Data Engineering Bootcamp - Series 1 | Udemy [Update 07/2025]
English | Size: 3.4 GB
Genre: eLearning[/center]

Get Started Today and Build Your Career in Data Engineering!

What you'll learn
Understand the Fundamentals of Modern Data Engineering
Build and Manage Scalable Data Lakes on AWS S3
Design Star Schema Data Models with Fact & Dimension Tables
Implement Slowly Changing Dimensions (SCD1 & SCD2)
Develop ETL Pipelines Using PySpark with Data Quality Checks
Query and Explore Data Lakes with AWS Athena and Glue Catalog
Automate Workflows and Pipelines Using Apache Airflow
Create Custom Airflow Plugins to Manage EMR Spark Jobs
Apply the WAP (Write-Audit-Publish) Pattern for Production Pipelines
Implement Data Quality Frameworks and Data Contracts
Deploy and Monitor Data Pipelines on AWS EMR
Optimize Data Workflows for Cost, Performance, and Reliability
Gain Hands-On Experience with Real-World Use Cases
Prepare for Data Engineering Interviews with Confidence

Take your first step into the world of data engineering and future-proof your career with this hands-on, project-based bootcamp built on the modern data stack. Taught by a seasoned data architect with over 11 years of industry experience, this course blends theory with practice, designed for aspiring data engineers, software engineers, analysts, and anyone eager to learn how to build real-world data pipelines.

You will learn to design scalable data lakes, build dimensional data models, implement data quality frameworks, and orchestrate pipelines using Apache Airflow, all using a real-life ride-hailing application use case to simulate enterprise-scale systems.

What You'll Learn

Section 1: Context Setup

Build your foundation with the Modern Data Stack, understand OLTP systems, and explore real-world data platform architectures.

Gain clarity on how data flows in data-driven companies

Learn using a ride-hailing app scenario

Get properly onboarded into the bootcamp journey

Section 2: Data Lake Essentials

Learn how to build and manage scalable data lakes on AWS S3.

S3 architecture, partitioning, layers, and schema evolution

IAM, encryption, storage classes, event notifications

Lifecycle management, backup & recovery

Hands-on with Boto3 S3 APIs

Section 3: Data Modeling

Master star schema design and implement SCD Type 1 and Type 2 dimensions.

Dimensional & Fact modeling

ETL development for analytical reporting

Build end-to-end models and data marts with hands-on labs

Section 4: Data Quality

Ensure trust and integrity in your data pipelines.

Understand accuracy, completeness, and consistency

Implement DQ checks using industry best practices

Use data contracts for accountability

Section 5: AWS Athena

Query massive datasets with serverless power using AWS Athena.

Learn DDL, Glue Catalog, and workgroup management

Automate queries using Boto3 APIs

Compare Athena vs Presto vs Trino

Optimize queries with best practices

Section 6: Apache Spark

Build production-grade data pipelines with PySpark on AWS EMR.

Learn Spark architecture and PySpark APIs

Build data pipelines using the WAP (Write-Audit-Publish) pattern

Run scalable jobs on AWS EMR

Apply UDFs and data quality within transformation logic

Section 7: Apache Airflow

Orchestrate workflows using Airflow and build custom plugins:

Design DAGs, schedule pipelines, manage dependencies

Automate Spark jobs using custom AWS EMR plugin

Hands-on labs for ingestion and transformation DAGs

Build reliable, reusable orchestration solutions

What You'll Build

A production-style data platform for a ride-hailing company, including:

Data lake on AWS S3

Dimensional data model with SCD logic

Spark-based transformation pipelines

Automated orchestration with Airflow

Query layer with Athena

Built-in data quality validations

Who this course is for:
Aspiring data engineers looking to break into the field
Software engineers or analysts transitioning into data roles
Professionals seeking real-world, hands-on data engineering experience
Anyone interested in mastering the modern data engineering stack

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