Big Data Analysis with Scala and Spark
Master distributed data processing techniques and implementations using Scala and Apache Spark for comprehensive large-scale data analysis and transformation.
Master distributed data processing techniques and implementations using Scala and Apache Spark for comprehensive large-scale data analysis and transformation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Functional Programming in Scala Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.6
(2,586 ratings)
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English
پښتو, বাংলা, اردو, 4 more
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What you'll learn
Load and manipulate large-scale data with Apache Spark
Optimize distributed computations for performance
Implement data analysis algorithms using functional programming
Work with structured data using Spark SQL and DataFrames
Handle data partitioning and avoid unnecessary shuffling
Skills you'll gain
This course includes:
5.83 Hours PreRecorded video
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FullTime access
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There are 4 modules in this course
This comprehensive course focuses on big data analysis using Scala and Apache Spark. Students learn to manipulate distributed datasets using functional programming concepts, optimize performance through data partitioning and shuffling, and work with structured data using Spark SQL. The curriculum covers essential topics including RDDs, transformations and actions, cluster topology, and advanced features like DataFrames and Datasets, providing hands-on experience with real-world data analysis.
Getting Started + Spark Basics
Module 1 · 11 Hours to complete
Reduction Operations & Distributed Key-Value Pairs
Module 2 · 6 Hours to complete
Partitioning and Shuffling
Module 3 · 56 Minutes to complete
Structured data: SQL, Dataframes, and Datasets
Module 4 · 8 Hours to complete
Fee Structure
Instructor
Assistant Professor
Heather Miller is an assistant professor in the School of Computer Science at Carnegie Mellon University, where she focuses on data-centric distributed systems and programming languages. Before her current role, she was a research scientist at the École Polytechnique Fédérale de Lausanne (EPFL) and co-founded the Scala Center, which promotes the use of the Scala programming language. Miller has a PhD from EPFL, where she contributed significantly to Scala's development, and she is known for her work on MOOCs that have engaged over a million students. Her research aims to bridge theoretical advancements in programming languages with practical industrial applications.
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4.6 course rating
2,586 ratings
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