What you'll learn:
- Big Data , Hadoop and Spark from scratch by solving a real world use case using Python and Scala
- Spark Scala & PySpark real world coding framework.
- Real world coding best practices, logging, error handling , configuration management using both Scala and Python.
- Serverless big data solution using AWS Glue, Athena and S3
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
This course uses high-quality AI-generated text-to-speech narration to complement the powerful visuals and enhance your learning experience.
