Name: Unlimited
Brand: Storytel
SKU: 7777
Price: 13800 KRW
Availability: InStock

Tomasz Drabas

Denny Lee

Combine the power of Apache Spark and Python to build effective big data applications

Key FeaturesPerform effective data processing, machine learning, and analytics using PySparkOvercome challenges in developing and deploying Spark solutions using PythonExplore recipes for efficiently combining Python and Apache Spark to process dataBook Description

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.

You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.

By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.

What you will learnConfigure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environmentsCreate DataFrames from JSON and a dictionary using pyspark.sqlExplore regression and clustering models available in the ML moduleUse DataFrames to transform data used for modelingConnect to PubNub and perform aggregations on streamsWho this book is for

The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.

Denny Lee is a technology evangelist at Databricks. He is a hands-on data science engineer with 15+ years of experience. His key focuses are solving complex large-scale data problems—providing not only architectural direction but hands-on implementation of such systems. He has extensive experience of building greenfield teams as well as being a turnaround/change catalyst. Prior to joining Databricks, he was a senior director of data science engineering at Concur and was part of the incubation team that built Hadoop on Windows and Azure (currently known as HDInsight). Tomasz Drabas is a data scientist specializing in data mining, deep learning, machine learning, choice modeling, natural language processing, and operations research. He is the author of Learning PySpark and Practical Data Analysis Cookbook. He has a PhD from University of New South Wales, School of Aviation. His research areas are machine learning and choice modeling for airline revenue management.

Packt Publishing

PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

스토리텔을 처음 사용하시나요?

다음으로 로그인하세요...

다음으로 계정을 만드세요...

계정 만들기

Enter a Code

Storytel

스토리텔 Unlimited로 매월 무제한 청취와 독서가 가능하며, 방대한 오디오북과 이북 카탈로그에 접근할 수 있습니다.