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Big Data Processing with Hadoop and Spark

Master the tools and techniques that power large-scale data processing and analytics. This course introduces the principles and frameworks of Big Data Processing with Hadoop and Spark, enabling learners to manage, process, and analyze massive datasets efficiently. You’ll start by understanding the Hadoop ecosystem, including HDFS and MapReduce, and how distributed storage and computation work together to handle data at scale. Then, you’ll explore Apache Spark, a powerful framework for fast, in-memory data processing and real-time analytics. Through guided exercises and case studies, you’ll learn how to build scalable data pipelines, optimize performance, and apply transformations for business insights. By the end of this course, you’ll be equipped to handle complex data workloads using industry-standard big data tools. Ideal for aspiring data engineers, analysts, and developers, this course bridges data management and cloud computing—preparing you to design, implement, and manage big data solutions that drive intelligent decision-making in modern organizations.
Duration 4 Months
Institution University of Pittsburgh
Format Online

Eligibility Criteria

school

Academic Foundation

A recognized Bachelor’s degree or high school equivalent required for admission into University of Pittsburgh.

language

Language Proficiency

English proficiency required. IELTS, TOEFL, or standard medium-of-instruction certificates accepted.

Detailed Fees Breakdown

Base Tuition Fee $226
Total Est. Investment $226

Scholarships and early-bird waivers may apply. Contact admissions for exact institutional fees.

Academic Trajectory

Program Outcome

Graduates of the Big Data Processing with Hadoop and Spark program at University of Pittsburgh are equipped with global perspectives, ready to excel in international markets and top-tier career opportunities.

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