Data Engineer Bootcamp eLearning (100% self-paced)

Data Engineer Bootcamp eLearning (100% self-paced)

Handle Data Like a Pro: Optimize, Analyze, and Deliver Insights. Build High-Demand Skills & Launch Your Career as a Data Engineer

To achieve their goals effectively, businesses must manage and make sense of the vast amounts of data they produce. A large portion of this data is unstructured and must be cleaned, organized, and maintained. This is where Data Engineers play a vital role—using their expertise in Big Data technologies to help organizations turn data into actionable insights that drive performance.

Data Engineer Bootcamp equips you with the skills to work confidently with raw data. You'll gain hands-on experience across a wide range of key data engineering areas, including data warehousing, Linux, Python, SQL, Hadoop, MongoDB, Big Data processing and security, AWS, and more. You’ll also learn to design and build databases, extract and interpret data, and develop effective data models. By the end of the program, you'll be prepared to tackle complex real-world challenges as a professional Data Engineer.

Key Features

• Course and material in English
• Beginner - Advanced level
• 290 Hours of E-Learning Material
• 12+ Real-World Case Studies
• Immersive Learning with 230+ Hands-on Exercises
• Auto-Graded Assessments and Recall Quizzes
• Create a Job-Ready Portfolio with 10+ Capstone Projects
• Study time: Approximately 4-6 months
• 2 years of access to the learning platform
• Upon successful completion, learners receive a course completion certificate

Learning Outcome

• Python for Data Analysis. Develop a strong command of Python, covering everything from the basics to advanced techniques used in Data Science.
• Working with Relational Databases and SQL. Discover how to extract and analyze data from databases using tools like Excel and SQL.
• SQL for Data Analytics. Learn to query databases effectively and analyze structured data using SQL.
• NoSQL with MongoDB. Gain comprehensive knowledge of MongoDB—from performing CRUD operations to deploying MongoDB in the cloud.
• Data Warehousing Fundamentals. Understand how to integrate large volumes of data and explore practical applications of data warehousing.
• Big Data Processing with Hadoop. Master data ingestion techniques for both structured and unstructured data using tools like Sqoop and Flume in the Hadoop ecosystem.
• Real-Time Data Streaming with Spark. Build solid skills in Spark programming, working with RDDs, DataFrames, and Spark SQL APIs.
• Apache Kafka. Learn how Kafka works, including its core components and how to properly configure a Kafka cluster.
• Big Data on AWS. Explore AWS tools and services for storing and analyzing large datasets efficiently in the cloud.
• Big Data Security Essentials. Understand the key data protection regulations, common challenges, and security best practices in handling big data.

Target Group

• Aspiring Data Engineers: Individuals with little to no experience in data engineering who want to break into the field.
• Beginners: who want a structured, hands-on learning path to master the fundamentals of data engineering.
• Early-Career IT Professionals: Developers, analysts, and system admins looking to transition into data engineering roles.
• Professionals in adjacent tech fields (e.g., QA, support, BI) aiming to shift to a data-centric career path.
• Data Enthusiasts & Self-Learners
• Learners who have dabbled in Python, SQL, or Big Data on their own and want a more formal, job-ready curriculum.
• Career Changers: Non-tech professionals interested in moving into the high-demand world of data engineering.
• Anyone looking to gain in-demand skills to increase employability in data-driven roles.
• Professionals in Data-Driven Organizations
• Employees in companies undergoing digital transformation, looking to upskill in data tools, infrastructure, and pipelines.

Prerequisites

No prior experience is required to join our Data Engineer Bootcamp. While having some familiarity with Linux and basic Python can be helpful, it’s not essential. All you need is the right mindset, logical thinking, and a curiosity to learn—everything else, we’ll guide you through!

Stand Out to Recruiters with an Exceptional Project Portfolio

Create industry-grade projects that reflect the skill level of top-performing Data Engineers. Develop a strong, employer-ready portfolio that captures the attention of leading companies. Boost your confidence and land your dream role as a Data Engineer. Here's a preview of the kind of projects you'll work on:

• BitBuy – Data Mining Application: Create a platform to mine Bitcoin, validate new transactions on the blockchain, and predict cryptocurrency trends using data analysis.
• HireMeIT – Real-Time Job Portal: Develop an application that taps into real-time Twitter data streams to help users discover the latest IT job opportunities.
• SparkUp – Log Analytics Tool: Leverage production-level logs to build a scalable log analytics solution using Apache Spark, Python, and Kafka.
• DataBuilder – Data Warehousing Solution: Design and implement your own data warehouse using AWS Redshift to generate quick, predictive insights from large datasets.
• MongoBite – API Development Project: Build a robust API capable of querying databases and delivering precise responses to HTTP requests in real time.

Skills You’ll Develop

• Data analysis and interpretation
• Kafka security configuration
• Real-time data processing with Spark Streaming
• Building efficient data pipelines
• Handling structured streaming data
• Managing large-scale data storage on AWS
• Working with unstructured datasets
• Implementing stream processing using Kafka
• Creating data warehouses on AWS
• Identifying patterns and trends in datasets
• Extracting data from various databases
• Validating data relationships
• Scheduling and managing Big Data workflows with Oozie
• Data transformation using Pandas
• Designing impactful data visualizations

Bootcamp Curriculum

1. Linux Processes and Networking

Learning Goals:

• Build confidence in navigating and working within Linux and Unix-like systems
• Use Linux command-line tools and shell scripting effectively
• Explore advanced Linux features including pipes, grep, system processes, and networking

Topics Covered:

• Introduction to Linux
• Linux Command Line Basics
• Managing Files and Directories
• Creating and Editing Files
• User, Group, and Permission Management
• Essential Linux Tools and Features
• Handling Processes in Linux
• Networking Fundamentals in Linux
• Writing and Running Shell Scripts

2. Python for Data Engineering

Learning Goals:

• Begin with foundational Python programming concepts
• Learn to use built-in functions and create custom functions
• Gain hands-on experience with essential Python libraries like Pandas and NumPy
• Understand how to create visualizations using Python tools

📚 Topics Covered:

• Python Introduction
• Working with Code and Data
• Core Programming Constructs
• String Manipulation
• Data Structures in Python
• Control Flow Techniques
• Defining and Using Functions
• Modules and File Handling
• Using NumPy for Numerical Computations
• Data Manipulation with Pandas
• Working with Regular Expressions
• Data Visualization Techniques

3. Relational Database Design and Architecture

Learning Goals:

• Understand how relational databases are structured and designed
• Explore key database modeling principles and techniques
• Learn various methodologies for database modeling
• Compare on-premises databases with cloud-based solutions

Topics Covered:

• Overview of Relational Databases
• Architecture of a Relational Database System
• Core Elements of Relational Databases
• Principles of Database Structure and Design
• Approaches to Database Modeling
• SQL Components and Functionality
• Transactions and Concurrency Management
• Optimizing Performance with Joins
• Backup and Data Recovery Strategies
• On-Premise vs Cloud Database Solutions

4.SQL for Data Analysis

Learning Goals:

• Master key SQL commands used in database operations
• Learn to filter and manipulate data using SQL operators
• Apply aggregation and summary functions for insights
• Understand how to combine data from multiple tables
• Dive into advanced techniques for efficient data analysis using SQL

Topics Covered:

• Introduction to SQL and Its Importance
• SQL Administrative Commands
• SQL Fundamentals
• Filtering Records with the WHERE Clause
• Using Aggregation and Summary Functions
• Performing Miscellaneous Data Analysis
• Understanding Table Relationships
• Merging Data from Multiple Tables
• Advanced Techniques in SQL Analysis
• Conducting Efficient and Optimized Analysis

5. MongoDB

Learning Goals:

• Understand how to design and model schemas for MongoDB
• Learn the concepts of replication and sharding for scalability
• Gain experience working with MongoDB in cloud environments

Topics Covered:

• Overview of MongoDB
• Core MongoDB Concepts and Features
• Performing CRUD (Create, Read, Update, Delete) Operations
• Schema Design and Data Modeling
• Advanced MongoDB Operations
• Implementing Replication and Sharding
• Administration and Security Best Practices
• Integrating MongoDB with Other Applications
• Deploying and Managing MongoDB in the Cloud

6. Data Warehousing

Learning Goals:

• Understand various implementation approaches and storage types for data warehouses
• Learn how data integration works within a data warehousing context
• Explore the overall ecosystem that supports a data warehouse

Topics Covered:

• Introduction to Data Warehousing Concepts
• Implementation Strategies and Storage Types
• Techniques for Integrating Data
• Data Warehouse Modeling Approaches
• Designing Dimensional Models
• Managing Historical Data in Warehouses
• Overview of the Data Warehouse Ecosystem
• Role of Business Intelligence
• Real-World Industry Use Cases

7. Big Data Processing with Hadoop

Learning Goals:

• Grasp the fundamentals of distributed storage and computation with Hadoop
• Learn to import structured and unstructured data using tools like Sqoop and Flume
• Understand data processing with MapReduce, Pig, and Hive
• Gain hands-on experience running Big Data workloads on AWS EMR and S3

Topics Covered:

• Introduction to Big Data and the Hadoop Ecosystem
• Hadoop Distributed File System (HDFS) and YARN
• Processing Data with MapReduce
• Ingesting and Exporting Data with Sqoop and Flume
• Data Processing Using Pig and Hive
• Working with NoSQL Databases and HBase
• Workflow Management with Apache Oozie
• Introduction to Apache Spark
• Deploying and Managing Hadoop on AWS Elastic MapReduce (EMR)

8. Real-Time Big Data Streaming with Spark

Learning Objectives:

• Develop Spark applications via interactive shell and batch processing
• Gain insight into Spark’s execution model and architecture
• Understand Structured Streaming and how it operates
• Explore real-world applications of Spark Streaming and Structured Streaming

Topics Covered:

• Overview of Spark Runtime Environment
• Building ETL Pipelines with Spark
• Working with NLP, SparkSQL, and DataFrames
• Fundamentals of Stream Processing in Spark
• Managing Stateful Operations in Spark Streaming
• Implementing Sliding Window Functions
• Getting Started with Structured Streaming
• Introduction to Apache Kafka
• Integrating Kafka with Spark Streaming
• Integrating Kafka with Structured Streaming
• Using Spark Streaming with Amazon Kinesis – Part 1
• Using Spark Streaming with Amazon Kinesis – Part 2
• Exploring Additional Spark Streaming Integrations

9. Apache Kafka

Learning Objectives:

• Explore the Kafka ecosystem, its architecture, key components, and operations
• Gain hands-on experience writing Kafka code
• Learn how to integrate Kafka with external systems
• Understand stream processing techniques using Kafka

Topics Covered:

• Introduction: Why Use Kafka?
• Getting Started with Kafka
• Kafka as a Distributed Logging System
• Ensuring Reliability and High Performance
• Setting Up Kafka Development Projects
• Producing Messages to Kafka
• Consuming Kafka Messages
• Enhancing Client Reliability and Performance
• Introduction to Kafka Connect
• Overview of Kafka Streams
• Stateless Stream Processing Concepts
• Stateful Stream Processing Techniques
• Securing Kafka Deployments
• Real-World Applications and Use Cases of Kafka

10. Big Data on AWS

Learning Objectives:

• Gain a solid understanding of AWS services used in Big Data analytics
• Learn how to collect, catalog, and prepare data for analysis
• Explore methods for storing and processing large-scale data on AWS
• Discover advanced machine learning capabilities available through EMR

Topics Covered:

• Introduction to Big Data and AWS
• Data Collection, Organization, and Preparation Techniques
• Strategies for Storing Massive Datasets on AWS
• Processing Data Effectively Using AWS Tools
• Advanced Concepts in Big Data on AWS

11. Data Security

Learning Objective:

• Understand key data privacy regulations and security standards
• Gain knowledge of various types and sources of threats
• Learn practical strategies for securing Big Data systems and maintaining user privacy

Topics Covered:

• Introduction and Overview
• Privacy Regulations and Security Standards
• Types of Threats and Their Origins
• Core Security Principles
• Data Governance and Understanding
• Securing the Big Data Pipeline
• Protecting Big Data Storage Solutions
• Managing End-User Access
• Leveraging Big Data Analytics to Counter Threats
• Practical Implementation of Big Data Security and Privacy

FAQ

What are the training formats available for the Data Engineering Bootcamp?

At present, the Data Engineer Bootcamp is available in a self-paced, on-demand format. This allows learners to study flexibly at their own pace, gaining in-depth knowledge and mastering essential skills for success in the dynamic world of data engineering. This self-paced course gives you the freedom to learn whenever and wherever it suits you. You can take your time with challenging topics, revisit lessons as often as needed, and pause or replay videos to reinforce your understanding. With 2-year access to the course materials, you’ll have ongoing support to refresh concepts and clear doubts anytime you choose.

Do I need specific software for this bootcamp?

No, nothing specific. You’ll need a laptop or workstation with an internet connection and a minimum of 8GB RAM. Additionally, make sure you have a web browser like Google Chrome, Microsoft Edge, or Firefox installed.

Who is a Data Engineer?

A Data Engineer is a professional who designs and builds systems that gather, process, and deliver reliable, high-quality data. Their main role is to transform raw, unstructured data into clean, structured formats that data scientists and analysts can effectively use for insights and decision-making.

What skills are needed to become a Data Engineer?

To launch a successful career in data engineering, you’ll need a solid foundation in programming, database architecture, and cloud technologies. It’s also important to understand Big Data systems, data security, and the basics of machine learning. A comprehensive data engineering course should equip you with these essential skills.

What are the roles and responsibilities of a Data Engineer?

A Data Engineer is responsible for:

• Collecting, organizing, and transforming raw data into structured formats suitable for analysis.
• Building and maintaining data pipelines that efficiently move data from source systems to storage and processing layers.
• Designing and optimizing database architectures, both relational and NoSQL.
• Creating tools and methods for data validation, cleaning, and quality assurance to ensure data integrity.
• Collaborating with data scientists and analysts to provide accessible, reliable datasets for analytics and machine learning.
• Implementing scalable data solutions that support business intelligence and decision-making processes.

A well-structured data engineering course will equip you with the knowledge and skills to handle all these responsibilities confidently.

What are the most popular data frameworks you’ll learn?

In this Data Engineer certification program, you’ll work with widely used frameworks such as Apache Kafka, Apache Spark, and Hadoop. You'll also gain hands-on experience with Big Data services on AWS and learn the core principles of Data Warehousing.

What are the major challenges to becoming a Data Engineer?

One of the biggest hurdles for aspiring data engineers is building a solid foundation in programming, cloud technologies, and database systems—both relational and NoSQL. Many struggle with the depth and breadth of knowledge required across these domains. That’s why structured data engineering certification programs are in high demand—they help bridge these gaps by offering comprehensive training across all key areas.

Who is this Data Engineer Bootcamp for?

This Bootcamp is ideal for anyone aiming to launch or transition into a rewarding career in data engineering. Typical participants include:

• IT professionals working in traditional ETL processes
• Database administrators and specialists
• Software developers and engineers
• Business analysts seeking a technical edge
• Data professionals looking to specialize in data engineering
• Finance and banking sector professionals handling large data volumes
• Marketing professionals focused on data-driven strategies
• Students or recent graduates aspiring to enter the data engineering field

What career opportunities are available after completing the Data Engineer Bootcamp?

Upon successfully finishing this self-paced Data Engineer Bootcamp, you’ll be well-prepared to pursue roles such as:

• Data Engineer – Design and manage scalable data pipelines and systems.
• Data Architect – Create and maintain the structure and strategy of data frameworks.
• Big Data Engineer – Handle massive datasets using technologies like Hadoop and Spark.
• Database Developer – Develop and optimize relational and NoSQL database systems.
• Data Security Administrator – Focus on safeguarding data infrastructure and ensuring compliance.

Can I Take This Course While Working Full-Time?

Yes, you can! We understand that balancing a full-time job and upskilling can be demanding. That’s why our Bootcamp is available in a flexible, part-time format designed specifically for working professionals. With the Flex option, you can learn at your own pace without disrupting your current commitments.

What if I Find the Bootcamp Too Challenging and Need to Drop Out?

If you’re finding the Bootcamp difficult, don’t hesitate to contact our support team. We’re here to help and will do everything we can to guide you through the tough spots and keep you moving forward with confidence. Keep in mind—mastering development skills takes time and effort. While anyone can learn to code, perseverance and a willingness to grow are key to success.