Data Engineering Course in Delhi
Data Engineering Course in Delhi Data engineering has quickly become one of the most in-demand career options among professionals working with large data systems and modern analytics platforms. A Data Engineering course equips learners with essential skills necessary for designing, creating, managing, and overseeing data pipelines to power analytics solutions such as machine learning. Leading institutes in Delhi offer industry-align training designed specifically to address current and future data infrastructure requirements.
- English
- English, Hindi
Upcoming Batch Weekdays!!!
Starting from Upcoming Weekend!
10:00 am – 01:00 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
- Data Engineering & Big Data Ecosystem
- Python & SQL for Data Engineering
- Data Warehousing Concepts
- ETL / ELT Data Pipelines
- Database Design & Optimization
- Big Data Tools (Hadoop & Spark)
- Cloud Platforms (AWS / Azure / GCP Basics)
- Data Streaming & Real-Time Processing
- Workflow Orchestration Tools
At our Data Engineering course in Delhi, you will acquire the skills needed to construct robust and scalable data pipelines. Starting from databases and SQL fundamentals and moving onwards towards big data frameworks, cloud platforms, and real world use cases; the course provides all the skills you need for developing data engineering pipelines that scale efficiently and seamlessly.
Who Can Go for a Data Engineering Course in Delhi
A data engineering course in Delhi can benefit engineering students, computer science graduates, working professionals such as data analysts or software developers as well as IT professionals looking for career transition. While previous programming experience will prove helpful for beginners in learning the fundamentals, stepwise guidance through fundamentals ensures everyone will make a smooth journey into data engineering roles.
Want to Discuss Your Roadmap to Become a Data Engineer in Delhi?
In these scenarios, do not hesitate to discuss with our career adviser your roadmap towards becoming one in Delhi!
Our experts create a tailored roadmap encompassing technical skill development, project-based learning, cloud exposure and certifications as well as interview preparation to help you become an accomplished Data Engineer. If you want us to call you back we offer unlimited batch access plus industry expert trainers sharing certifications so you can learn from any location! To request one please fill out this form.
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.
Data Engineering Course Content
Azure Data Engineering Course Content
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
AWS Data Engineering Course Content
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
- Amazon S3 (Simple Storage Service) for scalable object storage
- Amazon RDS (Relational Database Service) for managing relational databases
- Amazon DynamoDB for NoSQL database storage
- Amazon Redshift for data warehousing and analytics
- AWS Glue for ETL (Extract, Transform, Load) and data preparation
- Amazon EMR (Elastic MapReduce) for processing large amounts of data using Hadoop, Spark, or other big data frameworks
- Amazon Kinesis for real-time data streaming and processing
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
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
- Amazon Athena for querying data in S3 using SQL
- Amazon QuickSight for business intelligence and data visualization
- Implementing security best practices for data on AWS
- Managing data governance policies on AWS
- Monitoring data pipelines and optimizing performance and costs
- Using AWS tools for monitoring and optimizing data processing
- Hands-on experience with AWS services for data engineering
- Building data pipelines, processing data, and analyzing data using AWS
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Palin Analytics
Palin Analytics in Delhi is an esteemed analytics and data engineering training institute, focused on connecting academic learning to real world industry requirements. Through hands-on instruction, live projects, expert mentorship and expert advisement, Palin Analytics equips its learners for successful careers as data engineers.
FAQ's
Beginners with no prior programming knowledge or databases experience can also join as this course begins from SQL and Python fundamentals and progresses gradually to advanced data engineering concepts.
Our course in Data Engineering in Delhi encompasses Python, SQL, ETL/ELT pipelines, relational and NoSQL databases, Hadoop Spark cloud platforms such as AWS Azure or GCP as well as data warehousing Kafka orchestration tools along with real data engineering projects.
Course duration typically spans 3 to 6 months depending on learning mode; flexible schedules including weekday and weekend batches allow us to accommodate both students as well as working professionals.
Yes, the course entails real-world industry projects and hands-on labs where learners build data pipelines, cloud-based data systems and streaming workflows in order to gain practical work experience in this area.
Yes, upon successful completion of a course learners receive a shareable certification confirming their data engineering expertise and increasing credibility and employability within data engineering roles and analytics roles.