This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data. You'll feel empowered to have conversations about big data and the data analysis process.
Actions in Spark
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Compétences que vous apprendrez
Python Programming, Apache Hadoop, Mapreduce, Apache Spark
Avis
YN
May 23, 2016
Easy into into big data architecture with minimal previous development requirements. Relevant to anyone who wants to grasp basic concepts of how data flows/processed/analyzed in hadoop.
DA
Mar 12, 2017
A very nice course covering the basics of the Hadoop ecosystem and Apache spark. The lectures are high quality and the presenters do a very good work of explaining the concepts. Thanks
À partir de la leçon
Spark
Welcome to module 5, Introduction to Spark, this week we will focus on the Apache Spark cluster computing framework, an important contender of Hadoop MapReduce in the Big Data Arena. Spark provides great performance advantages over Hadoop MapReduce,especially for iterative algorithms, thanks to in-memory caching. Also, gives Data Scientists an easier way to write their analysis pipeline in Python and Scala,even providing interactive shells to play live with data.
Enseigné par
Natasha Balac
Director, Predictive Analytics Center of Excellence (PACE)
Paul Rodriguez
Research Programmer
Andrea Zonca
HPC Applications Specialist
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