Hadoop Platform and Application Framework
Learn Hadoop and Spark for big data analysis. Master MapReduce, HDFS, and core tools for wrangling and analyzing large datasets.
Learn Hadoop and Spark for big data analysis. Master MapReduce, HDFS, and core tools for wrangling and analyzing large datasets.
This course introduces novice programmers and business professionals to the core tools used in big data analysis. Focusing on Hadoop and Spark frameworks, it covers HDFS, MapReduce, and Spark RDDs. Through hands-on examples, learners will understand Hadoop architecture, execution environments, and fundamental big data concepts. The course emphasizes practical skills in using Hadoop stack components and designing MapReduce tasks.
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What you'll learn
Understand the Hadoop ecosystem and its major components
Explain the architecture and functionality of HDFS
Design and implement MapReduce tasks for data processing
Use Apache Spark for iterative algorithms and in-memory computing
Apply Hadoop and Spark to solve fundamental big data problems
Navigate the Hadoop execution environment and resource scheduling
Skills you'll gain
This course includes:
276 Minutes PreRecorded video
11 quizzes,4 programming assignments
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FullTime access
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There are 5 modules in this course
This course provides a comprehensive introduction to Hadoop and its ecosystem for big data processing. It covers the Hadoop Distributed File System (HDFS), MapReduce programming model, and Apache Spark framework. Students learn about the Hadoop stack, including YARN, Tez, Pig, Hive, and HBase. The curriculum includes hands-on practice with HDFS commands, MapReduce programming, and Spark RDD operations. By the end of the course, learners will be able to design and implement big data processing tasks using Hadoop and Spark.
Hadoop Basics
Module 1 · 2 Hours to complete
Introduction to the Hadoop Stack
Module 2 · 3 Hours to complete
Introduction to Hadoop Distributed File System (HDFS)
Module 3 · 3 Hours to complete
Introduction to Map/Reduce
Module 4 · 7 Hours to complete
Spark
Module 5 · 9 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
High-Performance Computing Expert and Scientific Computing Educator
Andrea Zonca serves as an HPC Applications Specialist at the San Diego Supercomputer Center, bringing unique expertise in cosmology and high-performance computing. His academic background includes a Ph.D. focused on analyzing Cosmic Microwave Background data from the Planck Satellite, which led to his specialization in managing and analyzing large-scale scientific datasets. At SDSC, he plays a crucial role in helping research groups across various scientific disciplines optimize their data analysis pipelines for XSEDE supercomputers. His technical expertise spans parallel computing in Python and C++, with particular focus on scientific computing applications. As a certified Software Carpentry instructor, he teaches essential computational skills including bash automation, git version control, and Python programming to scientists. His Coursera courses focus on high-performance computing and scientific programming, helping researchers develop the skills needed to leverage supercomputing resources effectively. His work bridges the gap between complex computational resources and scientific research needs, making advanced computing tools accessible to researchers across disciplines.
Interdisciplinary Center for Data Science
Dr. Natasha Balac is a faculty member at the Interdisciplinary Center for Data Science at the University of California, San Diego. She holds a Ph.D. and has a strong focus on exploring critical issues surrounding inequality, institutions.
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4 course rating
3,322 ratings
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