Big Data Analytics with Scala and Apache Spark
Master distributed data processing using Scala and Apache Spark. Learn functional programming for big data analysis, optimization techniques.
Master distributed data processing using Scala and Apache Spark. Learn functional programming for big data analysis, optimization techniques.
This comprehensive course teaches distributed big data processing using Scala and Apache Spark. Students learn to manipulate large-scale data using functional programming concepts, focusing on Spark's programming model and distributed collections framework. The curriculum covers essential topics including RDDs, transformations, actions, and performance optimization. Through hands-on programming assignments, participants master data loading, manipulation, and analysis while understanding crucial concepts like shuffling, partitioning, and data locality.
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What you'll learn
Read and load data into Apache Spark from persistent storage
Manipulate large datasets using Spark and Scala
Express data analysis algorithms in functional style
Optimize performance by avoiding shuffles and recomputation
Work with Spark SQL and DataFrames
Implement distributed data processing solutions
Skills you'll gain
This course includes:
350 Minutes PreRecorded video
7 programming assignments
Access on Mobile, Tablet, Desktop
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, emphasizing distributed data processing. Students learn to manipulate large datasets using functional programming concepts and Spark's distributed collections framework. The curriculum covers essential topics like RDDs, transformations, actions, and optimization techniques. Special attention is given to performance considerations in distributed systems, including data locality and shuffle operations. The course also explores structured data processing using Spark SQL, DataFrames, and Datasets.
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 · 1 Hours to complete
Structured data: SQL, Dataframes, and Datasets
Module 4 · 8 Hours to complete
Fee Structure
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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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