English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 115 lectures (8h 5m) | 8.61 GB
Master Data Engineering, Build Scalable Pipelines, and Land a High-Paying Data Job
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 hired
- Master Advanced SQL for Data Engineering – Work with complex queries, optimize databases, and impress hiring managers
- Build & Automate Data Pipelines – Learn Apache Airflow, ETL/ELT processes, and orchestration tools
- Cloud Data Engineering – Work hands-on with AWS, Azure, and Google Cloud tools like AWS Glue, Azure Data Factory, and GCP BigQuery
- Optimize 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 confidence
- Crush Data Engineering Interviews – Gain insider tips, real-world case studies, and must-know technical concepts
- Build a Job-Winning Portfolio – Apply what you learn through hands-on projects that showcase your expertise
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!
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
Table of Contents
Intro to Data Engineering Module Data Engineering Career
1 Course Introduction
2 What Is a Data Engineer
3 Data Engineering Lifecycle
4 Similar Careers to Data Engineering
5 Data Engineering Service Models
6 Data Engineer Leveling Guide
7 Technical Skills of a Data Engineer
Intro to Data Engineering Module Data Architecture
8 What Is Data Architecture
9 A Sample Data Architecture
10 Data Lakes Swamps Warehouses and Marts
11 Modern Data Stack
12 Connecting to Data
13 Good Data Architecture
Intro to Data Engineering Module Data Orchestration
14 Data Pipelines and Data Orchestration
15 Apache Airflow
Intro to Data Engineering Module Relational Databases
16 Relational Database Overview
17 Organizing Relational Databases
18 Relational Database Types
19 Interacting with Relational Databases SQL
Intro to Data Engineering Module Nonrelational Databases
20 ACID Properties
21 Document Databases
22 KeyValue Database
23 Object Storage
24 Columnar Database
25 Graph Database
26 NoSQL Database Questions
Intro to Data Engineering Module Software Engineering
27 Horizontal Scaling vs Vertical Scaling
28 Python
29 APIs
30 Shell Scripting
31 Cron
32 Version Control Git Mercurial
33 Testing
34 Docker and Containerization
35 Infrastructure Management
Intro to Data Engineering Module Big Data Engineer
36 What is Big Data
37 Hadoop
38 Spark
39 Kafka
Intro to Data Engineering Module Data Modeling
40 Logical Physical Data Model
41 Entity Relationship Diagrams
42 Normalization
43 Kimball and Inmon Data Warehousing
Intro to Data Engineering Module Security and Privacy
44 The Nonoptionality of Security and Privacy
45 PII
46 Principle of Least Privilege
Advanced SQL Setting Up the Environment
47 Introduction to the Course
48 Course GitHub Repository
49 Setting Up the Environment
50 An Overview of Relational Databases
51 DDL DML DQL DCL
52 SQL Syntax
Advanced SQL Manipulating Databases Mastering Essential SQL Statements
53 Intro SQL statements
54 CREATE
55 ALTER
56 INSERT
57 UPDATE
58 DELETE
59 MERGE
60 DROP
Advanced SQL Timing Is Everything Managing and Manipulating DateTime in SQL
61 DateTime Intro
62 Different DateTime Types
63 Timezones
64 Intervals
Advanced SQL Complex Data Types ENUMs ARRAYs Ranges and Nested Data in SQL
65 ENUM
66 ARRAYs
67 RANGE
68 Nested Data
Advanced SQL Advanced Query Techniques Exploring OVER JOINS CASE and more
69 OVER
70 CROSS JOIN
71 LATERAL JOIN
72 CROSS JOIN LATERAL
73 COALESCE
74 CASE
75 CONCAT
76 Recursive CTE
77 Recursive CTE Second Part
Advanced SQL Optimizing Data Structures The Art of Data Normalization
78 Data Normatlization 13
79 Data Normatlization 23
80 Data Normatlization 33
81 STAR Schema Snowflake
Advanced SQL Mastering Stored Procedures Temporary Tables
82 Stored Procedures and UDFs 13
83 Stored Procedures and UDFs 23
84 Stored Procedures and UDFs 33
85 Temp Table
86 Materialized View
87 Transactions
88 SQL Structures
Advanced SQL Practical Tasks
89 Question 1
90 Question 2
91 Question 3
92 Question 4
Building Data Pipelines with Apache Airflow Understanding Data Pipelines
93 Introduction to Data Pipelines
94 Data Pipeline Architecture
95 ETL vs ELT
96 Designing a Data Pipeline
Building Data Pipelines with Apache Airflow Handson with Apache Airflow
97 Introduction to Apache Airflow
98 Installation of Apache Airflow
99 Airflow UI
100 DAGs and Tasks
101 Airflow Architecture
102 Airflow Operators
103 Airflow Hooks
104 Introduction to the BashOperator
105 Introduction to the PythonOperator
106 Building an Endtoend Pipeline
Advanced Data Pipeline Concepts
107 Advanced Data Pipeline Concepts
108 Pipeline Failure
109 Ensuring Data Pipeline Reliability
110 Backfilling Pipelines
111 Change Data Capture
Building Pipelines In the Cloud
112 Building Pipelines In the Cloud
113 Security in the cloud
114 Cost management in the cloud
115 Managing outages in the cloud
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