabric
Explore and Integrate Dataflows Gen2 in Microsoft Fabric
Integrate Pipelines in Microsoft Fabric
Understand pipelines for data engineering
Use pipeline templates
Run and monitor Pipelines
Introduction to real-time data analytics in Microsoft Fabric
Ingest, Transform, Store and query real-time data
Visualise real-time data in Microsoft Fabric
Introduction to Microsoft Fabric eventhouse
Work with KQL effectively
Explore materialized views and stored functions for Microsoft Fabric Certification
Understand Real World lakehouse architecture for Data Engineering Roles
Use Microsoft Fabric for data ingestion, transformation, and analysis
Manage and utilize lakehouses for Microsoft Fabric Data Engineer Certificationˇ
Comprehend Delta Lake and delta tables within Fabric.
Create and handle delta tables using Spark.
Enhance the performance of delta tables.
Work on delta tables with Spark’s structured streaming.
Define data warehouses within Fabric.
Differentiate between a data warehouse and a data lakehouse.
Work on data warehouses in Microsoft Fabric.
Create and manage fact tables and dimensions in a data warehouse.
Explore strategies for loading data into a Fabric data warehouse.
Construct a data pipeline to populate a warehouse in Fabric.
Load data into a warehouse using T-SQL.
Load and transform data with Dataflows Gen 2.
Manage Data into a Fabric Datawarehouse
Protect Data into a Fabric Datawarehouse
Grasp the basics of CI/CD and their use in Microsoft Fabric.
Configure version control with Git repositories.
Leverage deployment pipelines to streamline the deployment workflow.
Automate CI/CD tasks using Fabric APIs.
Focus: Building strong fundamentals in PySpark for data ingestion, transformation, and analysis.
Focus: Understanding Databricks environment, architecture, and data governance.
Focus: Designing scalable data pipelines and real-world data engineering workflows.
What is Snowflake?
Snowflake’s use cases in data engineering
Setting up Snowflake
Creating a Snowflake account
Setting up the Snowflake environment
User roles and permissions
Navigating the Snowflake Web UI
Supported data types (BOOLEAN, INTEGER, STRING, etc.)
VARIANT data type for semi-structured data (JSON, XML, Parquet)
Tables (Permanent, Temporary, Transient)
Snowflake Architecture Deep Dive
Cloud Services Layer, Compute Layer, Storage Layer
Micro-partitioning and its benefits
How data is stored and accessed in Snowflake
Time Travel and Fail-safe
Zero Copy Cloning
Snowflake’s automatic scaling and partitioning
Loading Data into Snowflake (Data Engineering)
File formats supported by Snowflake (CSV, JSON, Parquet, Avro)
Using Snowflake’s COPY command
Using Snowflake’s SQL capabilities for ETL
Creating and managing stages
Data Transformation using Streams and Tasks
What are Streams and Tasks?
Implementing real-time ETL pipelines using Snowflake
Automation and scheduling tasks in Snowflake
Snowflake’s Integration with Data Lake and Data Science Tools
Connecting Snowflake to BI tools like Tableau, Looker, Power BI
Understanding virtual warehouses in Snowflake
Optimizing virtual warehouse size and performance
Auto-suspend and auto-resume configurations
Clustering Keys
Query profiling and performance tuning
Caching in Snowflake
Star schema vs Snowflake schema
Authentication and Authorization
Role-based access control (RBAC)
Data encryption at rest and in transit
Auditing and monitoring usage
Setting up data sharing and data masking
Access controls for sensitive data
Sharing data securely with other Snowflake accounts
Using Snowflake’s secure data sharing feature
Data sharing best practices
Introduction of Airflow
Different Components of Airflow
Installing Airflow
Understanding Airflow Web UI
DAG Operators & Tasks in Airflow Job
Create & Schedule Airflow Jobs For Data Processing
Need for Kafka
What is Kafka
Core Concepts of Kafka
Kafka Architecture
Where is Kafka Used
Understanding the Components of Kafka Cluster
Configuring Kafka Cluster
Hands-On:
CV Preperation
Interview Preperation
LinkedIn Profile Update
Expert Tips & Tricks
Our data engineer tutors are real business practitioners who hand-picked and created assignments and projects for you that you will encounter in real work, preparing you for a data engineering online certification course, aligned with a practical data engineer roadmap.
Build a production-grade insurance data platform on Microsoft Azure, leveraging Databricks for scalable data processing and transformations, and implementing medallion architecture (Bronze–Silver–Gold) to deliver clean, modeled, and business-ready data for analytics and dashboards.
Build a real-time data engineering system inspired by Uber using Apache Kafka, where a single booking triggers driver allocation, payments, notifications, and analytics instantly.
Build an enterprise-grade financial data platform using Apache Airflow, Snowflake, and dbt—transforming transaction data from PostgreSQL into actionable insights for risk, fraud, and analytics.
Design an end-to-end healthcare data platform on Microsoft Fabric, processing data from EHR systems, IoT wearables, medical imaging, and insurance claims to power AI-driven use cases like patient risk prediction, fraud detection, and operational optimization.
Build a production-grade retail analytics platform using Snowflake, transforming sales, customer, and product data into scalable, analytics-ready datasets for business insights and reporting.
Placed at Microsoft as a Data Engineer! The program gave me strong practical exposure to real-world data engineering workflows, hands-on projects, and guidance from industry professionals. It really helped me build the skills and confidence needed for this transition. Highly recommended for anyone serious about a career in Data Engineering.
Transitioned from traditional Big Data technologies into modern Data Engineering workflows with the help of this program. The practical learning approach, real-world projects, and exposure to new-age tools helped me strengthen my skills in modern data engineering. Highly recommended for professionals looking to upgrade from Big Data to cloud and modern Data Engineering technologies.
The Azure Data Engineering program gave me strong practical exposure to real-world data engineering workflows, cloud technologies, and hands-on projects. The knowledge and experience I gained through the program helped me develop the skills and confidence required to secure this opportunity. Highly recommended for anyone serious about building a career in Data Engineering.
I had an excellent experience with Prepzee’s Data Engineer course. The content was well-structured, practical, and aligned with current industry requirements. The hands-on approach helped me understand real-world use cases effectively. A special thanks to Ajitesh, whose clear explanations, industry insights, and willingness to answer questions made the learning experience even more valuable.
The program helped me strengthen my expertise in modern Azure Data Engineering with a strong focus on practical implementation and real-world workflows. The hands-on projects, industry-oriented approach, and guidance from working professionals made the learning highly valuable. A great program for professionals looking to upskill in modern cloud and data engineering technologies.
I have completed the Data Engineering program with Prepzee, where I learned Azure, Snowflake, DBT, and Airflow. It was an amazing experience with a strong focus on practical learning. Special thanks to our trainer, Aneel, who provided great support during the lab sessions. Prepzee is the best platform for beginners who want to become experts in Data Engineering.
Coming from a PostgreSQL DBA background, I wanted to expand my skills into modern Data Engineering. This program gave me hands-on experience with Azure, Databricks, Snowflake, Airflow, and real-world data engineering workflows. The practical projects and structured learning helped me understand cloud-based data platforms and strengthened my ability to contribute to data engineering initiatives.
Prepzee has been a great partner in upskilling employees. Their training content is well-structured, and the LMS recordings made it easy to review sessions anytime. The trainer was knowledgeable and always ready to help. I would highly recommend Prepzee to anyone looking to enhance their skills.
After several years in software development, I wanted to transition into Data Engineering. This program provided hands-on experience with Azure, Databricks, Snowflake, Airflow, Kafka, and real-world projects. The practical learning approach helped me build industry-relevant skills and played a key role in my successful transition to a Data Engineer role and later growth into a Senior Data Engineer position.
28/06/2026 - 01/11/2026
10:00 am TO 1:00 pm IST (GMT +5:30)
Online(Sat-Sun) 
12 Hours left at this price!
Get Certified after completing Data Engineer full course with Prepzee
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Data Transformation 
