What you'll get
  • 12+ Hours
  • 3 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Learn the basic ideas and structure of Apache Spark
  • Work with Spark's in-memory processing for fast data analysis
  • Use Spark SQL to process and optimize structured data
  • Apply MLlib for machine learning and data modeling tasks
  • Build and run data processing applications using Spark APIs
  • Perform data transformation, querying, and large-scale analytics
  • Use Spark in a clustered environment with YARN
  • Develop scalable big data solutions for real-world applications.

Content

Courses No. of Hours Certificates Details
Learning spark programming6h 4m✔View Curriculum
Apache Spark Fundamentals1h 38m✔View Curriculum
Courses No. of Hours Certificates Details
Apache Spark Advanced5h 47m✔View Curriculum

Description

The Apache Spark course introduces learners to one of the most powerful and widely used open-source big data processing frameworks. Developed in 2009, Spark has become a leading choice for large-scale data processing, machine learning, real-time analytics, and interactive querying, far surpassing traditional MapReduce in speed and efficiency.

This course provides a strong foundation in Spark's core concepts, architecture, and ecosystem. Learners will explore how Spark performs in-memory processing to deliver performance up to 1000 times faster than Hadoop, and how it supports data scientists in tasks such as data transformation, querying, and analysis. The course also covers essential components such as Spark SQL for structured data processing, MLlib for machine learning workflows, and YARN for cluster computing.

Participants will gain hands-on experience with Spark's APIs primarily developed in Scala and learn how to build scalable data-processing applications. With strong industry adoption from companies like IBM, Huawei, and major Hadoop vendors, Spark skills are highly valuable for modern data engineering and data science roles.

By the end of the course, learners will understand how to use Spark as a primary analytical engine, optimize workflows, and develop efficient big data solutions for real-world applications.

Sample Certificate

Course Certification

Requirements

  • Basic understanding of programming (Scala, Python, or Java preferred)

  • Familiarity with databases and SQL concepts

  • General knowledge of big data or distributed computing (helpful but not required)

  • Understanding of data processing workflows is an added advantage.

Target Audience

  • Aspiring data engineers and data scientists

  • Developers who want to work with big data tools

  • IT professionals looking to build big data processing skills

  • Hadoop users wanting to switch to faster in-memory processing

  • Anyone interested in learning how to build large-scale data analytics applications with Apache Spark.