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Essential PySpark for Scalable Data Analytics /
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key Features Discover how to convert huge amounts of raw data into meaningful and actionable insights Use Spark's unified analytics engine for end-to-end analytics, from...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing,
2021.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key Features Discover how to convert huge amounts of raw data into meaningful and actionable insights Use Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analytics Perform data ingestion, cleansing, and integration for ML, data analytics, and data visualization Book Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learn Understand the role of distributed computing in the world of big data Gain an appreciation for Apache Spark as the de facto go-to for big data processing Scale out your data analytics process using Apache Spark Build data pipelines using data lakes, and perform data visualization with PySpark and Spark SQL Leverage the cloud to build truly scalable and real-time data analytics applications Explore the applications of data science and scalable machine learning with PySpark Integrate your clean and curated data with BI and SQL analysis tools Who this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts wh ... | ||
| 542 | |f Copyright © 2021 Packt Publishing |g 2021 | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | 0 | |a Online resource; Title from title page (viewed October 29, 2021). | |
| 505 | 0 | |a Table of Contents Distributed Computing Primer Data Ingestion Data Cleansing and Integration Real-time Data Analytics Scalable Machine Learning with PySpark Feature Engineering – Extraction, Transformation, and Selection Supervised Machine Learning Unsupervised Machine Learning Machine Learning Life Cycle Management Scaling Out Single-Node Machine Learning Using PySpark Data Visualization with PySpark Spark SQL Primer Integrating External Tools with Spark SQL The Data Lakehouse. | |
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