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Data Engineering Course

This Data Engineering course in association with MITx MicroMasters is designed by top domain experts to help you master core Data Engineering skills like Python, SQL, AWS, Spark, Kafka, etc. through multiple Data Engineering courses & real-time projects. Learn from top faculty at MIT & get MITxMicroMasters certified.

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Upskill for Your Dream Job

Learning Format

Online Bootcamp

Live Classes

7 Months

Career Services

by Intellipaat



No Cost EMI Starts

at ₹8,000/month*

Data Engineering Course Overview

Our Data Engineering course will provide you with in-depth knowledge in SQL, Python, data pipelines, data transformation, Spark, and cloud services of AWS and Azure. Multiple Data Engineering courses and real-world projects help you master core concepts & skills like creating production-ready ETL and pulling data from multiple data sources, building cloud data warehouses, Data Modeling, etc.

Data Engineer Course Key Highlights

Career Guidance
7 Months of Live Sessions by Industry Experts
200 Hrs of Self-paced Videos
One-on-One with Industry Mentors
24*7 Support
50+ Industry Projects & Case Studies
Integrated with MITxMicroMasters
E-learning Videos from MIT faculty
Flexible Schedule
Lifetime Free Upgrade
Soft Skills Essential Training
Dedicated Learning Management Team

Free Career Counselling

We are happy to help you 24/7

About MIT and MIT IDSS

The Institute for Data, Systems, and Society (IDSS) is a cross-disciplinary unit made up of faculty from across the Massachusetts Institute of Technology (MIT). IDSS advances education and research in data analysis, statistics, and machine learning, and applies these tools in collaboration with social scientists, community, and policymakers to address complex societal challenges.

On the completion of this data engineer certification program, you will:

  • Receive an industry-recognized Certification in Data Engineering from Intellipaat.
  • Receive a course completion certification by MITxMicromasters on the completion of the modules by MIT.

To know more about the MIT IDSS, click here

Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of the MITx.

Career Transition

57% Average Salary Hike

45 LPA Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Who Can Apply for the Data Engineering Course?

  • Freshers and Undergraduates willing to pursue a career in data engineering
  • Anyone looking for a career transition to data engineering
  • IT professionals
  • Experienced professionals willing to learn data engineering
  • Technical and non-technical professionals with basic-level programming knowledge can also apply
  • Project Managers
Who can aaply

What Roles Does a Data Engineer Play?

Big Data Engineer

They design and build complex data pipelines and have expert knowledge in coding using Python, etc. These professionals collaborate and work closely with data scientists to run the code using various tools such as the Hadoop ecosystem, etc.

Data Architect

They are typically the database administrators and are responsible for data management. These professionals have in-depth knowledge of databases, and they also help in business operations.

Business Intelligence Engineer

They are skilled in data warehousing and create dimension models for loading data for large-scale enterprise reporting solutions. These professionals are experts in using ELT tools and SQL.

Data Warehouse Engineer

They are responsible for looking after the ETL processes, performance administration, dimensional design, etc. These professionals take care of the full back-end development and dimensional design of the table structure.

Technical Architect

They design and define the overall structure of a system to improve the business of an organization. The job role of these professionals involves breaking large projects into manageable pieces.

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Skills to Master


No SQL (MongoDB)

Data Warehousing




Python Programming



Spark Streaming





Apache Airflow


S3 Glacier




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Tools to Master

intellipaat intellipaat intellipaat intellipaat intellipaat intellipaat
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Data Engineer Training Curriculum

Live Course Self Paced

Introduction to SQL

  • SQL basics
  • Relational databases
  • Data manipulation language (DML) and data definition language (DDL)
  • SQL data types and constraints

Database Normalization and Entity Relationship Model

  • Database normalization
  • Entity Relationship (ER) model
  • Normal forms (1NF, 2NF, 3NF)
  • Entity Relationship Diagrams (ERDs)
  • Cardinality and relationships

SQL Operators

  • Comparison operators
  • Logical operators
  • Arithmetic operators
  • String operators
  • NULL-related operators

Join, Tables, and Variables in SQL

  • SQL joins (inner join, outer join, cross join)
  • Working with tables (creating, altering, dropping)
  • Using variables in SQL queries
  • Query optimization and performance tuning

Deep Dive into SQL Functions

  • Scalar functions
  • Aggregate functions
  • String functions
  • Date and time functions
  • Conversion functions

Subqueries in SQL

  • Subquery basics
  • Correlated subqueries
  • Subquery in the WHERE clause
  • Subquery in the FROM clause
  • Subquery optimization techniques

SQL Views, Functions, and Stored Procedures

  • Creating and using views
  • User-defined functions (UDFs)
  • Stored procedures
  • Parameters and variables in functions and procedures
  • Error handling and transactions in stored procedures

User-defined Functions in SQL

  • Creating and using UDFs
  • Scalar UDFs
  • Table-valued UDFs
  • Inline UDFs
  • Performance considerations for UDFs

SQL Optimization and Performance

  • Query optimization techniques
  • Indexing strategies
  • Query execution plans
  • Analyzing query performance
  • Performance tuning tips and best practices

SQL Parsing

  • Query parsing process
  • SQL statement components
  • Query optimization and execution plan generation
  • Understanding query parsing errors and messages

Managing Database Concurrency

  • Concurrency control in databases
  • Locking mechanisms
  • Transaction isolation levels
  • Deadlock detection and prevention
  • Managing concurrent database operations specific exceptions

Python Basics

  • Introduction to Python programming language
  • Variables, data types, and operators

OOPs Concept

  • Object-Oriented Programming (OOP)
  • Classes, objects, inheritance, encapsulation, abstraction, polymorphism


  • NumPy library
  • Arrays and array operations
  • Indexing, slicing, and reshaping arrays
  • Mathematical functions in NumPy


  • Pandas library for data analysis
  • Series and DataFrame data structures
  • Data cleaning and preprocessing with Pandas

Data Visualization

  • Introduction to data visualization
  • Matplotlib library for basic plotting
  • Seaborn library for statistical data visualization
  • Plotly library for interactive visualizations

File Handling

  • Reading and writing files in Python
  • Text file manipulation and processing
  • File manipulation and file formats

Exception Handling

  • Introduction to exception handling
  • Errors, exceptions, try-except blocks
  • Handling specific exceptions

Regular Expressions Fundamentals

  • Introduction to regular expressions
  • Pattern matching and searching in text
  • Matching and replacing patterns

Introduction to Linux

  • Overview of Linux and its key features.
  • Understanding the Linux file system hierarchy.
  • Different distributions of Linux and their characteristics.

File System Navigation

  • Navigating the Linux file system using commands like cd, ls, and pwd.
  • Working with directories and files.
  • Understanding file permissions and ownership.

File and Text Manipulation

  • Reading, creating, and modifying text files using commands like cat, echo, and nano.
  • Searching for patterns in files using grep.
  • Sorting and filtering text using commands like sort and awk.

User and Group Management

  • Creating and managing user accounts and groups.
  • Assigning permissions and privileges to users and groups.
  • Understanding user authentication and password management.

Process Management

  • Managing processes using commands like ps, top, and kill.
  • Running processes in the background and foreground.
  • Monitoring system resources and identifying resource-intensive processes.

System Configuration and Networking

  • Configuring network settings and interfaces.
  • Managing network connections and troubleshooting network issues.
  • Modifying system configurations using configuration files.

System Monitoring and Logging

  • Monitoring system performance and resource usage.
  • Analyzing system logs for troubleshooting and debugging.
  • Understanding log rotation and log file management.

Shell Scripting

  • Introduction to shell scripting and shell programming.
  • Writing and executing shell scripts.
  • Automating repetitive tasks using scripts.

Security and Permissions

  • Understanding Linux security mechanisms.
  • Setting file and directory permissions.
  • Managing user access and implementing security best practices.

Advanced Linux Commands

  • Exploring advanced Linux commands and utilities.
  • Working with compressed files and archives.
  • Performing remote operations using SSH and SCP.

Introduction to Data Warehousing

  • Overview of data warehousing concepts
  • Benefits and use cases of data warehousing
  • Data warehousing architecture and components
  • Introduction to ETL (Extract, Transform, Load) processes

Data Cleaning Techniques

  • Understanding data quality issues and challenges
  • Data cleaning methodologies and best practices
  • Handling missing data and outliers
  • Data deduplication and data normalization
  • Data validation and data cleansing techniques

SQL Connectors and Data Integration

  • Introduction to SQL connectors
  • Connecting to various data sources (relational databases, flat files)
  • Extracting data from different sources using SQL
  • Loading data into data warehouse using SQL connectors

Building Data Pipelines

  • Introduction to data pipelines in data warehousing
  • Overview of data integration and transformation
  • Designing and implementing data pipelines using SQL
  • Monitoring and managing data pipelines

Data Quality and Governance in Data Warehousing

  • Importance of data quality in data warehousing
  • Data quality dimensions and metrics
  • Data profiling and data quality assessment

Performance Optimization in Data Warehousing

  • Performance challenges in data warehousing
  • Indexing strategies and optimization techniques
  • Query optimization in data warehousing
  • Monitoring and troubleshooting performance issues

Emerging Trends in Data Warehousing

  • Cloud-based data warehousing solutions (e.g., Amazon Redshift, Google BigQuery)
  • Data lakes and data warehouse integration
  • Real-time data warehousing and streaming data processing
  • Data virtualization and data federation in data warehousing

Basic Concepts of Data Modelling

  • Introduction to data modeling
  • Conceptual, logical, and physical models
  • Best practices in data modeling

Business Data Requirements – Entities and Classes

  • Identifying business data requirements
  • Entities and classes in data modeling
  • Entity-relationship modeling (ER modeling)

Business Data Requirements – Attributes

  • Types of attributes in data modeling
  • Attribute domains and data types
  • Constraints and naming conventions for attributes

How To Link Things Together – Relationships

  • Types of relationships in data modeling
  • Cardinality and optionality in relationships
  • Role names and associative entities

Requirements Analysis

  • Gathering and analyzing data requirements
  • Functional and non-functional requirements
  • Documentation of data requirements

Conceptual Data Modeling

  • Creating a conceptual data model
  • Entity-relationship diagrams (ERDs)
  • Representing business processes and data flows

Logical Data Modeling

  • Transforming conceptual model to logical model
  • Tables, columns, normalization
  • Primary and foreign keys in data modeling

Physical Data Modelling

  • Converting logical model to physical model
  • Database schema, tables, denormalization
  • Indexing and partitioning strategies

Data Modelling Tools and Techniques

  • Overview of data modeling tools (e.g., ERwin, PowerDesigner)
  • Reverse engineering and forward engineering
  • Techniques for data model manipulation

Data Modelling Documentation and Communication

  • Documenting data models using notations
  • Data dictionaries and model diagrams
  • Presenting data models to stakeholders
  • Effective communication of data models

Introduction to  Hadoop Ecosystem

  • Overview of big data and parallel processing
  • Hadoop architecture and components
  • Hadoop Distributed File System (HDFS)
  • MapReduce and YARN frameworks

HDFS (Hadoop Distributed File System)

  • Understanding the Hadoop file system
  • HDFS architecture and data storage
  • HDFS commands for file operations
  • File replication and fault tolerance in HDFS

Apache Hive for Data Warehousing and Querying

  • Introduction to Apache Hive
  • Hive architecture and components
  • HiveQL: SQL-like querying language for Hive
  • Creating and managing tables in Hive
  • Hive data types and file formats

Apache Impala for High-Performance 

  • Introduction to Apache Impala
  • Impala architecture and components
  • Impala  for interactive querying
  • Impala integration with Hadoop ecosystem
  • Performance optimizations in Impala

Working with Big Data using Hadoop and Hive

  • Data ingestion into Hadoop using Hive
  • Data transformation and processing with Hive
  • Hive queries for data analysis and reporting
  • Joining and aggregating data in Hive
  • Advanced Hive features and functions

Integrating Hive with Other Tools and Systems

  • Integrating Hive with Apache Spark
  • Hive integration with Apache HBase
  • Hive integration with data visualization tools
  • Real-time data processing with Hive

Working with Cassandra in Hadoop Ecosystem

  • Introduction to Apache Cassandra
  • Cassandra architecture and data model
  • Data ingestion into Cassandra from Hadoop
  • Integrating Cassandra with Hive and Impala
  • Querying and managing data in Cassandra

Advanced Topics in Hadoop and Hive

  • Introduction to Hadoop ecosystem components 
  • Working with NoSQL databases in Hadoop
  • Monitoring and troubleshooting in Hadoop
  • Future trends and advancements in Hadoop and Hive

AWS Basics

  • Introduction to AWS (Amazon Web Services)
  • Overview of cloud computing and its benefits
  • Understanding AWS services and solutions
  • Basics of AWS account setup and management

Amazon Kinesis

  • Introduction to Amazon Kinesis
  • Real-time streaming data processing
  • Creating Kinesis data streams
  • Collecting, processing, and analyzing streaming data

Amazon MSK (Managed Streaming for Apache Kafka)

  • Introduction to Amazon MSK
  • Apache Kafka basics
  • Setting up and managing MSK clusters
  • Streaming data ingestion and processing with MSK

AWS Glue

  • Introduction to AWS Glue
  • Data cataloging and metadata management
  • Extract, Transform, Load (ETL) with Glue
  • Building and managing ETL pipelines using Glue

Amazon EMR (Elastic MapReduce)

  • Introduction to Amazon EMR
  • Distributed big data processing with EMR
  • Setting up EMR clusters and configuring resources
  • Running data processing jobs using EMR

Amazon S3 (Simple Storage Service)

  • Introduction to Amazon S3
  • S3 bucket creation and management
  • Uploading, downloading, and managing objects in S3
  • Integrating S3 with other AWS services

Amazon S3 Glacier

  • Introduction to Amazon S3 Glacier
  • Long-term storage and archiving with Glacier
  • Creating and managing Glacier vaults
  • Retrieving and managing data from Glacier


  • Introduction to Amazon DynamoDB
  • NoSQL database fundamentals
  • Creating and managing DynamoDB tables
  • Querying and scanning data in DynamoDB

AWS Redshift

  • Introduction to AWS Redshift
  • Columnar data warehousing with Redshift
  • Provisioning and managing Redshift clusters
  • Loading, querying, and optimizing data in Redshift

Amazon Athena

  • Introduction to Amazon Athena
  • Serverless querying and analysis of data
  • Creating tables and querying data with Athena
  • Optimizing performance and cost in Athena

Amazon QuickSight

  • Introduction to Amazon QuickSight
  • Business intelligence and data visualization
  • Creating visualizations and dashboards in QuickSight
  • Sharing and presenting insights from QuickSight

Introduction to Microsoft Azure

  • Introduction to Microsoft Azure
  • Introduction to ARM & Azure Storage
  • Azure Virtual Machines
  • Azure Networking – I
  • Azure Networking – II

Authentication, Authorization, and Monitoring

  • Authentication and Authorization in Azure using RBAC
  • Microsoft Azure Active Directory
  • Azure Monitoring

Data Storage and Integration

  • Data Storage in Microsoft Azure
  • Non-Relational Data Stores and Azure Data Lake Storage
  • Data Lake and Azure Cosmos DB
  • Relational Data Stores
  • Why Azure SQL?
  • Azure Data Lake Storage Gen2 and Data Streaming Solution
  • Data Integration with Microsoft Azure Data Factory
  • Designing Data Flows in Azure
  • Using Azure Data Factory Pipelines to Copy Data
  • Monitor Azure Data Factory using Azure Monitor

Azure Synapse Analytics and Databricks

  • Introduction to Microsoft Azure Synapse Analytics
  • Using Azure Synapse Analytics to Query Data Lake
  • Optimizing Dedicated SQL Pools in Azure Synapse Analytics
  • Data Warehousing with Microsoft Azure Synapse Analytics
  • Data Engineering with MS Azure Synapse Apache Spark Pools
  • Operational Analytics with Microsoft Azure Synapse Analytics
  • Handling Slowly Changing Dimensions With Azure Synapse Analytics Pipelines
  • Microsoft Azure Databricks for Data Engineering
  • Running Spark on Azure Databricks
  • Using Azure Databricks to Import and Analyze Data
  • Introduction to Delta Lake on Azure Databricks

Azure Stream Analytics, and Azure Service Bus

  • Introduction to Azure Stream Analytics
  • Monitoring & Security
  • Azure Functions
  • Azure Service Bus

Introduction to Apache Spark

  • Overview of big data processing and Apache Spark
  • Spark architecture and components
  • Introduction to Resilient Distributed Datasets (RDDs)
  • Understanding Spark’s distributed computing model

PySpark SQL and Data Frames

  • Introduction to PySpark SQL module
  • Working with structured and semi-structured data
  • Data exploration and analysis using DataFrames
  • Querying and manipulating data with SQL-like syntax

Apache Kafka and Flume

  • Introduction to Apache Kafka and Apache Flume
  • Streaming data ingestion using Kafka and Flume
  • Integration of Kafka and Flume with PySpark
  • Real-time data processing and analysis

PySpark Streaming

  • Introduction to PySpark Streaming
  • Processing live data streams with PySpark
  • Windowed operations and aggregations
  • Real-time analytics using PySpark Streaming

Introduction to PySpark Machine Learning

  • Overview of PySpark Machine Learning (MLlib)
  • Machine learning concepts and algorithms in PySpark
  • Building and evaluating machine learning models with PySpark
  • Feature engineering and data preprocessing

Introduction to Power BI

  • Overview of Power BI and its role in data engineering
  • Introduction to self-service business intelligence
  • Understanding Power BI components: Power BI Desktop, Power BI Service, Power BI Mobile

Data Extraction

  • Connecting to various data sources in Power BI
  • Importing data from databases, files, web services, and other sources
  • Configuring data refresh options and scheduling data updates

Data Transformation – Shaping & Combining Data

  • Understanding data transformation concepts in Power BI
  • Applying data shaping techniques: filtering, sorting, and removing duplicates
  • Combining multiple data sources using merging and appending operations

Data Modeling & DAX (Data Analysis Expressions)

  • Introduction to data modeling in Power BI
  • Creating relationships between tables
  • Implementing calculations and measures using DAX formulas

Data Visualization with Analytics

  • Creating interactive visualizations using Power BI visuals
  • Formatting and customizing visual elements
  • Applying data analytics techniques: forecasting, clustering, and trend analysis

Power BI Service (Cloud), Q & A, and Data Insights

  • Publishing and sharing Power BI reports on the Power BI Service
  • Configuring security and access controls in Power BI Service
  • Using Q & A feature for natural language queries
  • Utilizing data insights and recommendations in Power BI

Power BI Settings, Administration & Direct Connectivity

  • Configuring Power BI settings and options
  • Managing workspaces, datasets, and reports in Power BI Service
  • Implementing direct connectivity to on-premises and cloud data sources

Embedded Power BI with API & Power BI Mobile

  • Integrating Power BI reports and dashboards into custom applications using Power BI API
  • Building and embedding Power BI visuals in web and mobile applications
  • Accessing and interacting with Power BI content on mobile devices

Power BI Advance & Power BI Premium

  • Exploring advanced features and capabilities in Power BI
  • Implementing advanced data modeling techniques
  • Introduction to Power BI Premium features and benefits

Introduction to SSIS and Data Engineering

  • Overview of SSIS and its role in data engineering
  • Understanding the ETL (Extract, Transform, Load) process
  • Introduction to data engineering concepts and best practices

SSIS Architecture and Components

  • Understanding the architecture of SSIS
  • Exploring SSIS components: Control Flow, Data Flow, Connection Managers
  • Overview of tasks, transformations, and data sources in SSIS

SSIS Package Development

  • Creating a new SSIS package in SQL Server Data Tools (SSDT)
  • Working with control flow tasks: Execute SQL Task, File System Task, Script Task
  • Configuring properties and expressions in SSIS packages

Data Extraction in SSIS

  • Extracting data from various sources: relational databases, flat files, Excel
  • Configuring connection managers for different data sources
  • Using SSIS Data Flow Task to extract data into staging area

Data Transformation in SSIS

  • Understanding data transformation concepts and SSIS transformations
  • Performing data cleansing, filtering, sorting, and aggregations
  • Implementing data type conversions and handling NULL values

Data Loading and Destination in SSIS

  • Configuring destination connection managers: databases, data warehouses
  • Loading data into target tables using SSIS Data Flow Task
  • Handling data loading errors and implementing data quality checks

Advanced SSIS Transformations and Tasks

  • Exploring advanced SSIS transformations: Lookup, Derived Column, Merge, Pivot
  • Working with SSIS tasks: FTP Task, XML Task, Script Component
  • Implementing complex data transformations and business rules

Error Handling and Logging in SSIS

  • Implementing error handling and data validation in SSIS
  • Configuring error outputs and error handling options
  • Enabling logging and monitoring package execution

SSIS Package Deployment and Execution

  • Deploying SSIS packages to SQL Server Integration Services Catalog
  • Configuring package parameters and environment variables
  • Scheduling and executing SSIS packages using SQL Server Agent

Performance Tuning and Optimization in SSIS

  • Identifying performance bottlenecks in SSIS packages
  • Applying performance tuning techniques: buffer size, parallelism, caching
  • Optimizing data loading and transformation processes

Advanced SSIS Features and Integration

  • Working with SSIS expressions and variables
  • Implementing event handling and package configurations
  • Integrating SSIS with other technologies and platforms (e.g., Azure, Hadoop)

SSIS Deployment and Maintenance

  • Managing and monitoring deployed SSIS packages
  • Package versioning and promotion between environments
  • Troubleshooting and debugging SSIS packages

Introduction to DevOps

  • Understanding the DevOps culture and principles
  • Benefits of DevOps in data engineering
  • Overview of DevOps tools and practices


  • Introduction to version control systems
  • Git fundamentals: repositories, branches, commits
  • Collaborative development with Git
  • Git workflows: branching strategies, pull requests, merging


  • Introduction to containerization and Docker
  • Docker architecture and components
  • Building Docker images for data engineering applications
  • Container orchestration with Docker Compose


  • Introduction to Kubernetes for container orchestration
  • Kubernetes architecture and components
  • Deploying and managing applications with Kubernetes
  • Scaling, monitoring, and updating applications in Kubernetes


  • Introduction to Jenkins for continuous integration and continuous delivery
  • Jenkins installation and configuration
  • Building and automating data engineering pipelines with Jenkins
  • Integration with Git, Docker, and Kubernetes
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Intellipaat reserves the right to modify, amend or change the structure of module & the curriculum, after due consensus with the university/certification partner.

Program Highlights

Live Session across 7 months
1:1 Industry Mentorship
50+ Industry Projects & Case Studies
24*7 Support

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.


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Career Services By Intellipaat

Career Services

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These guided sessions and that will help you stay on track with your up-skilling objective.

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage.

Mock Interview Preparation

After 80% of the course completion.

Students will go through several mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, experience, and future career aspirations.

3 Guaranteed Interviews

Upon movement to the Placement Pool

Guaranteed 3 job interviews upon movement to the placement pool after clearing the Placement Readiness Test ( PRT). Get interviewed by our 400+ hiring partners.

Access to Intellipaat Job Portal

For six months after Course Completion

Exclusive access to our dedicated job portal to apply for jobs. More than 400 hiring partners, including leading startups and product companies, are hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Our Alumni Works At

Master Client Desktop

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, and make new friends – the possibilities are endless and our community has something for everyone!


Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Submit Application

Submit Application

Tell us a bit about yourself and why you want to join this program

Application Review

Application Review

An admission panel will shortlist candidates based on their application


Application Review

Selected candidates will be notified within 3 days

Program Fee

Total Admission Fee

₹ 1,50,024 + GST

No Cost EMI Starts at

₹ 8,000

We partnered with financing companies to provide competitive finance option at 0% interest rate with no hidden costs

Financing Partners

EMI Partner

The credit facility is provided by a third party credit facility provider and any arrangement with such third party is outside Intellipaat’s purview.

Upcoming Application Deadline 8th Oct 2023

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Program Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 8th Oct 2023 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 8th Oct 2023 08:00 PM IST Weekend (Sat-Sun)

Data Engineer Training FAQs

What courses are included in this Data Engineering course?

In this Data Engineer PG program, you will learn multiple Data Engineering courses along with case studies and project work.

Online Instructor-led:

Course 1: Preparatory Sessions — Python and Linux
Course 2: Data Wrangling with SQL
Course 3: Introduction to Data Engineering
Course 4: Big Data Engineering with Apache Spark and Kafka
Course 5: Data Modeling
Course 6: Cloud Data Warehouses
Course 7: Mastering ETL Tool — Informatica
Course 8: Data Engineering on the Cloud
Course 9: Schedule and Automate Data Pipelines with Apache Airflow
Course 10: Data Virtualization and Containerization
Course 11: Capstone Project

At the end of the course you will master the concepts and skills by working on Capstone Projects.

Apart from the live classes, you will have electives in a self-paced format:

Azure Data Factory

Intellipaat’s online Data Engineering courses will validate your skills in the domain and will add value to your resume. The real-life practical applications will help you develop a strong skill set that you can showcase to recruiters. Get the best Data Engineer certification course and excel in your data engineering career.

Data engineering can be called a branch of data science that involves preparing the data to be analyzed by data scientists and data analysts. This online data engineer training course includes practically applying data collection techniques and maintaining the organization’s data pipeline systems.

Intellipaat provides career services that include 1:1 mentorship to all the learners enrolled in this top online certification training course. MIT is not responsible for placements and career services.

On the completion of the data engineering online course, and the completion of the various projects and assignments in this program, you will receive your certification.

According to Glassdoor, the average annual salary of a certified data engineer in India is ₹850,000.

No, Anyone passionate about learning data engineering and big data engineering is welcome to join our best online courses.

It is recommended that you have a basic level of knowledge in programming or any object-oriented coding language to better understand the Data Engineering concepts.

The top companies hiring data engineers around the globe are as follows:

  • Tata Consultancy Services (TCS)
  • LTI
  • Accenture
  • IBM
  • Amazon
  • Infosys
  • Capgemini

Yes, you can easily join the data engineering courses even if you do not have technical experience or are not from a technical background. However, knowing any object-oriented programming language will be helpful.

The data engineering courses come with a duration of six months of live classes and lifelong access to course material. In this tenure, it is suggested that you devote six to seven hours a week to master the data engineering concepts taught in the online classes.

Due to the increase in the adoption of digital transformation by companies, domains like data engineering are very much in demand. Currently, there is a shortage in the supply of data engineers and their demand is increasing. Due to this, companies are ready to offer high salaries to the right candidates. Hence, the data engineering domain has a bright future.

Intellipaat provides career guidance services such as interview preparatory sessions, industry mentorship, and more for all learners enrolled in the Data Engineering courses. MIT is not responsible for placements and career services.

You can expect a salary range of Rs.7-8 lakhs from the job offer. However, it depends on how you perform in your interview. We have seen our learners achieving up to 30 LPA in salary packages.

We value the candidates who wish to learn but do not have the financial bandwidth to make an upfront payment of the fees. Hence, Intellipaat offers an easy no-cost EMI option to the candidates.

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What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work

I’m Interested in This Program

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