Key Features
- This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data
- Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning
- Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability
Book Description
Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing.
This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.
Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
What you will learn
- Sharpen your functional programming skills in Scala using REPL
- Apply standard and advanced machine learning techniques using Scala
- Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data
- Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data
- Understand the principles of supervised and unsupervised learning in machine learning
- Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet
- Construct reliable and robust data pipelines and manage data in a data-driven enterprise
- Implement scalable model monitoring and alerts with Scala
About the Author
Alexander Kozlov has worked with hundreds of clients consulting on Big Data and machine learning topics ranging from data ingest to building a complete machine learning data lifecycle. He was one of the primary architects behind the recently released E8 Security software platform, a Silicon Valley Big Data malware detection start-up where he is still an employee. He has also participated in various meetups and given conference presentations on the topics.
Previously, Alex was a Principal Solutions Architect at Cloudera Inc. Prior to Cloudera and after graduation, Alex worked on data mining and statistical analysis at Turn, Inc., an online advertising company, HP Labs, and SGI, Inc. He has two PhDs, one of which is from Stanford University in Applied Physics and the other is from Moscow State University in Physics and Mathematics.
