Big Data Processing with Apache Spark & PySpark

Updated with Spark 3.5 features and latest best practices on 20-10-2024

Master Apache Spark and process big data at scale. Learn to build distributed computing applications, work with massive datasets, and optimize performance for real-world big data challenges. This advanced course is taught by a big data architect with 15+ years of experience at leading data companies.

4.7 (215 Verified ratings)
5,120 Enrolled Learners
Last Updated: Oct 20, 2024 4:30 PM
English
Instructor
Created by:
James Anderson
Course Preview
₹3,500
This course includes:
  • 28h:15m:30s on-demand videos
  • 180 Lectures
  • 20 Exercises
  • 16 Quizzes
  • Access on any Device
  • Certificate of completion

What you'll learn

Master Apache Spark architecture and components
Work with Resilient Distributed Datasets (RDDs)
Analyze data with Spark SQL and DataFrames
Stream real-time data with Spark Streaming
Optimize Spark applications for performance
Build 3 enterprise big data projects

Requirements

  • Strong Python programming skills
  • Understanding of SQL and databases
  • Basic knowledge of distributed systems concepts

Description

Apache Spark has become the industry standard for big data processing. It powers data pipelines at companies processing petabytes of data daily. This comprehensive course teaches you everything you need to become proficient with Spark.

You'll learn to work with massive datasets that don't fit in a single machine's memory, utilizing Spark's distributed computing capabilities. From interactive data analysis to building scalable data pipelines, this course covers the full spectrum of Spark applications.

Three real-world projects will expose you to practical challenges: processing web logs at scale, building real-time analytics dashboards, and optimizing queries on massive datasets. You'll understand not just the what, but also the why and how of distributed data processing.

Key Features:
  • Complete coverage of Spark ecosystem
  • RDDs, DataFrames, Datasets APIs explained
  • Spark SQL for complex analytics
  • Real-time stream processing techniques
  • Performance tuning and optimization
  • Lifetime access to course materials

Course Content

14 sections • 180 lectures • 28h 15m total length

  • What is Big Data?
    12:30
  • Introduction to Spark
    18:45

Deep dive into Spark's distributed architecture and low-level RDD API.

Master structured data processing with DataFrames and SQL queries.

Process real-time data streams with Spark Streaming.

Apply your knowledge to solve enterprise-scale data processing problems.

Instructor

Instructor

James Anderson

Big Data Architect | Ex-Databricks, Cloudera

James has 15+ years of experience building and scaling big data systems. He has led data platform engineering teams at Databricks and Cloudera, designing systems that process petabytes of data daily. He brings practical knowledge from real-world production environments.

Experience 15+ Years
Students Taught 6,000+
Course Rating
4.7
Courses 3 Courses

Student Reviews

4.7

215 reviews

5 star
78%
4 star
18%
3 star
3%
2 star
1%
1 star
0%
Reviewer
Rajesh Gupta
5 days ago

Best Spark course available! The instructor's experience shows in every lecture. The projects are challenging and very relevant to industry needs.

Reviewer
Lisa Zhang
2 weeks ago

Comprehensive and practical! Loved the real-world examples. This course prepared me well for my data engineering role.

Reviewer
Marcus Brown
1 month ago

Perfect blend of theory and practice. The performance tuning section alone is worth the course price!

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Course Features
  • Duration 28h 15m
  • Level Advanced
  • Language English
  • Certificate Yes
  • Enrolled 5,120