{"product_id":"data-analysis-with-python-and-pyspark-9781617297205","title":"Data Analysis with Python and Pyspark","description":"\u003cp\u003e • Author(s): Jonathan Rioux\u003cbr\u003e • Publisher: Manning Publications\u003cbr\u003e • Publisher Imprint: Manning Publications\u003cbr\u003e • BISAC: Data Science - Data Analytics\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThink big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eData Analysis with Python and PySpark\u003c\/i\u003e you will learn how to: \u003cp\u003e\u003c\/p\u003e Manage your data as it scales across multiple machines\u003cbr\u003e Scale up your data programs with full confidence\u003cbr\u003e Read and write data to and from a variety of sources and formats\u003cbr\u003e Deal with messy data with PySpark's data manipulation functionality\u003cbr\u003e Discover new data sets and perform exploratory data analysis\u003cbr\u003e Build automated data pipelines that transform, summarize, and get insights from data\u003cbr\u003e Troubleshoot common PySpark errors\u003cbr\u003e Creating reliable long-running jobs \u003cp\u003e\u003c\/p\u003e \u003ci\u003eData Analysis with Python and PySpark\u003c\/i\u003e is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you've learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e About the technology\u003cbr\u003e The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark's core engine with a Python-based API. It helps simplify Spark's steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. \u003cp\u003e\u003c\/p\u003e About the book\u003cbr\u003e \u003ci\u003eData Analysis with Python and PySpark\u003c\/i\u003e helps you solve the daily challenges of data science with PySpark. You'll learn how to scale your processing capabilities across multiple machines while ingesting data from any source--whether that's Hadoop clusters, cloud data storage, or local data files. Once you've covered the fundamentals, you'll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. \u003cp\u003e\u003c\/p\u003e What's inside \u003cp\u003e\u003c\/p\u003e Organizing your PySpark code\u003cbr\u003e Managing your data, no matter the size\u003cbr\u003e Scale up your data programs with full confidence\u003cbr\u003e Troubleshooting common data pipeline problems\u003cbr\u003e Creating reliable long-running jobs \u003cp\u003e\u003c\/p\u003e About the reader\u003cbr\u003e Written for data scientists and data engineers comfortable with Python. \u003cp\u003e\u003c\/p\u003e About the author\u003cbr\u003e As a ML director for a data-driven software company, \u003cb\u003eJonathan Rioux\u003c\/b\u003e uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. \u003cp\u003e\u003c\/p\u003e Table of Contents \u003cp\u003e\u003c\/p\u003e 1 Introduction\u003cbr\u003e PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK\u003cbr\u003e 2 Your first data program in PySpark\u003cbr\u003e 3 Submitting and scaling your first PySpark program\u003cbr\u003e 4 Analyzing tabular data with pyspark.sql\u003cbr\u003e 5 Data frame gymnastics: Joining and grouping\u003cbr\u003e PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE\u003cbr\u003e 6 Multidimensional data frames: Using PySpark with JSON data\u003cbr\u003e 7 Bilingual PySpark: Blending Python and SQL code\u003cbr\u003e 8 Extending PySpark with Python: RDD and UDFs\u003cbr\u003e 9 Big data is just a lot of small data: Using pandas UDFs\u003cbr\u003e 10 Your data under a different lens: Window functions\u003cbr\u003e 11 Faster PySpark: Understanding Spark's query planning\u003cbr\u003e PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK\u003cbr\u003e 12 Setting the stage: Preparing features for machine learning\u003cbr\u003e 13 Robust machine learning with ML Pipelines\u003cbr\u003e 14 Building custom ML transformers and estimators","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":45031667728535,"sku":"9781617297205","price":5836.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781617297205.webp?v=1769207062","url":"https:\/\/atlanticbooks.com\/products\/data-analysis-with-python-and-pyspark-9781617297205","provider":"Atlantic Books","version":"1.0","type":"link"}