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Data Engineering with Spark

Handle massive datasets at scale. Master distributed computing with Apache Spark, Databricks, and build robust ETL pipelines for big data.

When Excel crashes and Python runs out of memory, you need Spark. This course teaches distributed computing for Big Data. You will learn the Spark Architecture (Driver/Worker nodes), use PySpark for data transformation, and manage jobs in Databricks. We cover building resilient ETL pipelines, handling streaming data, and optimizing queries for performance. This is the core skillset for Data Engineers building the infrastructure that powers Data Science teams.

✅ 100% Free & Lifetime Access

⏱️ 5-Minute Lessons (Bite-sized learning)

🚀 21-Lesson Path (Independent modules)

📱 Mobile Friendly (Learn anywhere)

Big Data Team

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Complete Course Syllabus

1

Spark Architecture

Nodes, Clusters, and the execution model explained.
2

PySpark DataFrames

Transforming big data with familiar syntax.
3

ETL Pipelines

Extracting, Transforming, and Loading data at scale.
4

Spark SQL

Running SQL queries directly on distributed data.
5

Optimization

Caching, broadcasting, and fixing skewed partitions.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary

$120k - $170k

Career Paths

Data Engineer $120k-$165k

Big Data Developer $115k-$160k

Spark Specialist $130k-$180k

What You Will Learn

Build scalable ETL pipelines using Apache Spark and PySpark

Process massive datasets using distributed RDDs and DataFrames

Optimize Spark jobs by understanding partitioning and shuffling

Manage data workflows within the Databricks environment

Implement Delta Lake for reliable data lakes and ACID transactions

Skills You Will Gain

Apache Spark PySpark ETL Pipelines Databricks Data Warehousing

Who Is This For

Data Engineers

Backend Devs

Big Data Architects

Prerequisites

Python

SQL

Database Concepts

Data Engineering with Spark FAQs

Hadoop?

Spark has largely replaced MapReduce/Hadoop.

Python or Scala?

We focus on PySpark (Python) as it is most popular.

Need a cluster?

We use Databricks Community Edition (free cloud).

Hard to learn?

Conceptual shift from single-computer thinking.

Apache Spark

Databricks Community

AWS S3

Python

SQL

Your Learning Path

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Step 1 · First Step

Data Engineering with Spark Basics

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Advanced Data Engineering with Spark

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Advanced Data Engineering with Spark Course

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