This guide appeals to big data engineers, analysts, architects, software engineers, even technical managers who need to perform efficient data processing on Hadoop at real time. Basic familiarity with Java or Scala will be helpful.The assumption is that readers will be from a mixed background, but would be typically people with background in engineering/data science with no prior Spark experience and want to understand how Spark can help them on their analytics journey.

If you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required.

If you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required.

This book assumes knowledge of application development with Java and familiarity with distributed systems. Familiarity with other real-time streaming frameworks is not required, but some practical experience with other big data processing utilities might be helpful.

If you are a NoSQL developer and new to Apache Cassandra who wants to learn its common as well as not-so-common features, this book is for you. Alternatively, a developer wanting to enter the world of NoSQL will find this book useful.It does not assume any prior experience in coding or any framework.

If you have some experience of developing applications in one or more languages supported by Apache Thrift (C++, Java, PHP, Python, Ruby, and others) and want to broaden your knowledge and skills in building cross-platform, scalable applications, then this book is for you.

Apache Spark is a flexible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases.It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis.Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.

If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.

Nick Pentreath 是Graphflow公司联合创始人。Graphflow是一家大数据和机器学习公司，专注于以用户为中心的推荐系统和客户服务智能化技术。Nick拥有金融市场、机器学习和软件开发背景，曾任职于高盛集团，之后去在线广告营销创业公司Cognitive Match Limited（伦敦）担任研究科学家，后又去非洲最大的社交网络Mxit领导数据科学与分析团队。Nick是Apache Spark项目管理委员会成员之一。

If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.