Azure Data Engineering Course in Patiala
Cloud computing and big data technologies are revolutionizing how organizations manage and analyze their data. An Amazon Web Services (AWS) Data Engineering Course in Patiala equips professionals to construct secure, scalable data pipelines using this service at Amazon Web Services (AWS).
- English
- English, Hindi
Upcoming Batch Weekdays!!!
Starting from Upcoming Weekend!
09:00 pm – 11:00 pm Weekends
Fully Interactive Classroom Training
- 90 Hours Online Classroom Sessions
- 11 Module 04 Projects 5 MCQ Test
- 6 Months Complete Access
- Access on Mobile and laptop
- Certificate of completion
65,000 Students Enrolled
What we will learn in Azure Data Engineering course Patiala in Palin analytics
- Cloud Computing & Microsoft Azure
- Azure Data Engineering Architecture
- Data Storage on Azure
- Data Ingestion using Azure Data Factory
- Data Processing with Azure Databricks & Spark
- Data Warehousing with Azure Synapse Analytics
- SQL & Python for Azure Data Engineering
- Data Security, Governance & Monitoring
- Performance Optimization & Cost Management
In our Azure Data Engineering course in Patiala, you will gain hands-on experience building secure, scalable, and high-performance cloud data pipelines. From fundamentals to advanced enterprise-level implementations, the course prepares you for real-world Azure data engineering roles.
Who Can Go for an Azure Data Engineering Course in Patiala?
This course is ideal for:
- Engineering & Computer Science Students
- IT Professionals
- Data Analysts
- Software Developers
- Professionals transitioning into Cloud Data Engineering
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Basic knowledge of SQL or programming is helpful but not mandatory. The course starts with core cloud fundamentals to ensure smooth learning for beginners.
Need Assistance Planning Your Path to Becoming an Azure Data Engineer in Patiala?
Our mentors help you create a customized roadmap including:
- Azure Skill Development
- Hands-On Cloud Projects
- Microsoft Certification Preparation
- Resume & Interview Preparation
- Career Transition Support
- Placement Assistance
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With flexible batches, unlimited access, and expert trainers, you can confidently step into Azure cloud data roles.
Advantages
Countless Batch Access
Industry Expret Trainers
Shareable Certificate
Learn from anywhere
Career Transition Guidance
Real-Time Projects
Industry Endorsed Curriculum
Interview Preparation Techniques
Class recordings
Course Mentor
Kushal Dwivedi
- 10 + Batches
- 4.8 Star Rating
- 859 Students Trained
- 450+ Successfully Placed
Hi, I’m Kushal Dwivedi, and I’m excited that you’re here.
Professionally, I am a Data Engineering mentor with strong industry exposure and hands-on experience in building scalable data solutions. I have successfully delivered 10+ batches and trained 859+ students, helping them understand data engineering concepts from fundamentals to advanced levels. With a 4.8-star rating and 450+ successful placements, I focus on practical learning, real-time tools, and industry use cases. In this course, you’ll learn how I combine real-world experience with structured, step-by-step teaching to help you build job-ready data engineering skills.
what is Course Content of Azure Data Engineering Course Content Patiala
Introduction to Programming
Basics of programming logic
Understanding algorithms and flowcharts
Overview of Python as a programming language
Setting Up Python Environment
Installing Python
Working with Python IDEsÂ
(Integrated Development Environments)
Writing and executing the first Python script
Python Basics
Variables and data types
Basic operations (arithmetic, comparison, logical)
Input and output (print, input)
Control Flow
Conditional statements (if, elif, else)
Loops (for, while)
Break and continue statements
Functions in Python
Defining functions
Parameters and return values
Scope and lifetime of variables
Lists and Tuples
Creating and manipulating lists
Slicing and indexing
Working with tuples
Dictionaries and Sets
Understanding dictionaries
Operations on sets
Use cases for dictionaries and sets
File Handling
Reading and Writing Files
Opening and closing files
Reading from and writing to files
Working with different file formats (text, CSV)
Error Handling and Modules
Error Handling
Introduction to exceptions
Try, except, finally blocks
Handling different types of errors
Overview of Microsoft Azure
History and evolution of Azure
Azure services and products
Azure global infrastructure
Getting Started with Azure
Creating an Azure account
Azure Portal overview
Azure pricing and cost management
Azure Core Services
Azure Virtual Machines (VMs)
Azure Storage (Blobs, Files, Queues, Tables)
Azure Networking (Virtual Network, Load Balancer, VPN Gateway)
Azure Database Services
Azure SQL Database
Azure Cosmos DB
Azure Storage
Azure Data Lake Storage
Introduction to Azure Data Factory
Overview of Azure Data
Factory and its features
Comparison with other data integration services
Getting Started with Azure Data Factory
Setting up an Azure Data Factory instance
Exploring the Azure Data Factory user interface
Data Movement in Azure Data Factory
Copying data from various sources to destinations
Transforming data during the copy process
Data Orchestration in Azure Data Factory
Creating and managing data pipelines
Monitoring and managing pipeline runs
Data Integration with Azure Data Factory
Using datasets and linked services
Building complex data integration workflows
Data Transformation in Azure Data Factory
Using data flows for data transformation
Transforming data using mapping data flows
Integration with Azure Services
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
Using Azure Data Factory with Azure Databricks for advanced data processing
Monitoring and Management
Monitoring pipeline and activity runs
Managing and optimizing data pipelines for performance
SQL Advance Queries
SQL Data Models
SQl
Overview of Azure Data
Factory and its features
Comparison with other data integration services
Getting Started with Azure Data Factory
Setting up an Azure Data Factory instance
Exploring the Azure Data Factory user interface
Data Movement in Azure Data Factory
Copying data from various sources to destinations
Transforming data during the copy process
Data Orchestration in Azure Data Factory
Creating and managing data pipelines
Monitoring and managing pipeline runs
Data Integration with Azure Data Factory
Using datasets and linked services
Building complex data integration workflows
Data Transformation in Azure Data Factory
Using data flows for data transformation
Transforming data using mapping data flows
Integration with Azure Services
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
Using Azure Data Factory with Azure Databricks for advanced data processing
Monitoring and Management
Monitoring pipeline and activity runs
Managing and optimizing data pipelines for performance
Data Modeling: Designing the structure of the data warehouse, including defining dimensions, facts, and relationships between them.
ETL (Extract, Transform, Load): Processes for extracting data from source systems, transforming it into a format suitable for analysis, and loading it into the data warehouse.
Dimensional Modeling: A technique for designing databases that are optimized for querying and analyzing data, often used in data warehousing.
Star and Snowflake Schema: Common dimensional modeling schemas used in data warehousing to organize data into a central fact table and related dimension tables.
Data Mart: A subset of the data warehouse that is designed for a specific department or business function, providing a more focused view of the data.
Fact Table: A table in a data warehouse that contains the primary data for analysis, typically containing metrics or facts that can be analyzed.
Dimension Table: A table in a data warehouse that contains descriptive information about the data, such as time, location, or product details.
ETL Tools: Software tools used to extract data from various sources, transform it into a usable format, and load it into the data warehouse.
Data Quality: Ensuring that data is accurate, consistent, and reliable, often through processes such as data cleansing and validation.
Data Governance: Policies and procedures for managing data assets, ensuring data quality, and ensuring compliance with regulations and standards.
Data Warehouse Architecture: The overall structure and components of a data warehouse, including data sources, ETL processes, storage, and access layers.
Introduction to Azure Databricks
Overview of Azure Databricks and its features
Benefits of using Azure Databricks for data engineering and data science
Getting Started with Azure Databricks
Creating an Azure Databricks workspace
Overview of the Azure Databricks workspace interface
Apache Spark Basics
Introduction to Apache Spark
Understanding Spark RDDs, DataFrames, and Datasets
Working with Azure Databricks Notebooks
Creating and managing notebooks in Azure Databricks
Writing and executing Spark code in notebooks
Data Exploration and Preparation
Loading and saving data in Azure Databricks
Data exploration and basic data cleaning using Spark
Data Processing with Spark
Performing data transformations using Spark SQL and DataFrame API
Working with structured and semi-structured data
Advanced Analytics with Azure Databricks
Running machine learning algorithms using MLlib in Azure Databricks
Visualizing data and results in Azure Databricks
Optimizing Performance
Best practices for optimizing Spark jobs in Azure Databricks
Understanding and tuning Spark configurations
Integration with Azure Services
Integrating Azure Databricks with Azure Storage (e.g., Azure Blob Storage, Azure Data Lake Storage)
Using Azure Databricks in conjunction with other Azure services (e.g., Azure SQL Database, Azure Cosmos DB)
Collaboration and Version Control
Collaborating with team members using Azure Databricks
Using version control with Azure Databricks notebooks
Real-time Data Processing
Processing streaming data using Spark Streaming in Azure Databricks
Building real-time data pipelines
Introduction to Azure Synapse Analytics
What is Synapse Analytics Service?
Create Dedicated SQL Pool Explore Synapse Studio V2
Analyse Data using Apache Spark Notebook
Analyse Data using Dedicated SQL Pool
Monitor Synapse Studio
Apache Spark
Introduction of Spark
Spark Architecture
PySpark
What Our Students Say About Us
About Palin Analytics
Palin Analytics is an industry-driven cloud and analytics training institute focused on bridging academic learning with enterprise requirements. Through hands-on cloud labs, real-world projects, and expert mentorship, we prepare learners for successful careers in Azure Data Engineering and cloud analytics.
FAQ's
The best Azure Data Engineering course in Patiala offers hands-on Azure projects, expert trainers, Microsoft certification preparation, and strong placement support. Programs that include real enterprise case studies and interview training provide better career outcomes.
The course duration typically ranges from 3 to 6 months depending on batch type (weekday or weekend). Fees vary based on curriculum depth, cloud lab access, certification guidance, and placement assistance. Contact the institute for updated fee details.
No prior cloud experience is mandatory. While basic knowledge of SQL or programming is helpful, the course begins with Azure fundamentals and gradually progresses to advanced cloud data engineering concepts.
The training prepares you for Microsoft certifications such as Azure Data Engineer Associate (DP-203) and Azure Fundamentals (AZ-900). These certifications validate your expertise in Azure data services and enhance job prospects.
After completion, you can apply for roles such as Azure Data Engineer, Cloud Data Engineer, Big Data Engineer, or ETL Developer. Salaries depend on experience and skills, with cloud data professionals earning competitive packages in IT and enterprise organizations.