Data Engineering Masterclass for Beginners

Become a Job-Ready Data Engineer with Real-World, Hands-On Projects!

The Data Engineering Masterclass prepares you for an actual Data Engineer role, covering everything from Hadoop and Spark to AWS Glue, Databricks, Delta Lake, and Apache NiFi — the complete modern data engineering ecosystem.

Data Engineering powers every data-driven organization — it’s the foundation behind analytics, AI, and business intelligence. In this course, you’ll master how large-scale data is collected, processed, stored, and analyzed using today’s most in-demand Big Data tools.

Through step-by-step, hands-on labs and real-world projects, you’ll build end-to-end data pipelines using Hadoop, Spark, Databricks, and NiFi — applying both Python (PySpark) and Scala.

You’ll also learn professional-grade coding techniques including logging, error handling, unit testing, and configuration management — to code like an industry data engineer.

With Apache NiFi, you’ll go beyond traditional ETL. You’ll learn how to design, automate, and monitor data flows between systems, and understand where NiFi fits in a modern cloud-based architecture.

By the end, you’ll confidently work with cloud platforms, data lakes, and ETL pipelines, and know how to leverage ChatGPT and other generative AI tools to boost productivity, automate repetitive tasks, and think critically in an AI-driven world.

What You’ll Learn

• Big Data and Hadoop fundamentals

• Create a free Hadoop and Spark cluster using Google Dataproc

• Hands-on Hadoop: HDFS and Hive projects

• Python and PySpark basics for Big Data

• PySpark RDD, SQL, and DataFrame operations — hands-on

• Spark SQL and Temporary Views - Querying DataFrames with SQL

• Build an end-to-end project using PySpark and Hive

• Scala basics and Spark Scala DataFrames

• Real-world Spark Scala project with IntelliJ and Maven

• Databricks and Delta Lakehouse fundamentals

• Manage Delta Tables — versioning, restoring, and time travel

• Unity Catalog Volumes - File Storage and Operations

• Optimize Spark queries using Delta Cache

• Build a full data pipeline with Hive, PostgreSQL, and Spark

• Logging, error handling, and unit testing for PySpark & Scala applications

• Apache NiFi fundamentals — build, automate, and monitor data flows

• Integrate AWS Glue, Athena, and S3 for data transformation and analytics

• Use ChatGPT to accelerate learning and automate repetitive tasks

• Vibe coding with GitHub Copilot to build data pipelines using simple natural language conversation.

  
  

Tools & Technologies Covered

Hadoop • Spark • Hive • PySpark • Scala • Databricks • Delta Lake • NiFi • AWS Glue • Athena • PostgreSQL • IntelliJ • Maven • PyCharm

Who This Course Is For

• Beginners who want to become Data Engineers

• Software or SQL developers looking to move into Big Data

• Data Analysts or Scientists wanting to understand data pipelines

• Anyone preparing for a Data Engineer job or interview

  
  

Prerequisites

No prior programming experience is required — you’ll learn Python and Scala from scratch.
A basic understanding of databases and SQL will help, but it’s not mandatory.

Outcome

By completing this masterclass, you will:

• Understand Big Data and distributed computing concepts

• Build and deploy Spark and NiFi data pipelines on cloud platforms

• Work confidently with Databricks, Delta Lake, and AWS Glue

• Apply best practices in logging, testing, error handling, and performance tuning

• Be ready for real-world Data Engineering roles with hands-on, practical experience