Python and R are two popular languages for data scientists due to the large number of modules or packages that are readily available to help them solve their data analytics problems. However, traditional uses of these tools are often limiting, as they process data on either a single machine or with main memory-based approaches where the movement of data becomes time-consuming, the analysis requires sampling, and moving from development to production environments requires extensive re-engineering. To address these issues, Spark provides data engineers and data scientists a powerful and unified engine that is both faster and easy to use. This allows you to solve their machine learning problems interactively and at much greater scale.

Therefore, if you are an academic, researcher, data science engineer, or even a big data engineer working with large and complex data sets. Furthermore, if you want to board your data processing pipelines and machine learning applications to scale up more quickly, this book would be a suitable companion to this journey. Moreover, Spark provides many language choices, including Scala, Java, and Python. This facility will definitely help you to lift your machine learning applications on top of Spark and reshape using any one of these programming languages with Spark.

You should be familiar with the basics of machine learning concepts at least. Knowledge of open source tools and frameworks such as Apache Spark and Hadoop-based MapReduce would be good, but is not essential. A solid background in statistics and computational mathematics is expected. In addition, knowledge of Scala, Python, and Java is advisable. However, if you are experienced with intermediate programming languages, this will help you to understand the discussions and examples demonstrated in this book.