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The Data Engineer Bootcamp - BaDshaH - 02-14-2025
![[Image: d84ca38527715177687a08ea0de3e702.jpg]](https://i124.fastpic.org/big/2025/0214/02/d84ca38527715177687a08ea0de3e702.jpg)
Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.61 GB | Duration: 8h 6m
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
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