Data analysis with Python and PySpark / Jonathan Rioux.

Por:

Rioux, Jonathan [autor]

Tipo de material: Texto

texto

Tipo de medio:

sin medio

Tipo de soporte:

volumen

ISBN:

9781617297205

Tema(s):

Clasificación CDD:

006.312 23

Clasificación LoC:

QA 76 .9 .D343 R56 2022

Contenidos:

PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK -- 2 Your first data program in PySpark -- 3 Submitting and scaling your first PySpark program -- 4 Analyzing tabular data with pyspark.sql -- 5 Data frame gymnastics: Joining and grouping PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE -- 6 Multidimensional data frames: Using PySpark with JSON data -- 7 Bilingual PySpark: Blending Python and SQL code -- 8 Extending PySpark with Python: RDD and UDFs -- 9 Big data is just a lot of small data: Using pandas UDFs -- 10 Your data under a different lens: Window functions -- 11 Faster PySpark: Understanding Spark’s query planning -- PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK -- 12 Setting the stage: Preparing features for machine learning -- 13 Robust machine learning with ML Pipelines -- 14 Building custom ML transformers and estimators --

Resumen: In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that transform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data 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. About the technology 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. About the book Data 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. What's inside Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs

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Existencias
Tipo de ítem	Biblioteca actual	Biblioteca de origen	Colección	Signatura topográfica	Copia número	Estado	Notas	Fecha de vencimiento	Código de barras	Reserva de ítems
Libros para consulta en sala	Biblioteca Antonio Enriquez Savignac	Biblioteca Antonio Enriquez Savignac	COLECCIÓN RESERVA	QA 76 .9 .D343 R56 2022 (Navegar estantería(Abre debajo))	Ejem.1	No para préstamo (Préstamo interno)	Ingeniería Logística		043030

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Previo	QA76 .9 .D343 L54 2015 Spatial data mining : theory and application /	QA 76.9.D343 L58 2011 Web data mining : exploring hyperlinks, contents, and usage data /	QA 76 .9 .D343 M46 2014 Big data : a revolution that will transform how we live, work, and think /	QA 76 .9 .D343 R56 2022 Data analysis with Python and PySpark /	QA 76 .9 .D343 R87 2018 Mining the social web : data minig facebook, twitter, linkedin, instagram, github, and more /	QA 76 .9 .D343 W82 2017 Data mining : practical machine learning tools and techniques /	QA76.9.D35 C1368 2006 Estructuras de datos /	Siguiente

Incluye referencias bibliográfica e índice.

PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK --
2 Your first data program in PySpark --
3 Submitting and scaling your first PySpark program --
4 Analyzing tabular data with pyspark.sql --
5 Data frame gymnastics: Joining and grouping
PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE --
6 Multidimensional data frames: Using PySpark with JSON data --
7 Bilingual PySpark: Blending Python and SQL code --
8 Extending PySpark with Python: RDD and UDFs --
9 Big data is just a lot of small data: Using pandas UDFs --
10 Your data under a different lens: Window functions --
11 Faster PySpark: Understanding Spark’s query planning --
PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK --
12 Setting the stage: Preparing features for machine learning --
13 Robust machine learning with ML Pipelines --
14 Building custom ML transformers and estimators --

In Data Analysis with Python and PySpark you will learn how to:

Manage your data as it scales across multiple machines
Scale up your data programs with full confidence
Read and write data to and from a variety of sources and formats
Deal with messy data with PySpark’s data manipulation functionality
Discover new data sets and perform exploratory data analysis
Build automated data pipelines that transform, summarize, and get insights from data
Troubleshoot common PySpark errors
Creating reliable long-running jobs

Data 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.

About the technology
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.

About the book
Data 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.

What's inside

Organizing your PySpark code
Managing your data, no matter the size
Scale up your data programs with full confidence
Troubleshooting common data pipeline problems
Creating reliable long-running jobs

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Data analysis with Python and PySpark / Jonathan Rioux.

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