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)
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Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Data journey

Let's look at the journey of data from its creation to its usage:

  • Raw data creation: The observation, event, action, and manual entry are the key elements that contribute to data creation. This data is typically persisted as a raw data source for future usage. The persistent storage could be flat files, a database, a Kafka topic, AWS Kinesis Data Streams (KDS), or any other suitable storage.
  • Raw data extraction: Raw data extraction is the act of receiving or fetching raw data from a source. In an enterprise, raw data sources are internal as well as external. Some examples of commonly used external sources are currency exchange rates, stock prices, and weather data. A company's transactional data is an example of internal data.
  • Raw data ingestion: Raw data ingestion refers to the act of storing raw data in an organized form to support orderly data extraction...