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In Mastering Large Datasets with Python, author J.T. 2 ¦ Accelerating large dataset work: Map and parallel computing, 3 ¦ Function pipelines for mapping complex transformations, 4 ¦ Processing large datasets with lazy workflows, 6 ¦ Speeding up map and reduce with advanced parallelization, 7 ¦ Processing truly big datasets with Hadoop and Spark, 8 ¦ Best practices for large data with Apache Streaming and mrjob, 9 ¦ PageRank with map and reduce in PySpark, 10 ¦ Faster decision-making with machine learning and PySpark, 11 ¦ Large datasets in the cloud with Amazon Web Services and S3, 12 ¦ MapReduce in the cloud with Amazon’s Elastic MapReduce, Learning Spark: Lightning-Fast Big Data Analysis, Big Data: Principles and best practices of scalable realtime data systems, IPython Interactive Computing and Visualization Cookbook, IPython Interactive Computing and Visualization Cookbook, 2nd Edition, An introduction to the map and reduce paradigm, Parallelization with the multiprocessing module and pathos framework, Hadoop and Spark for distributed computing, Running AWS jobs to process large datasets, Title: Mastering Large Datasets with Python. 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Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code Modern data science solutions need to be clean, easy to read, and scalable. TEST is that true. The discussion, overview, and rankings are submitted by the developers that have used the course. You’ll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code by John T. Wolohan (Early release, v.6) Requirements:.ePUB, .PDF reader, 5 mb, 8 mb Overview: Mastering Large Datasets with Python teaches you to write easily readable, easily scalable Python code that can efficiently process large … This tutorial can be found on manning.com. 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