The continued growth in data coupled with the need to make increasingly complex decisions against that data is creating massive hurdles that prevent organizations from deriving insights in a timely manner using traditional analytical approaches. The field of big data has become so related to these frameworks that its scope is defined by what these frameworks can handle. Whether you're scrutinizing the clickstream from millions of visitors to optimize online ad placements, or sifting through billions of transactions to identify signs of fraud, the need for advanced analytics, such as machine learning and graph processing, to automatically glean insights from enormous volumes of data is more evident than ever.

Apache Spark, the de facto standard for big data processing, analytics, and data sciences across all academia and industries, provides both machine learning and graph processing libraries, allowing companies to tackle complex problems easily with the power of highly scalable and clustered computers. Spark's promise is to take this a little further to make writing distributed programs using Scala feel like writing regular programs for Spark. Spark will be great in giving ETL pipelines huge boosts in performance and easing some of the pain that feeds the MapReduce programmer's daily chant of despair to the Hadoop gods.

In this book, we used Spark and Scala for the endeavor to bring state-of-the-art advanced data analytics with machine learning, graph processing, streaming, and SQL to Spark, with their contributions to MLlib, ML, SQL, GraphX, and other libraries.

We started with Scala and then moved to the Spark part, and finally, covered some advanced topics for big data analytics with Spark and Scala. In the appendix, we will see how to extend your Scala knowledge for SparkR, PySpark, Apache Zeppelin, and in-memory Alluxio. This book isn't meant to be read from cover to cover. Skip to a chapter that looks like something you're trying to accomplish or that simply ignites your interest.

Happy reading!