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Key benefits

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

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.

Who is this book for?

This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

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Frequently bought together

Essential PySpark for Scalable Data Analytics

$48.99

Machine Learning Engineering with Python

$54.99

$43.99

Total $ 147.97

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Amazon verified reviews

Prathamesh Verlekar Feb 07, 2022

As a Cloud Data Engineer, I work on big data processing using Dataframes and Spark SQL. This book helped me understand things that need to be taken care of while processing and transforming the data. This book is helpful for professionals who have started their careers in the Data Engineering field. There is a separate section focused on Data Engineering using Spark, and I find it very useful. Along with this, the Data Science section covers topics like Data Wrangling, Feature Engineering, Supervised and Unsupervised Machine Learning, and these topics are necessary for every data engineer today. Altogether, this book is a complete package for anyone new to the data engineering field, and I highly recommend this book to anyone interested in Data Engineering.

Amazon Verified review

Vinoth Oct 29, 2021

Apache Spark is a powerful platform for Big Data applications that explores a lot of advanced technique. The book describes clearly and systematically the Spark architecture and has a lot of outstanding examples that helps the reader to become familiar with the brilliant Spark programming models. Good for someone trying to learn pyspark (RDD, SparkSQL) ,sparkML and Data Visualization using Pyspark. The presentation of the material is excellent and the explanations are supportive and help the understanding.It is very detailed with lots of code samples and all the code is on GitHub. Databricks Spark Clusters is used for executing the code provided in book but same code can be used on any Spark cluster running Spark 3.0, or higher. The book is well organized into different components of Spark, e.g. intro, structured api, streaming, optimizations, data lake, ml life cycle using MLflow and deployment options. ML section is thorough and covers Feature Engineering, Supervised , unsupervised Machine learning and ML Life cycle management. There is also coverage on how to connect to different applications like tableau, thrift.Overall, the book contains solid information on the inner workings of Spark. I would recommend giving this book a read !!

Anirudh Oct 29, 2021

I’ve recently been ramping up on the PySpark. As this was really helpful for me to understand some of the real time topics & knowledge on one of the topic "Building analytical data stores using clouddata lakes". Understanding the challenges on working on Integrating data using data virtualization techniques.I Particularly found the topic Real-time analytics systems architecture and the use cases very useful & the author explains how the traditional of BI moving to AI, advantages Modern advanced analytics and data science tools.

Felix Nov 02, 2021

As a Data Scientist, I find this book to be a practical introduction to the basic applications of Pyspark. I have used Pyspark at work for a few years now, but in limited capacities. This book touches on concepts spanning ingestion to analytics and provides some code snippets as support material. You will find this book especially useful if you work in a Databricks environment, as the provided examples live there (no worries if you don't -- there is a community edition you can use for free that the book points to). For me, this is a great reference when I am doing crossfunctional work with analysts and engineers. Additionally, I enjoyed the machine learning section because I have used Pyspark's ML libraries rarely and they are always evolving. All in all, a good reference book for a professional new to newish to Pyspark.

Emily Oct 29, 2021

I started this book at the same time I started my new role as a Data Scientist since I needed to learn PySpark for my job. I found it to be incredibly helpful since I work with billions of data points. The hands-on exercises were the most helpful part of this book since that is the best way to learn programming at any capacity. Highly recommend! The author is highly skilled and I would be thrilled to read any future work they publish.

Essential PySpark for Scalable Data Analytics: A beginner's guide to harnessing the power and ease of PySpark 3

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