You receive an e-mail. It contains an offer for a complete personal computer system. It seems like the retailer read your mind since you were exploring computers on their web site just a few hours prior...As you drive to the store to buy the computer bundle, you get an offer for a discounted coffee from the coffee shop you are getting ready to drive past. It says that since you...(展开全部)

This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows.It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.

Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory.

The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

克里斯托弗•苏达克  宾夕法尼亚州立大学机械工程学学士，美国塔夫脱大学法学学士。宾夕法尼亚大学沃顿商学院高级技术管理学硕士，美国维拉诺瓦大学信息安全学硕士。  商业数据系统与业务模式的设计者和创新者。大学毕业后，进入世界最大的国防工业承包商洛克希德•马丁空间系统公司总部，担任空间系统工程师和火箭专家。离开该公司后，先后于BDM 信息技术公司和玛氏糖果公司担任高级战略顾问，后又在埃森哲、西门子、戴尔和花旗银行等领先企业担任高级战略顾问和技术架构师。他的服务对象包括美国商务部、世界银行、国际货币基金组织、摩根士丹利、百事可乐、三星、沃尔玛等。  2014年getAbstract国际图书奖获得者，《欧洲商业评论》杂志撰稿人。

克里斯托弗•苏达克  宾夕法尼亚州立大学机械工程学学士，美国塔夫脱大学法学学士。宾夕法尼亚大学沃顿商学院高级技术管理学硕士，美国维拉诺瓦大学信息安全学硕士。  商业数据系统与业务模式的设计者和创新者。大学毕业后，进入世界最大的国防工业承包商洛克希德•马丁空间系统公司总部，担任空间系统工程师和火箭专家。离开该公司后，先后于BDM 信息技术公司和玛氏糖果公司担任高级战略顾问，后又在埃森哲、西门子、戴尔和花旗银行等领先企业担任高级战略顾问和技术架构师。他的服务对象包括美国商务部、世界银行、国际货币基金组织、摩根士丹利、百事可乐、三星、沃尔玛等。  2014年getAbstract国际图书奖获得者，《欧洲商业评论》杂志撰稿人。

This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies.

This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies.

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.