Professional Certificate course in Data Engineering

Learn in Hindi, Tamil and Telugu

Become Data Engineer with IFACET. 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 3-5 months, as we have a weekday or weekend options. Work on real projects under industry experts and kickstart your career.

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3 Months / 5 Months (Weekday/Weekend)


Live Online Class

Hiring Partners

600+ Companies

About IFACET’s Data Engineering Certification

IFACET 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


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Students Placed


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


Top Product-Based Companies Hiring Data Engineer

Avg. Salary in these companies: ₹9.55 LPA

High Demand Across Industries







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 IFACET's 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



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. It offers a rich
ecosystem of libraries and tools, making it a popular choice for both novice and
experienced programmers.

  • Why python?
  • Python IDE
  • Hello World Program
  • Variables & Names
  • String Basics
  • List
  • Tuple
  • Dictionaries
  • Conditional Statements
  • For and While Loop, TRY AND EXCEPT
  • Numbers and Math Functions
  • Common Errors in Python



We will dive into some advanced concepts like comprehension, file
handling, regular expressions, object oriented programming, pickling and
many more essential concepts.

  • FUNCTIONS , Lambda, Filters and Map
  • Functions as Arguments
  • List Comprehension
  • Debugging in Python
  • Class and Objects
  • Inheritance, Polymorphism, Abstractions
  • Liner and non-Linear Data structures
  • Singly, doubly,ciculer Linked list, Binary tree
  • Bubble, insertion, merge, quick, Heap sorting
  • File Handling (Text, Json, csv)
  • Iterators
  • Pickling, Multi Threading



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

  • Joins
  • SQL Outer Join
  • SQL Left Join
  • SQL Right Join
  • SQL Full Join
  • SQL Cross Join
  • Integrating Python SQL
  • Window functions (rank, dense rank, row number, etc)
  • Data Types, Variables
  • Constants
  • Conditional Structures (IF, CASE, GOTO, and NULL)
  • Stored procedures and Function
  • subqueries



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

  • Composite key
  • Triggers
  • Indexes
  • Transaction
  • Views

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

  • CAP Theorem
  • Structured and unstructured data
  • OLTP vs OLAP
  • Schema vs Schema less
  • Dimensional modelling
  • Cluster set and up Monitoring
  • Insert First Data
  • CRUD Operations
  • Insert Many
  • Update and Update Many
  • Delete and Delete Many
  • Projection
  • Intro to Embed Documents
  • Embed Documents in Action
  • Adding Arrays
  • Fetching Data From Structured Data
  • Schema Types
  • Types of Data in MongoDB
  • Relationship between data's
  • Aggregation
  • One to One using Embed Method
  • One to One using Reference Many
  • One to Many Embed
  • One to Many Reference Method
  • Assessment

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 Linux
  • Basic Shell script commands
  • Creating Frameworks
  • Cron jobs, Email alerts
  • Running Batch jobs



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. It is essential for software development, as it tracks revisions, facilitates collaboration, and helps in code management.

  • Introduction to Git
  • Git Commonds
  • Cloning repository in vs code
  • Working on cloning branches, commit, push, add, merge from vs code

AWS Cloud


  • Introduction to Cloud
  • AWS Services overview
  • Server vs serverless
  • Cloud data warehouse
  • Cloud Datalake
  • Cloud database (Dynamo DB)
  • IAM, roles, policies
  • EC2, VM’s
  • S3
  • RDS - MySQL Free tier database
  • Integrating RDS to Local System and Integrating RDS to Python Environment
  • Lambda
  • Cloud Watch
  • Integratrating All the Above components and RDS
  • Glue, Data crawler, Athena
  • Monitoring ETL pipeline with Step function


System Design

  •  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



We study Snowflake to grasp modern cloud-based data warehousing,
focusing on its architecture, data sharing, scalability, and data analytics

  • Introduction to snowflake
  • Diffrence between Datalake, Datawarehouse, Deltalake, Database
  • Dimension and Fact Tables
  • Roles and users
  • Data modeling, snowpipe
  • Partitioning and indexing
  • Data mart and data cubes & caching
  • Data masking
  • Handling json files
  • Data loading from S3 and tranformation



We 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

Big Data


We delve into big data to learn about handling and analyzing vast datasets, using tools
like Hadoop, Hive , and HDFS , PIG for insights and decision-making.

  • Installing Hive, Installing MYSQL Locally
  • Running Hive Query to integrate Local and HDFS file system
  • Installing Pig
  • Working with Pig script and integrating with local and HDFS file system
  • Installing HBase working with HBase Query
  • Installing Cassandra and working with Cassandra
  • Installing Sqoop and flume and do the data Migration
  • Local RDBMS to HDFS
  • Local RDBMS to Hive
  • Local RDBMS to HBase
  • HDFS to local RDBMS
  • Hive to RDBMS



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
  • Zookeper, replication
  • Batch vs realtime streaming
  • Real streaming process
  • Assignment and Task



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
  • Spark architecture
  • Hadoop vs Spark
  • RDDs , Dag , transformation , actions
  • Data Partitioning and Shuffling
  • DataFrame & Spark SQL
  • Streaming data handling in Spark
  • Spark batch data processing(CSV, JSON,parquet files)
  • AWS Data Management Tools [AWS EMR , GLUE jobs]
  • Assessments

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



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.

  •  Server, architecture
  • Installation
  • understanding prom UI
  • node exporters
  • promql (agg, fun, operators,data types)
  • integrating python with prom
  • counter , gauge , summary ,histogram
  • recording rules
  • alerting rules
  • alert manager ,installation of alert manager
  • grouping, inhibiting , throttling , silencing alerts
  • slack integration with prom with alert manager
  • pager duty integration with alert manager
  • black box exporters,installation
  • mysql exporter
  • integrating aws and prom
  • aws cloudwatch and prom
  • implementing grafana dashboard to prom

Data dog


Datadog is 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 is 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 is an open-source container orchestration platform that automates the
deployment, scaling, and management of containerized applications.

  • Nodes

  • Pods

  • ReplicaSets

  • Deployments

  • Namespaces

  • Ingress

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Professional Data Engineering Certification

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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 IFACET for Learning Professional Data Engineering?

IFACET 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.

  • IIT-K Certification

Highlight your portfolio with skill certifications from 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 IFACET's 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 IFACET 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 IFACET's Data Engineering course in 3 months by attending the weekday batch or in 5 months by joining the weekend batch to gain top-notch data skills and develop a competitive advantage over other engineers” because 3 months is specifically for weekday and 5 for weekend.

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 IFACET 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 our placement cell 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.

Yes, you will receive a globally recognized skill certificate accredited by IITK and IFACET, 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 IFACET 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 or +91-9219972805, +91-9219972806