Self-Paced Data Engineer Bootcamp

Learn Data Engineering Build a Job-Worthy Portfolio & Get Hired as a Data Engineer

Land lucrative offers with an average salary of per year

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Optimize and Analyze Data Like A Pro

Businesses need to manage the data they generate to meet business goals successfully. The huge quantum of unstructured data needs to be cleaned, structured, and maintained. This is where Data Engineers step in and help organizations leverage data-driven insights to optimize business performance with their expertise in Big Data technologies.

KnowledgeHut’s Data Engineer Bootcamp will make you an expert in dealing with raw data by making you proficient in a range of data engineering topics. These include Data warehousing, Linux, Python, SQL, Hadoop, MongoDB, Big Data processing, Big Data security, AWS and more. You'll also become industry ready by learning how to design and create databases, capture and analyze data, and prepare data models. By the end of this Data Engineer Certification, you'll be able to handle complex business problems in the Data Engineering space.

Bootcamp Highlights

  • 289 Hours of E-Learning Material

  • Immersive Learning with 230+ Hands-on Exercises

  • 12+ Real-World Case Studies

  • Create a Job-Ready Portfolio with 10+ Capstone Projects 

Technologies You Will Master

  • Python
  • Hadoop
  • Spark
  • SQL
  • Mongo DB logo
  • AWS Tools- New

Take one step ahead towards the most in-demand Data Engineering jobs

benefits of Data Engineer Bootcamp

In this digital economy, businesses can successfully overcome competition and market forces only if they have the right form of data. How else will they make future predictions and adjust business practices to reflect market trends? No wonder the demand for data engineers is so steep, with the average salary offered being $112,493 per year.

For as long as there's data to process, organizations around the world will always need data engineers to help them make sense of it all. Data Engineers rank at #6 in LinkedIn's U.S. Jobs on the Rise Report for 2021. As per Dice Insight's 2020 Tech Job Report, Data Engineering is also one of the fastest-growing jobs in the tech space, recording a 50% year-on-year job growth!  Data engineering is an-ever evolving field, and this data engineer course will help you gain solid knowledge in important topics such as Big Data, Databases, and more. You will be able to master tools for data analysis and validation, as well as maintain data infrastructures and pipelines.

Become a skilled Data Engineer!

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prerequisites for Data Engineer Bootcamp


  • There are no prerequisites for attending the Data Engineer Bootcamp.  
  • Prior knowledge of Linux and Basics of Python programming language will be beneficial but is not mandatory. 
  • The right aptitude, logical thinking, and drive for curiosity are all you need. Leave the rest to us!

Who Should Attend This Bootcamp

IT professionals in traditional ETL domain

IT professionals in database domain

Software engineers or developers

Business Analysts

Data Professionals interested in Data Engineering

Banking and Finance Professionals

Marketing Professionals

Students interested in a career in Data Engineering

Impress Recruiters With a Stellar Project Portfolio

Build professional projects like the top 1% of Data Engineers and create a solid, job-worthy portfolio worthy of Tier 1 companies. Build confidence and get hired as a Data Engineer. Here’s a glimpse of some of the projects you’ll build:

  • BitBuy
    BitBuy Data Mining

    An app where you can mine Bitcoin, verify new transactions to the Bitcoin currency system, and forecast the latest trends. 

  • HireMeIT
    HireMeIT Job Portal

    An app that uses streaming data from Twitter to help people find the latest IT jobs with their real-time information.

  • SparkUp
    SparkUp Log Analytics

    Use real-world production logs to perform scalable log analytics with Apache Spark, Python, and Kafka.

  • DataBuilder
    DataBuilder Data Warehouse

    Build your own Data Warehouse and get predictive insights on all your data quickly and efficiently using AWS Redshift.

  • MongoBite
    MongoBite API

    Create an Application Programming Interface (API) which queries databases for specific data and responds to HTTP requests.

What You Will Learn

Essentials of Python for Data Analysis

Master basic, intermediate, and advanced levels of Python for Data Science

Relational Databases and SQL

Learn how to use Excel to extract data from databases and perform analysis on it.

SQL for Data Analysis

Learn how to use SQL to extract and analyze the data stored in databases. 

NoSQL – MongoDB

Gain complete, end to end knowledge of MongoDB concepts, ranging from CRUD operations to MongoDB on the cloud 

Data Warehousing

Learn how to integrate data while understanding the applications of data warehousing 

Big Data Processing using Hadoop

Learn how to ingest structured and unstructured data in Hadoop using Sqoop and Flume 

Streaming Big Data with Spark

Build a robust foundation of Spark programing in the RDD, Data Frame and Spark SQL APIs.  

Apache Kafka

Learn about the Kafka Cluster, its components, and about configuring clusters accurately. 

AWS in Big Data

Understand the AWS Services stack for Big Data Analytics and how to store and process big data on AWS 

Big Data Security

Learn about the regulations and standards, challenges and solutions that come into play for data protection. 

Skills You Will Gain

Analyzing data

Securing Kafka

Spark Streaming

Data Pipelining

Structured Streaming

Storing large data on AWS

Exploring unstructured data

Stream Processing with Kafka

Date warehousing on AWS

Exploring datasets for trends

Extracting data from databases

Confirming relationships in data

Scheduling Big Data jobs in Oozie

Transforming datasets using Pandas

Building compelling visualizations

Course Curriculum

Download Curriculum

Learning Objective:

  • Gain confidence working with Linux and *-nix based work environments
  • Demonstrate the ability to work with Linux commands and the shell
  • Learn all about advanced Linux features such as pipes, grep, networking and more
  1. Introduction 
  2. Linus Command Line 
  3. Files and Directories 
  4. Creating and Editing files 
  5. User, Group and Permissions 
  6. Other Essential Features 
  7. Processes in Linux 
  8. Networking in Linux 
  9. Shell Scripts 
Video preview 2.

Learning Objectives:

  • Start with the basics of Python programming
  • Learn how to use existing functions and create user definedfunctions 
  • Work on different Python packages such as Pandas andNumPy
  • Understand visualizations using Python
  1. Introduction to Python 
  2. Code and Data 
  3. Building Blocks 
  4. Strings 
  5. Data Structures 
  6. Flow Control  
  7. Functions 
  8. Modules 
  9. Files 
  10. NumPy 
  11. Pandas 
  12. Regular Expressions
  13. Visualization 

Learning Objectives:

  • Learn database structure and design
  • Database modelling concepts
  • Participants will learn database modelling methodologies
  • On-Prem vs Cloud databases will be explained
  1. Introduction to Relational Database 
  2. Architecture of Relational database 
  3. Important Aspects of Relational Databases 
  4. Database Structure and Design 
  5. Database Design 
  6. Data modelling methodologies 
  7. SQL Components 
  8. Transaction and Concurrency 
  9. Database Joins and Performance Tuning 
  10. Backup and Recovery 
  11. On-Prem vs Cloud databases
Video preview 4.

Learning Objectives:

  • Understand essential SQL database Commands
  • Filter data and operators in SQL
  • Learn about Aggregation and Summary functions
  • Learn about combining tables
  • Explore advanced Data Analysis using SQL
  1. What is SQL and Why is it Important? 
  2. SQL Database Admin Commands 
  3. The Basics of SQL 
  4. Filtering Data Using WHERE Clause in SQL 
  5. Aggregation and Summary Functions in SQL 
  6. Miscellaneous Analysis in SQL 
  7. Table to Table Relationship in SQL 
  8. Combining Tables 
  9. Advanced SQL Data Analysis 
  10. Making Efficient Analysis 
Video preview 5.

Learning Objectives:

  • Learn Schema Design and modelling
  • Participants will learn replication and sharding
  • Learn MongoDB on cloud
  1. Introduction to MongoDB 
  2. MongoDB Fundamentals 
  3. CRUD Operations 
  4. Schema Design and Modelling 
  5. Advanced Operations 
  6. Replication and Sharding 
  7. Administration and Security 
  8. MongoDB with other Applications 
  9. MongoDB in the Cloud 
Video preview 6.

Learning Objectives:

  • Learn different types of implementation methods/data stores of data warehouse
  • Learn Data Integration
  • Ecosystem of Data Warehouse.
  1. Concept of a Data Warehouse 
  2. The Different Implementation Methods of the Data Warehouse 
  3. Data Integration 
  4. Data model for a data warehouse 
  5. Designing Dimension Models 
  6. Managing history in data warehouse 
  7. Ecosystem for data warehouse 
  8. Business Intelligence 
  9. Industry Examples 

Learning Objective:

  • Understand distributed storage, computing, and MapReduce processing
  • Learn how to ingest structured and unstructured data in Hadoop using Sqoop and Flume
  • Understand how to process Big Data using MapReduce, Pig, and Hive
  • Run Big Data loads on AWS EMR and AWS S3
  1. Introduction to Big Data and Hadoop 
  2. Hadoop Distributed File System – HDFS and YARN 
  3. MapReduce Processing in Hadoop 
  4. Data Ingestion and Egestion into Hadoop 
  5. Data Processing in Hadoop 
  6. NoSQL and HBase 
  7. Apache Oozie 
  8. Introduction to Spark 
  9. Hadoop Cloud on Amazon/Elastic MapReduce 

Learning Objectives:

  • Master development of Spark applications using the interactive shell or as batch applications
  • Understand the Spark application and runtime architecture
  • Understand how Structured Streaming works
  • Understand use cases for Spark Streaming and Structured Streaming
  1. The Spark Runtime 
  2. ETL with Spark 
  3. NLP SparkSQL and DataFrames 
  4. Introduction to Stream Processing with Spark 
  5. Stateful processing with Spark Streaming 
  6. Sliding Window Operations with Spark Streaming 
  7. Introduction to Structured Streaming 
  8. Introduction to Apache Kafka 
  9. Kafka Integration with Spark Streaming 
  10. Kafka Integration with Structured Streaming 
  11. Using Spark Streaming with Kinesis (Part-1)
  12. Using Spark Streaming with Kinesis (Part-2)
  13. Additional Spark Streaming Integrations 
Video preview 9.

Learning Objectives:

  • Understand Apache Kafka Ecosystem, Architecture, Core Concepts and Operations
  • Learn to write code for Kafka
  • Learn to connect Kafka to third party systems
  • Learn Stream Processing with Kafka
  1. Why Kafka? 
  2. First Steps with Kafka 
  3. Kafka as a Distributed Log 
  4. Reliability and Performance 
  5. Setting up a Development Project 
  6. Producing Messages with Kafka 
  7. Consuming Messages 
  8. Improving the Reliability and Performance of Our Clients 
  9. What is Kafka Connect? 
  10. Kafka Streams 
  11. Stateless Stream Processing 
  12. Stateful Stream Processing 
  13. Securing Apache Kafka 
  14. Real-world Use Cases of Apache Kafka 
Video preview 10.

Learning Objectives:

  • Understand the AWS Services stack for Big Data Analytics
  • Learn Data Collection, Catalogs and preparation
  • Storing and processing Big Data on AWS
  • Advance ML features on EMR
  1. Big Data and AWS 
  2. Data Collection, Catalogs, and Preparation 
  3. Storing Large data on AWS 
  4. Processing your data on AWS 
  5. Advanced Topics on Big Data 


Learning Objective:

  • Learn Data Security / Privacy Standards and Regulations
  • Master different threat sources and types
  • Learn Big Data Security and Privacy in action
  1. Introduction / Overview 
  2. Data Security / Privacy Standards and Regulations 
  3. Threat Sources and Types 
  4. Security Concepts 
  5. Data Understanding and Governance 
  6. Big Data Pipeline 
  7. Big Data Storage 
  8. Big Data End User Access 
  9. Using Big Data to Combat Big Data Threats 
  10. Big Data Security and Privacy Implementation 

Data Engineer Bootcamp FAQs

Course FAQs

There are no prerequisites for attending our Data Engineer Bootcamp. Prior knowledge of Linux and Basics of Python programming language will be beneficial but is not mandatory. 

The right aptitude, logical thinking, and drive for curiosity are all you need. Leave the rest to us!

The minimum recommended system requirement is to have a workstation or laptop with at least 8GB of RAM and an internet connection. 

No, nothing specific – you just need a web browser such as Google Chrome, Microsoft Edge or Firefox.  

Your bootcamp instructors are expert Data Engineers with years of experience working with Data Engineering technologies such as Python, NoSQL, Big Data, Hadoop, and more. 

Absolutely. Even if you have no prior understanding of data engineering, this data engineering certification will make you proficient in programming fundamentals, NoSQL, Relational Databases, Data Warehousing, Big Data, and the Hadoop ecosystem – in short, you will understand everything that a data engineering needs to know. 

Workshop Experience

Currently, our Data Engineer Bootcamp is offered as an on-demand self-paced program. Learners can dive deep into learning at their convenience and pace, mastering the skills needed to excel in the rapidly evolving field of data engineering.

Experts in the field of data engineering with extensive industry experience have created the on-demand course materials for our Data Science and Engineering program. You will learn fundamental concepts, practical aspects, and the best practices in Data Engineering in the bootcamp.  

You will need a workstation or laptop with Internet access, with at least 8GB of RAM. Apart from that, you will also need a web browser such as Google Chrome, Microsoft Edge, or Firefox. 

You have the option to pause the data training program for 14 days. Before rejoining, you would need to catch up with the Program by watching the recorded instructor-led sessions. You may opt for this option after discussing it with your Student Success Manager. 

You also have the option to defer a program, provided there is a valid reason offered to your Student Success Manager and is approved by the Program Director. Once you are back, you can discuss with your Student Success Manager to know which batch of the Data Bootcamp you can join.

Please contact your Learning Advisor for more information about this. 

Additional FAQs

Data Engineering Bootcamp FAQs

Data refers to information that has been converted from a basic, unstructured format to something that can be processed and moved easily. 

A Data Engineer is someone who builds systems to collect, validate and prepare, and publish high-quality data. If you're a Data Engineer, you're primarily responsible for converting raw, unstructured data into a format which data scientists can further analyze and interpret. 

Data is knowledge. It is solid evidence, and by collecting data you can tell if a particular system is working or not. Without data, you cannot measure the effectiveness or impact of any process, strategy, or method. Data also helps you visualize existing relationships between several variables in a system

There are no eligibility criteria for this data bootcamp. The aim of this Data Engineer Course is to allow learners from diverse backgrounds to come, understand, and master Data Engineering if they wish. 

To break into a lucrative and rewarding career in data engineering, you need strong fundamentals in coding, database design, and cloud computing. You also need to have sound knowledge in data security, Big Data, and Machine Learning. A good data science course should cover most (if not all) of these topics. 

A Data Engineer is responsible for building systems that collect, process, and convert raw, unstructured data into useful information. Data scientists/analysts will then further analyze this information to glean insights and help stakeholders make better decisions. 

To become a data engineer, holding a degree in computer science or other related field will help you. Apart from a degree, learning about data engineering tools and technologies through a data engineer certification is also a good idea. 

You can become a Data Engineer by enrolling yourself for a data engineer bootcamp or certification. In case that's not an option for you, you can start building a certain set of skills that'll help you become a data engineer. This includes coding, databases (relational and non-relational), cloud computing, Big Data, and security. 

This Data Engineer bootcamp will make you proficient in several tools and technologies including Python, SQL, MongoDB, Hadoop, Apache Kafka, and AWS (in the context of Big Data). 

The biggest benefit of being a data engineer is that there will always be demand for such roles, because data is everywhere and ever-increasing. In addition, data engineer is one of the fastest growing job roles within the digital transformation space. The pay is really good as well, with the annual average salary for a data engineer being $112,000. The demand for data engineers isn't going anytime soon, which is why the demand for the best data engineering courses is on the rise. 

Absolutely! If you take Dice Insights' Tech Job Report for 2020, for instance, the Data Engineer job role alone has seen a 50% year-on-year growth. It's also among the top three jobs on the rise in the United States within digital transformation, as per a LinkedIn report. No wonder data courses online are very popular.  

The most popular data frameworks that you'll learn in this data engineer certification include Apache Kafka, Spark, and Hadoop. You will also learn about Big Data in AWS and Data Warehousing fundamentals.   

The most common challenge faced by aspiring data engineers is the lack of strong fundamentals in coding, cloud computing, and database knowledge (both relational and non-relational). That's why a data certification is really sought-after by learners today, as they provide learners with strong fundamentals in all these areas and much more. 

With the sheer number of data training courses available online today, anybody can become a data engineer if they want to. You just need to be willing to learn, have an analytical bent of mind, and comfortable with coding cloud technologies, and working with databases. 

Anybody who desires a lucrative career in data engineering can take this data engineer bootcamp. Typical candidate profiles include the following: 

  • IT professionals in traditional ETL domain
  • IT professionals in database domain
  • Software engineers or developers
  • Business Analysts
  • Data Professionals interested in Data Engineering
  • Banking and Finance Professionals
  • Marketing Professionals
  • Students interested in a career in Data Engineering

Some of the top-paying organizations looking for data engineers are as follows: 

  • Oracle 
  • Walmart 
  • Nvidia 
  • Airbnb 
  • Apple 
  • PayPal 
  • Lyft 
  • Cisco 
  • Twitter 
  • Snowflake 

As you can see, these are top organizations, which is why aspiring data engineers are looking for the best data engineering bootcamp to help them get started on their learning journey. 

The best way to command a higher salary as a Data Engineer is by constantly developing your skills. The field of data analytics and engineering is constantly changing, which means that you must always stay updated on the latest developments in coding, cloud computing, and databases (SQL, NoSQL). If you don't know where to start, enrolling yourself in a top-rated Data Engineer course is a good idea. 

As per Glassdoor (August 2022), the average annual salary for a data engineer in the United States is $112,493. This is one of the reasons why certifications for data engineer are in high demand. 

On completion of this self-paced Data Engineer bootcamp, you can confidently apply for any of the following roles: 

  • Data Engineer 
  • Data Architect 
  • Big Data Engineer 
  • Database Developer 
  • Data Security Administrator  

Data Engineers are primarily responsible for collecting, managing, and converting unstructured data into a format that's both useful and accessible. Apart from this, they also maintain database pipeline architectures and create new methods/tools for data validation and analysis. If you're interested in learning to do these things, any good data engineer certification will be able to cover all these aspects and much more. 

This Data Engineer bootcamp will feature a capstone project, where you will put into practice all that you've learnt throughout the course. You'll do this through real-world problems and scenarios, similar to the ones actual data engineers work on. 

In order for you to become a data engineer in a product-based company, you must demonstrate two things - strong data engineering fundamentals and an updated project portfolio. A good data engineer course will help you with both these aspects. You'll gain strong foundational knowledge in coding, cloud computing, and databases, and also work on several real-world projects which you can later show as a part of your portfolio. 

A high-level learning path for data engineers looks like this: 

  1. Programming Fundamentals 
  2. Relational Databases and SQL 
  3. Data Warehousing 
  4. Big Data and Hadoop Ecosystem 
  5. Big Data on Cloud 
  6. Big Data Security 

Most well-designed data courses follow this learning path, which ensures that you have a well-rounded and comprehensive experience in terms of grasping fundamentals. 

This Data Engineer online course is priced at $3,999, and you can access it for a discounted price of $2,799 for a limited period. However, please check our bootcamp schedules for an exact idea of how much it'll cost you. 

Some of the most popular applications of data science include: 

  • Fraud and Risk Detection 
  • Healthcare 
  • Targeted Advertising 
  • Website Recommendations 
  • Advanced Image Recognition 
  • Speech Recognition 
  • Augmented Reality      

If you’re looking for the best courses for data engineering, it’s a good idea to check if the curriculum covers the above important topics.

To data engineer roles in top companies, you need to demonstrate solid skills in data modeling, coding, Python, and NoSQL. An updated project portfolio containing real-world project experience will build your case as well. This is also why our Data Engineer Bootcamp is extremely popular amongst out learners - it features a separate module for career assistance. This module includes mock interviews, resume building, and building a strong LinkedIn and GitHub profile.     

Pool of Stellar Course Creators and Instructors

Our industry-validated curriculum is designed with inputs from our Software Engineering Advisory Board comprised of industry veterans and renowned experts. The program is delivered by top instructors with several years of experience under their belt.

David Haertzen

Big Data Analytics Leader

Denis Rothman

AI Author, Speaker, and Instructor

Jeffrey Aven

Principal Consultant

Gopikrishnan R

Co-Founder and CTO

Beau - Carnes

Director of Technology, Education

Jignesh Kariya

Sr. Database Consultant

Peter Henstock

Machine Learning & AI Lead

Ashish Gulati

Python & Data Science Consultant

Shobhit Nigam

Program Director

Phillip Kinn

Senior Data Scientist

Emmanuel Segui

Asst. Director, Reporting and Programming

Rahul Tiwari

Data Scientist and Co-Founder

Mark Strefford

Machine Learning Lead

George Mount


Dr. Vishwakarma J.S.

Enterprise Architect, CTO

Jeremiah Lobo

Data Visualization Lead

Enes Bilgin

Staff Machine Learning Engineer

Harish Masand

Project Manager - Digital Enablement (Data and AI)

Mo Medwani

Sr. Data Scientist

Marie Stephen Leo

Director of Data Science - APAC

Anatoly Zelenin

Freelance Trainer, Author

Bradford Tuckfield

Data Science Instructor and Consultant

Malvik Vaghadia

Principal Consultant - Data and Analytics

Rashmi Banthia

Data Scientist

Avery Smith

Data Scientist

Prince Kumar

Data Scientist

Sudhanshu Saxena

Sr. Data Scientist and Data Science Trainer

Azib Hasan

Freelance Trainer

What Learners are Saying

Zhang Bingwen Data Storage System Designer
Apart from the course curriculum that was very well-thought-out, I really liked the fact that I was also working on my project portfolio as a part of the course. This was a great asset in terms of securing my first data engineer job.

Attended Data Engineer Bootcamp workshop in December

Priya Mitra Business Analyst
The case studies and capstone projects really prepared be for my job as a data engineer, which I secured after enrolling in this course. I'm grateful for my instructors who made learning data engineering a proper fun experience.

Attended Data Engineer Bootcamp workshop in December

Johan Shmidt Analytics Professional
I'm glad I signed up for this data engineering bootcamp because it covers important topics like Hadoop, Python, Big Data and many others. The concepts were taught in a clear and precise manner where everyone could understand.

Attended Data Engineer Bootcamp workshop in December

Nigel Smith Marketing Professional
Full value for money this course is! The instructors answer all your queries confidently thanks to their wealth of experience. You can even practice what you learn thanks to Cloud Labs - an awesome feature.

Attended Data Engineer Bootcamp workshop in December

Ursula Anders Data Professional
Data engineers are one of the most in-demand jobs of 2022 and KnowledgeHut helps you take advantage of that. This Data Engineer bootcamp is well-structured with the required relevant curriculum in 2022.

Attended Data Engineer Bootcamp workshop in December