Mastering Large Datasets With Python
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Mastering Large Datasets with Python
Summary Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You’ll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Programming techniques that work well on laptop-sized data can slow to a crawl—or fail altogether—when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change. About the book Mastering Large Datasets with Python teaches you to write code that can handle datasets of any size. 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. You’ll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, you’ll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3. What's inside 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 About the reader For Python programmers who need to work faster with more data. About the author J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington. Table of Contents: PART 1 1 ¦ Introduction 2 ¦ Accelerating large dataset work: Map and parallel computing 3 ¦ Function pipelines for mapping complex transformations 4 ¦ Processing large datasets with lazy workflows 5 ¦ Accumulation operations with reduce 6 ¦ Speeding up map and reduce with advanced parallelization PART 2 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 PART 3 11 ¦ Large datasets in the cloud with Amazon Web Services and S3 12 ¦ MapReduce in the cloud with Amazon’s Elastic MapReduce
Mastering Large Datasets
Author: J. T. Wolohan
language: en
Publisher: Manning Publications
Release Date: 2020-01-06
With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Mastering Python for Data Engineering
Author: Thompson Carter
language: en
Publisher: Independently Published
Release Date: 2025-01-09
Mastering Python for Data Engineering: Transform and Manipulate Big Data with Python Unlock the true potential of Python for big data manipulation and engineering with Mastering Python for Data Engineering. This comprehensive guide is designed to help data engineers and aspiring professionals transform, process, and analyze massive datasets efficiently. By leveraging Python's powerful libraries and tools, you'll be equipped to build scalable data pipelines, integrate various data sources, and optimize data workflows for performance. From basic data wrangling to advanced engineering techniques, this book provides a practical, hands-on approach to mastering data engineering tasks with Python, making it the perfect companion for anyone aiming to work with big data. What You'll Learn: The fundamentals of Python for data engineering, including essential libraries like pandas, NumPy, and Dask. Building efficient data pipelines for ETL (Extract, Transform, Load) processes. Working with large datasets using parallel and distributed processing tools like Apache Spark and Dask. Integrating data from various sources, such as databases, APIs, and streaming data. Data transformation and cleaning techniques to prepare data for analysis. Optimizing performance and scaling data workflows with Python. With step-by-step guidance and practical examples, Mastering Python for Data Engineering will show you how to handle data at scale, integrate different data sources, and build automated data workflows that are crucial for modern data infrastructure. Dive into the world of data engineering with Python and learn how to transform raw data into actionable insights while building systems that can handle vast amounts of information.