Professional Certificate course in Data Engineering

Learn in Hindi, Tamil and Telugu

Become Data Engineer with E&ICT Academy, IITK . Master the skill of building exceptional data systems and gain in-demand job skills like AWS, Spark, Docker, Python, SQL, etc. in a course duration of 5 months, as we have a weekday or weekend options. Work on real projects under industry experts and kickstart your career.


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Duration

5 Months
(Weekend program)

Format

Live Online Class

About E&ICT Academy, IITK Data Engineering Certification

E&ICT Academy, IITK provides a 360-degree upskilling experience for freshers and working professionals who are seeking superior job opportunities with higher pay in the data, cloud computing, and IT industries. With our data engineering certification, you will master highly valuable data skills like Python, SQL, MongoDB, Spark, AWS, Docker, etc., while learning big data, database infrastructure, data cleaning, data visualization, shell scripting, and cloud technologies. As you build a promising portfolio of industry-level capstone projects under the mentorship of industry experts, this course prepares you for a flourishing future in data engineering.

Our Prestigious Accreditations

Unlock Your Dream Job with Our Certification

50+

Instructors

1:1

Doubt Clarification

99%

Learners Most Liked

Top Reasons To Choose Data Engineering as a Career

Data Engineering Growth

37% from 2021-2031
(Creating 36,457 jobs on average)

Average Salary of Professional Data Engineer in India

₹9.55 LPA

Glassdoor

Top Product-Based Companies Hiring Data Engineer

Avg. Salary in these companies: ₹9.55 LPA

High Demand Across Industries

E-Commerce

Entertainment

Banking

Healthcare

Finance

Education

Scale Success with Lucrative Career Opportunities After Course Completion: Big Data Engineer, Data Architect, Technical Architect, Cloud Engineer, Business Intelligence Engineer, Data Warehouse Engineer

The entire technical ecosystem today relies on the efficient utilization of data. This makes the job market ripe for potential data engineers who build efficient data infrastructures to ensure the proper organization, evaluation, and safety of the huge volumes of data available. Doing an online data engineering certification will expose students and working professionals with a technical background to a plethora of phenomenal opportunities that offer higher pay. Such skilled data engineers are in high demand for their ability to create leading-edge technologies that will revolutionize the world’s outlook on data.

While data engineering is growing at a rapid pace, the number of skilled professionals in the field remains scarce. By 2030, the global market for big data engineering is expected to experience a robust growth rate of 30.7%, eventually reaching a total value of $346.24 billion. Moreover, data engineering was also the fastest-growing tech role in 2020, given its massive 50% year-over-year growth. All these statistics show that the gap between the demand and availability of data engineers is wide. A professional data engineering certification is the best way to upskill yourself and fill the gap effectively. A beginner data engineer can earn ₹5.5-7.0 LPA, which can go as high as ₹25-47 LPA based on the company, location, and experience.

Why Choose E&ICT Academy, IITK Professional Data Engineering Certification?

Get to Know Our Professional Data Engineering Course Syllabus

This program has been made specially for you by leading experts of the industry that can help you land on a High-paying Job

Introduction to DE


 

This module provides an understanding of data engineering concepts, skills, practices, and tools essential for managing data at scale.

  • What is Data Engineering?
  • Role of Data Engineers in the Industry
  • Importance of Data Engineering in Data-driven Organizations
  • Overview of Data Engineering Tools and Technologies
  • Career Paths and Opportunities in Data Engineering

Python


 

We will explore Python, a versatile and beginner-friendly programming language.Python is known for its readability and wide range of applications, from web development and data analysis to artificial intelligence and automation. 

  • Introduction to Python
  • Basic Syntax and Data Types
  • Control Structures (Conditional Statements and Looping)
  • Functions
  • Lambda Functions
  • Data Structures (Lists, Tuples, Dictionaries, Sets)
  • File Handling
  • Error Handling (try and except)
  • List Comprehensions
  • Decorators
  • NumPy
  • Pandas
  • Regex
  • Code optimisation

RDBMS


 

We will explore RDBMS (Relational Database Management System) to understand the database technology that organizes data into structured tables with defined relationships. 

  • Introduction to Databases
  • MYSQL -Introduction & Installation
  • SQL KEYS
  • PRIMARY KEY
  • FOREIGN KEY
  • UNIQUE KEY
  • Composite key
  • Normalization and Denormalization
  • ACID Properties

SQL


 

We will dive into SQL (Structured Query Language) to acquire the skills needed for managing and querying relational databases. SQL enables them to retrieve, update, and manipulate data, making it a fundamental tool for working with structured data in various applications. 

  • Basic SQL Queries
  • Advanced SQL Queries
  • Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
  • Data Manipulation Language (DML): INSERT, UPDATE, DELETE
  • Data Definition Language (DDL): CREATE, ALTER, DROP
  • Data Control Language (DCL): GRANT, REVOKE
  • Aggregate Functions (SUM, AVG, COUNT, MAX, MIN)
  • Grouping Data with GROUP BY
  • Filtering Groups with HAVING
  • Subqueries
  • Views
  • Indexes
  • Transactions and Concurrency Control
  • Stored Procedures and Functions
  • Triggers
  • Stored procedures

Mongo DB


 

We delve into MongoDB to understand this popular NoSQL database, which stores data in flexible, JSON-like documents. They learn how MongoDB’s scalability and speed make it suitable for handling large volumes of unstructured data.

  • Introduction to NoSQL and MongoDB
  • Installation and Setup of MongoDB
  • MongoDB Data Model (Documents, Collections, Databases)
  • CRUD Operations (Create, Read, Update, Delete)
  • Querying Data with MongoDB
  • Indexing and Performance Optimization
  • Aggregation Framework
  • Data Modeling and Schema Design
  • Working with Embedded Documents and Arrays
  • Transactions and Atomic Operations
  • Security in MongoDB (Authentication, Authorization)
  • Replication and High Availability
  • Sharding and Scalability
  • Backup and Disaster Recovery
  • MongoDB Atlas (Cloud Database Service)
  • MongoDB Compass (GUI for MongoDB)
  • MongoDB Drivers and Client Libraries (e.g., pymongo for Python)
  • Using MongoDB with programming languages Python
  • Real-world Applications and Case Studies

Shell Script


 

We explore shell scripting in the Linux environment, where they learn to write and execute scripts using the command-line interface. Shell scripts are text files containing a series of commands, and We discover how to automate tasks.

  • Introduction to Shell Scripting
  • Basics of Shell Scripting (Variables, Comments, Quoting)
  • Input/Output in Shell Scripts
  • Control Structures (Conditional Statements, Loops)
  • Functions and Scripts Organization
  • Command Line Arguments and Options
  • String Manipulation
  • File and Directory Operations
  • Process Management (Running Commands, Background Processes)
  • Text Processing (grep, sed, awk)
  • Error Handling and Exit Status
  • Environment Variables
  • Regular Expressions in Shell Scripts
  • Debugging and Troubleshooting
  • Advanced Topics (Signals, Job Control, Process Substitution)
  • Shell Scripting Best Practices
  • Scripting with Specific Shells (Bash, Zsh, etc.)
  • Scripting for System Administration Tasks
  • Scripting for Automation and Task Orchestration

GIT


 

We will study Git, a distributed version control system, to learn how it tracks changes in software code. Git allows collaborative development, enabling multiple people to work on the same project simultaneously while managing different versions of code.

  • Introduction to Version Control Systems (VCS) and Git
  • Installation and Setup of Git
  • Basic Git Concepts (Repositories, Commits, Branches, Merging)
  • Git Workflow (Local and Remote Repositories)
  • Creating and Cloning Repositories
  • Git Configuration (Global and Repository-specific Settings)
  • Tracking Changes with Git (git add, git commit)
  • Viewing Commit History (git log)
  • Branching and Merging (git branch, git merge)
  • Resolving Merge Conflicts
  • Working with Remote Repositories (git remote, git push, git pull)
  • Collaboration with Git (Forking, Pull Requests, Code Reviews)
  • Git Tags and Releases
  • Git Hooks
  • Rebasing and Cherry-picking
  • Git Reset and Revert
  • Git Stash
  • Git Workflows (e.g., Gitflow, GitHub Flow)

Cloud


  We delve into cloud computing, which involves delivering various computing services (such as servers, storage, databases, networking, software, and analytics) over the internet.
  • Introduction to Cloud Computing and Data Engineering
  • Overview of Cloud Providers (AWS and Azure)
  • Cloud Storage Solutions (AWS S3, Azure Blob Storage)
  • Cloud Database Services (AWS RDS, Azure SQL Database)
  • Data Warehousing in the Cloud (AWS Redshift, Azure Synapse Analytics)
  • Cloud Data Integration and ETL (AWS Glue, Azure Data Factory)
  • Big Data Processing in the Cloud (AWS EMR, Azure HDInsight)
  • Real-time Data Processing and Streaming Analytics (AWS Kinesis, Azure Stream Analytics)
  • NoSQL Databases in the Cloud (AWS DynamoDB, Azure Cosmos DB)
  • Data Lakes and Analytics Platforms (AWS Athena, Azure Databricks)
  • Machine Learning and AI Services (AWS SageMaker, Azure Machine Learning)
  • Data Visualization and BI Tools (AWS QuickSight, Azure Power BI)
  • Cloud Security and Compliance
  • Cost Management and Optimization in the Cloud
  • Best Practices for Cloud Data Engineering

System Design


 

The System Design provides an in-depth exploration of the principles, methodologies, and best practices involved in designing scalable, reliable, and maintainable software systems. 

  • Load balancer and High availability
  • Horizontal vs Vertical Scaling
  • Monolithic vs microservice
  • Distributed messaging service and Aws SQS
  • CDN (content delivery Network)
  • Caching , scalability
  • Aws API gateway

Snowflake


 

In this module, We will study Snowflake to grasp modern cloud-based data warehousing, focusing on its architecture, data sharing, scalability, and data analytics applications. 

  • Introduction to snowflake
  • Difference between Datalake,Data Warehouse,Delta Lake,Database
  • Dimension and Fact Tables
  • Roles and users
  • data modeling , snowpipe
  • MOLAP and ROLAP
  • Partitioning and indexing
  • Data mart and data cubes & caching
  • data masking
  • handling json files
  • data loading from S3 and transformation

Data cleaning


 

We will engage in data cleaning to understand the process of identifying and correcting errors or inconsistencies in datasets, ensuring data accuracy and reliability for analysis and reporting. 

  • Structured vs Unstructured Data using Pandas
  • Common Data issues and how to clean them
  • Data cleaning with Pandas and PySpark
  • Handling Json Data
  • Meaningful data transformation (Scaling and Normalization)
  • Example: Movies Data Set Cleaning

Hadoop


 

This module provides a comprehensive introduction to Hadoop, its core components, and the broader ecosystem of tools and technologies for big data processing and analytics. 

  • Introduction to Big Data
  • Characteristics and Challenges of Big Data
  • Overview of Hadoop Ecosystem
  • Hadoop Distributed File System (HDFS)
  • Hadoop MapReduce Framework
  • Hadoop Cluster Architecture
  • Hadoop Distributed Processing
  • Hadoop YARN (Yet Another Resource Negotiator)
  • Hadoop Data Storage and Retrieval
  • Hadoop Data Processing and Analysis
  • Hadoop Streaming for Real-time Data Processing
  • Hadoop Ecosystem Components:
    • HBase for NoSQL Database
    • Hive for Data Warehousing and SQL
    • Pig for Data Flow Scripting
    • Spark for In-memory Data Processing
    • Sqoop for Data Import/Export
    • Flume for Data Ingestion
    • Oozie for Workflow Management
    • Kafka for Real-time Data Streaming
  • Hadoop Security and Governance

Kafka


 

In this module, We learn about Kafka, an open-source stream processing platform. Kafka is used for ingesting, storing, processing, and distributing real-time data streams and explores Kafka’s architecture, topics, producers, consumers, and its role in handling large volumes of data with low latency.

  • Introduction to kafka
  • producer, consumer, Consumer Groups
  • topics , offset , partitions, brokers
  • Zookeeper,replication
  • Batch vs real time streaming
  • real streaming process
  • Assignment and Task

Spark


 

In this module, We will explore Spark, which is an open-source, distributed computing framework that provides high-speed, in-memory data processing for big data analytics. 

  • Introduction to Apache Spark
  • Features and Advantages of Spark over Hadoop MapReduce
  • Spark Architecture Overview
  • Resilient Distributed Datasets (RDDs)
  • Directed Acyclic Graph (DAG) Execution Engine
  • Spark Core and Spark SQL
  • DataFrames and Datasets in Spark
  • Spark Streaming for Real-time Data Processing
  • Structured Streaming for Continuous Applications
  • Machine Learning with MLlib in Spark
  • Graph Processing with GraphX in Spark
  • Spark Performance Tuning and Optimization Techniques
  • Integrating Spark with Other Big Data Technologies (Hive, HBase, Kafka, etc.)
  • Spark Deployment Options (Standalone, YARN, Mesos)
  • Spark Cluster Management and Monitoring

Airflow


 

Here, We will explore Airflow to understand its role in orchestrating and automating workflows, scheduling tasks, managing data pipelines, and monitoring job execution. 

  • Why and what is airflow
  • airflow UI
  • Run first dag
  • grid view
  • graph view
  • landing times view
  • calendar view
  • gantt view
  • Code view
  • Core concepts of airflow
  • DAGs
  • Scope
  • Operators
  • control flow
  • Task and task instance
  • Database and executors
  • ETL/ELT process implementation
  • monitoring ETL pipeline with airflow

DataBricks


 

This module provides a comprehensive introduction to DataBricks.You will learn how to leverage DataBricks to build and deploy scalable data pipelines.

  • Introduction to Databricks
  • Overview of Databricks Unified Analytics Platform
  • Setting up Databricks Environment
  • Databricks Workspace: Notebooks, Clusters, and Libraries
  • Spark Architecture in Databricks
  • Spark SQL and DataFrame Operations in Databricks Notebooks
  • Data Import and Export in Databricks
  • Working with Delta Lake for Data Versioning and Transaction Management
  • Performance Optimization Techniques in Databricks
  • Advanced Analytics and Machine Learning with MLlib in Databricks
  • Collaboration and Sharing in Databricks Workspace
  • Monitoring and Debugging Spark Jobs in Databricks
  • Integrating Databricks with Other Data Engineering Tools and Services

Prometheus


 

We will study Prometheus to explore its role as an open-source monitoring and alerting toolkit, used for collecting and visualizing metrics from various systems, aiding in performance optimization and issue detection. 

  • Introduction to Prometheus
  • Prometheus Server and Architecture
  • Installation and Setup of Prometheus
  • Understanding Prometheus UI (User Interface)
  • Node Exporters: Monitoring System Metrics
  • Prometheus Query Language (PromQL) for Aggregation, Functions, and Operators
  • Integrating Python Applications with Prometheus for Custom Metrics
  • Key Metric Types: Counter, Gauge, Summary, and Histogram
  • Recording Rules for Pre-computed Metrics
  • Alerting Rules for Generating Alerts
  • Alert Manager: Installation and Configuration

Data dog


 

We will study about Datadog, a monitoring and analytics platform for cloud-scale applications. It provides developers, operations teams, and business users with insights into their applications, infrastructure, and overall performance. 

  • Metrics
  • Dashboards
  • Alerts
  • Monitors
  • Tracing
  • Logs monitoring
  • Integrations

Docker


 

In this module, we will cover Docker, an open-source platform used to develop, ship, and run applications in containers. Containers are lightweight, portable, and self-sufficient units that package an application along with its dependencies, libraries, and configuration files, enabling consistent deployment across different environments. 

  • What is docker
  • Installation of docker
  • Docker images , containers
  • Docker file
  • Docker volume
  • Docker registry
  • Containerizing applications with docker hands-on

Kubernetes


 

This module provides a comprehensive introduction to Kubernetes, an open-source container orchestration platform for automating deployment, scaling, and management of containerized applications.

  • Nodes
  • Pods
  • ReplicaSets
  • Deployments
  • Namespaces
  • Ingress

Sharpen your skills in:

Enhance Your Resume with Industry Projects

Enhance Your Resume with Industry Projects

Enhancing E-Commerce Agility With Advanced ETL Pipeline

The project aims to build an end-to-end automated data processing workflow that handles data uploads from the Order and Returns teams, performs a join operation using Glue & PySpark, stores the joined data in Redshift, and sends notifications about the pipeline's status using SNS.

Prometheus Monitoring and Alerting for Multiple EC2 Instances in Multiple Accounts with Slack Integration

This project aims to create a centralized monitoring system for EC2 instances across multiple AWS accounts. By using Prometheus and Slack integration, it enhances visibility, enables timely troubleshooting, and boosts infrastructure reliability and performance.

Optimizing Data Management with a Data Migration and Transformation Solution for HIVE Data Warehouses

This project aims to create a centralized monitoring system for EC2 instances across multiple AWS accounts. By using Prometheus and Slack integration, it enhances visibility, enables timely troubleshooting, and boosts infrastructure reliability and performance.

Designing an Automatic Data Collection and Storage System with AWS Lambda and Slack Integration for Server Availability Monitoring and Slack Notification

This project aims to create a centralized monitoring system for EC2 instances across multiple AWS accounts. By using Prometheus and Slack integration, it enhances visibility, enables timely troubleshooting, and boosts infrastructure reliability and performance.

Designing an Automatic Data Collection and Storage System with AWS Lambda and Slack Integration for Server Availability Monitoring and Slack Notification

This project aims to create a centralized monitoring system for EC2 instances across multiple AWS accounts. By using Prometheus and Slack integration, it enhances visibility, enables timely troubleshooting, and boosts infrastructure reliability and performance.

Learn From Our Top Data Engineering Experts

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Professional Data Engineering Certification

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Professional Data Engineer Certification with Placement Guidance

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Who Can Apply for the Professional Data Engineering Certification?

  • Fresh graduates interested in joining the data and advanced technology fields

  • Job aspirants with at least a bachelor’s degree and a keen interest in data engineering

  • Early professionals looking for a career switch into a data engineering role

Why Choose E&ICT Academy, IIT Kanpur for Learning Professional Data Engineering?

E&ICT Academy, IITK career programs are project-based online boot camps that focus on bestowing job-ready tech skills through a comprehensive course curriculum instructed in regional languages for the comfort of learning the latest technologies.

  • E&ICT Academy, IIT-K Certification

Highlight your portfolio with skill certifications from E&ICT Academy, IIT-K that validate your skills in Advanced Programming & Globally recognized certifications in other latest technologies of Data Science.

  • Vernacular Upskilling

Ease your upskilling journey by learning the high-end skills of Data Engineering in your preferred native languages such as हिंदी & தமிழ் and Telugu.

  • Industry Experts’ Mentorship

Get 360-degree career guidance from mentors with expertise & professional experience from world-famous companies such as Google, Microsoft, Flipkart & other 600+ top companies.

Frequently Asked Questions

No, a basic level of programming is preferred, but it is not mandatory to get started in the E&ICT Academy, IITK Data Engineering Program. You can start learning from scratch & still master core data engineering courses in a jiffy.

Yes! Data engineering is a brilliant career for people with an interest in high-tech fields like AI, machine learning, metaverse, etc. After learning data engineering, you can easily secure a high-paying job given the huge demand for proficient engineers and experts who can handle large volumes of data to fuel the vehicles of futuristic technologies that solve modern problems. Given that, becoming a highly skilled data engineer is bound to open many doors for you.

Even freshly graduated data engineers will earn an average annual salary of ₹9 Lakhs. With more specialization, experience, and better skills, data engineers can earn as high as ₹47 LPA.

You can become a data engineer by enrolling in a professional data engineering certificate course online. This E&ICT Academy, IITK and AWS-certified professional data engineering course will comprehensively cover all the in-demand tools and skills that will help you accelerate your data engineering careers. You’ll be guided by industry experts and provided with guaranteed placement support to crack your dream role as a professional data engineer.

You can finish the E&ICT Academy, IITK Data Engineering course in 5 months by joining the weekend batch to gain top-notch data skills and develop a competitive advantage over other engineers.

Yes! There are multiple online platforms and organizations that offer online certificate courses in data engineering. One can easily learn the basics of data engineering from these online courses that offer both LIVE and recorded content. However, if you are looking for an all-around online course with hands-on learning and great projects that will help you launch your career in Data Engineering with E&ICT Academy, IITK is the perfect course for you. It provides the flexibility of learning in a regional language like Tamil alongside Hindi and English. Top industry experts will shape your skills, and will extend its unwavering support to help you secure a job post-course completion. You’ll gain industry-grade skills, from fundamental to advanced, and step out as a certified data engineering professional.

You will receive a globally recognized skill certificate accredited by E&ICT Academy, IITK which will solidify your credibility and skills exponentially.

Data engineering is the technique of designing and creating systems that can efficiently collect, store, and interpret data for analytical or operational purposes. It is an aspect of data science that focuses on practical data applications.

To keep the chances fair, we provide a Pre-Bootcamp session for Our Class where interested students will be given a little overview of the course structure and demo classes, which will enable them to know if they're ready for the program. A small eligibility test is conducted right after the Pre-Bootcamp, which will provide you with a final ticket to be part of the Our Bootcamp.

With the objective of creating as many job opportunities as possible for our students, we do intend to help every student who is willing to “make the extra catching up needed” in terms of programming & development logic.

We assess this via a comprehensive Pre-Bootcamp where you can figure out if you're ready for the our Bootcamp. In case you are unable to clear the eligibility criteria, don't worry, our mentors will charter a few self-paced E&ICT Academy, IITK courses to help you become ready.

As part of the Capstone Project, the participants are required to build their own application by the end of the course, which can be added to their GitHub profile for professional development. With an emphasis on learning by doing, the bootcamp course helps participants work on building a real-world application from the first week itself. In the end, the participant builds their own application, understands the data pipeline, and learns the best practices and tools in data analytics, visualization, etc.

Our Classes are flexible to suit your day-to-day life so that they do not hamper your work or education. The program will be conducted in the format of LIVE online classes on weekends for five months.

At Our Class, we create job-ready skills that empower achievement. The real-world capstone projects in the Data Engineering Course go far beyond step-by-step guides, cultivating the critical thinking required for workplace relevance.

The tools and technologies covered in this program include Python, SQL, Shell Script, Orchestrator, Cloud Services, Big Data, Data Cleaning, Data Visualization, etc.

Still have queries? Contact Us

Request a callback. An expert from the admission office will call you in the next 24 working hours. You can also reach out to us at support_ifacet@iitk.ac.in or +91-9219972805, +91-9219972806