{"product_id":"data-analysis-with-python-and-pyspark","title":"Data Analysis with Python and PySpark","description":"Think 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.\n\nIn Data Analysis with Python and PySpark you will learn how to:\n\nManage your data as it scales across multiple machines\nScale up your data programs with full confidence\nRead and write data to and from a variety of sources and formats\nDeal with messy data with PySpark’s data manipulation functionality\nDiscover new data sets and perform exploratory data analysis\nBuild automated data pipelines that transform, summarize, and get insights from data\nTroubleshoot common PySpark errors\nCreating reliable long-running jobs\n\nData Analysis with Python and PySpark 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.\n\nPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\n\nAbout the technology\nThe 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.\n\nAbout the book\nData Analysis with Python and PySpark 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.\n\nWhat's inside\n\nOrganizing your PySpark code\nManaging your data, no matter the size\nScale up your data programs with full confidence\nTroubleshooting common data pipeline problems\nCreating reliable long-running jobs\n\nAbout the reader\nWritten for data scientists and data engineers comfortable with Python.\n\nAbout the author\nAs a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts.\n\nTable of Contents\n\n1 Introduction\nPART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK\n2 Your first data program in PySpark\n3 Submitting and scaling your first PySpark program\n4 Analyzing tabular data with pyspark.sql\n5 Data frame gymnastics: Joining and grouping\nPART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE\n6 Multidimensional data frames: Using PySpark with JSON data\n7 Bilingual PySpark: Blending Python and SQL code\n8 Extending PySpark with Python: RDD and UDFs\n9 Big data is just a lot of small data: Using pandas UDFs\n10 Your data under a different lens: Window functions\n11 Faster PySpark: Understanding Spark’s query planning\nPART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK\n12 Setting the stage: Preparing features for machine learning\n13 Robust machine learning with ML Pipelines\n14 Building custom ML transformers and estimators\u003cbr\u003eASIN: 1617297208\u003cbr\u003eVSKU: GBV.1617297208.A\u003cbr\u003eCondition: Acceptable\u003cbr\u003eAuthor\/Artist:Rioux, Jonathan\u003cbr\u003eBinding: Paperback\u003cbr\u003e\u003cb\u003eNote:\u003c\/b\u003e Any images shown are stock photographs and product may differ from what is shown.  \u003cbr\u003e\u003cb\u003eCondition Notes\u003c\/b\u003e: This copy has liquid damage.  \u003cbr\u003e","brand":"Good Books Company","offers":[{"title":"Default Title","offer_id":52968625865009,"sku":"GBV.1617297208.A","price":39.07,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0914\/3730\/2065\/files\/1617297208-0.jpg?v=1772231899","url":"https:\/\/goodbookscompany.org\/products\/data-analysis-with-python-and-pyspark","provider":"Good Books Company","version":"1.0","type":"link"}