Book Image

Hands-On Data Analysis with Scala

By : Rajesh Gupta
Book Image

Hands-On Data Analysis with Scala

By: Rajesh Gupta

Overview of this book

Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights
Table of Contents (14 chapters)
Free Chapter
Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Spark Streaming overview

Spark Streaming is an extension of the core Spark API that enables scalable and fault-tolerant, stream-oriented processing of data. Spark provides the ability to stream data from multiple sources, with a number of key sources being the following:

  • Apache Kafka
  • Amazon Kinesis and S3
  • TCP
  • HDFS

Spark offers two flavors of streaming:

  • Spark Structured Streaming that is built on top of the Spark SQL engine
  • Spark Discretized Stream (DStream), which uses a discretized stream—that is, a continuous stream of data

In this section, we will be exploring Spark DStreams and develop an understanding of how this could be leveraged to develop streaming solutions.

Let's start with a classic word count problem, where we are trying to count the frequency of each distinct word.