The Data Engineer Bootcamp

Master Data Engineering, Build Scalable Pipelines, and Land a High-Paying Data Job

What you'll learn

Understand the Fundamentals of Data Engineering

Master Advanced SQL for Data Engineering

Build and Manage Data Pipelines

Work with Cloud Data Engineering Tools

Optimize Data Storage and Warehousing

Implement Best Practices for Security and Cost Management

Troubleshoot and Monitor Data Pipelines

Prepare for Data Engineering Interviews

Requirements

Fundamental SQL Skills

Basic Understanding of Databases

Description

Data Engineering Bootcamp: From Beginner to Job-Ready!Want to break into Data Engineering? Or level up your skills to land a high-paying job? This bootcamp will take you from beginner to job-ready, helping you master the tools, technologies, and best practices used by top tech companies like Meta, Google, and Amazon.Taught by industry expert Shashank Kalanithi, a software engineer at Meta, this bootcamp is packed with real-world projects, hands-on exercises, and career insights to fast-track your success in data engineering.What You’ll Learn & Achieve:Get a clear roadmap into Data Engineering – Understand what data engineers do, career opportunities, and how to get hiredMaster Advanced SQL for Data Engineering – Work with complex queries, optimize databases, and impress hiring managersBuild & Automate Data Pipelines – Learn Apache Airflow, ETL/ELT processes, and orchestration toolsCloud Data Engineering – Work hands-on with AWS, Azure, and Google Cloud tools like AWS Glue, Azure Data Factory, and GCP BigQueryOptimize Performance & Security – Learn how to manage costs, secure data, and implement logging and monitoring.Troubleshoot Like a Pro – Handle pipeline failures, outages, and performance bottlenecks with confidenceCrush Data Engineering Interviews – Gain insider tips, real-world case studies, and must-know technical conceptsBuild a Job-Winning Portfolio – Apply what you learn through hands-on projects that showcase your expertiseWhy This Bootcamp?Learn in-demand skills used by top tech companiesHands-on projects to build real-world experienceTaught by an industry expert with Meta & tech experienceJob-ready content to help you land a data engineering roleLifetime access – Learn at your own pace, anytime! This is your fastest path to a career in Data Engineering. Don’t waste months figuring it out on your own—get structured, expert-led training and land high-paying opportunities in tech!Enroll now and start building your future in Data Engineering today!

Overview

Section 1: Intro to Data Engineering Module: Data Engineering Career

Lecture 1 Course Introduction

Lecture 2 What Is a Data Engineer?

Lecture 3 Data Engineering Lifecycle

Lecture 4 Similar Careers to Data Engineering

Lecture 5 Data Engineering Service Models

Lecture 6 Data Engineer Leveling Guide

Lecture 7 Technical Skills of a Data Engineer

Section 2: Intro to Data Engineering Module: Data Architecture

Lecture 8 What Is Data Architecture?

Lecture 9 A Sample Data Architecture

Lecture 10 Data Lakes, Swamps, Warehouses, and Marts

Lecture 11 Modern Data Stack

Lecture 12 Connecting to Data

Lecture 13 Good Data Architecture

Section 3: Intro to Data Engineering Module: Data Orchestration

Lecture 14 Data Pipelines and Data Orchestration

Lecture 15 Apache Airflow

Section 4: Intro to Data Engineering Module: Relational Databases

Lecture 16 Relational Database Overview

Lecture 17 Organizing Relational Databases

Lecture 18 Relational Database Types

Lecture 19 Interacting with Relational Databases: SQL

Section 5: Intro to Data Engineering Module: Non-relational Databases

Lecture 20 ACID Properties

Lecture 21 Document Databases

Lecture 22 Key-Value Database

Lecture 23 Object Storage

Lecture 24 Columnar Database

Lecture 25 Graph Database

Lecture 26 No-SQL Database Questions

Section 6: Intro to Data Engineering Module: Software Engineering

Lecture 27 Horizontal Scaling vs Vertical Scaling

Lecture 28 Python

Lecture 29 APIs

Lecture 30 Shell Scripting

Lecture 31 Cron

Lecture 32 Version Control - Git - Mercurial

Lecture 33 Testing

Lecture 34 Docker and Containerization

Lecture 35 Infrastructure Management

Section 7: Intro to Data Engineering Module: Big Data Engineer

Lecture 36 What is Big Data?

Lecture 37 Hadoop

Lecture 38 Spark

Lecture 39 Kafka

Section 8: Intro to Data Engineering Module: Data Modeling

Lecture 40 Logical Physical Data Model

Lecture 41 Entity Relationship Diagrams

Lecture 42 Normalization

Lecture 43 Kimball and Inmon Data Warehousing

Section 9: Intro to Data Engineering Module: Security and Privacy

Lecture 44 The Non-optionality of Security and Privacy

Lecture 45 PII

Lecture 46 Principle of Least Privilege

Section 10: Advanced SQL: Setting Up the Environment

Lecture 47 Introduction to the Course

Lecture 48 Course GitHub Repository

Lecture 49 Setting Up the Environment

Lecture 50 An Overview of Relational Databases

Lecture 51 DDL, DML, DQL, DCL

Lecture 52 SQL Syntax

Section 11: Advanced SQL: Manipulating Databases: Mastering Essential SQL Statements

Lecture 53 Intro SQL statements

Lecture 54 CREATE

Lecture 55 ALTER

Lecture 56 INSERT

Lecture 57 UPDATE

Lecture 58 DELETE

Lecture 59 MERGE

Lecture 60 DROP

Section 12: Advanced SQL: Timing Is Everything: Managing and Manipulating DateTime in SQL

Lecture 61 DateTime Intro

Lecture 62 Different DateTime Types

Lecture 63 Timezones

Lecture 64 Intervals

Section 13: Advanced SQL: Complex Data Types: ENUMs, ARRAYs, Ranges, and Nested Data in SQL

Lecture 65 ENUM

Lecture 66 ARRAYs

Lecture 67 RANGE

Lecture 68 Nested Data

Section 14: Advanced SQL: Advanced Query Techniques: Exploring OVER, JOINS, CASE and more

Lecture 69 OVER

Lecture 70 CROSS JOIN

Lecture 71 LATERAL JOIN

Lecture 72 CROSS JOIN LATERAL

Lecture 73 COALESCE

Lecture 74 CASE

Lecture 75 CONCAT

Lecture 76 Recursive CTE

Lecture 77 Recursive CTE - Second Part

Section 15: Advanced SQL: Optimizing Data Structures: The Art of Data Normalization

Lecture 78 Data Normatlization (1/3)

Lecture 79 Data Normatlization (2/3)

Lecture 80 Data Normatlization (3/3)

Lecture 81 STAR Schema Snowflake

Section 16: Advanced SQL: Mastering Stored Procedures, Temporary Tables

Lecture 82 Stored Procedures and UDFs (1/3)

Lecture 83 Stored Procedures and UDFs (2/3)

Lecture 84 Stored Procedures and UDFs (3/3)

Lecture 85 Temp Table

Lecture 86 Materialized View

Lecture 87 Transactions

Lecture 88 SQL Structures

Section 17: Advanced SQL: Practical Tasks

Lecture 89 Question 1

Lecture 90 Question 2

Lecture 91 Question 3

Lecture 92 Question 4

Section 18: Building Data Pipelines with Apache Airflow: Understanding Data Pipelines

Lecture 93 Introduction to Data Pipelines

Lecture 94 Data Pipeline Architecture

Lecture 95 ETL vs. ELT

Lecture 96 Designing a Data Pipeline

Section 19: Building Data Pipelines with Apache Airflow: Hands-on with Apache Airflow

Lecture 97 Introduction to Apache Airflow

Lecture 98 Installation of Apache Airflow

Lecture 99 Airflow UI

Lecture 100 DAGs and Tasks

Lecture 101 Airflow Architecture

Lecture 102 Airflow Operators

Lecture 103 Airflow Hooks

Lecture 104 Introduction to the BashOperator

Lecture 105 Introduction to the PythonOperator

Lecture 106 Building an End-to-end Pipeline

Section 20: Advanced Data Pipeline Concepts

Lecture 107 Advanced Data Pipeline Concepts

Lecture 108 Pipeline Failure

Lecture 109 Ensuring Data Pipeline Reliability

Lecture 110 Backfilling Pipelines

Lecture 111 Change Data Capture

Section 21: Building Pipelines In the Cloud

Lecture 112 Building Pipelines In the Cloud

Lecture 113 Security in the cloud

Lecture 114 Cost management in the cloud

Lecture 115 Managing outages in the cloud

Aspiring Data Engineers,Data Analysts & Data Scientists,Software Engineers & Developers,IT & Cloud Professionals